Faculty of Engineering, Architecture and Information Technology

THE UNIVERSITY OF QUEENSLAND

Ingredients impact on biscuit dimension variation

Student Name: Jenny Gustavsson

Course Code: ENGG7290

Supervisor: Sophia Rodrigues

Submission date: 19 December 2018

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Executive summary

This is the final report for the project “Ingredients impact on biscuit dough variation” conducted at

Arnott’s Biscuits in Virginia, Australia. This industry placement position is arranged by the University

of Queensland through the course ENGG7290 as a part of a Bachelor of Engineering and Master of

Engineering degree. The quality and quantity of ingredients have an impact on the quality and structure

of the baked biscuit. Furthermore, ingredients quality and quantity variation are suspected to be the

reason to biscuit dimension variation, which causes trouble when the biscuits go through packaging.

The variation is due to less or more spread of the biscuit, more spread results to a longer biscuit width

and a shorter biscuit height. When the biscuits enter the packaging step, a number of biscuits get stacked

together and are expected to reach a specific packet length, a lower biscuit height will result in a too

short packet length.

One of the production lines at Arnott’s produce two biscuits which have significant problems with

biscuit dimension variation and are therefore the reason for a high waste percentage on the production

line. An increase of the performance on the line is of great interest, due to the economic and the

environmental benefit of a lower waste percentage. The purpose of this project has been to evaluate a

couple of ingredients that are suspected to cause the dimension variation of the two biscuits.

Furthermore, to investigate the possibility to reduce its impact on the variation and thereby reduce the

waste on the production line. The ingredients which have been investigated are icing sugar, ammonium

bicarbonate and flour.

This report is the final report and it outlines an extended version of the Project Progress report. This

report present project findings and conclusions that have been found, together with recommendations

and results from implemented project recommendations.

A couple of major findings have been made in this project:

• The concentration of the liquid Vol is not controlled after it is dissolved and transferred to a

storage tank. The concentration decreases due to storage conditions. The concentration

decreases and varies even more from the storage tank to the manually tapped dough buckets.

• The variation of the Vol concentration does not contribute to the biscuit dimension variation of

biscuit A and biscuit B, based on the calculated correlation. The variation may have a greater

impact on other biscuits in the factory with liquid Vol as the only rising agent.

• Due to a shared ingredient handling system in the factory, different quantities of graded sugar

follow the icing sugar to the mixer dependent on the frequency of graded sugar calls. The graded

sugar increases the mean sugar particle size.

• The variation of the mean sugar particle size due to the sugar system has an impact on the dough

spread of biscuit A.

• The temperature of the flour has an impact on the dough temperature, based on the measured

moderate correlation. On the other hand, the dough temperature does not contribute to biscuit

dimension variation of biscuit A and biscuit B, based on the calculated correlation.

• Calculations show on a low correlation between dough weight variation and the biscuit

dimension variation, it seems to have a greater impact when the dough weight deviates

significantly from the expected weight. There is not an obvious trend between the dough weight

and which flour delivery system that is used.

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Recommendations have been made based on the investigation of the ingredient systems. The Vol

concentration variation can be reduced by a better-controlled make-up procedure and redesign of the

make-up and storage tanks. The biscuit dimension variation should be investigated for other biscuits

that only use liquid vol as the rising agent, to evaluate if the concentration variation may have a greater

impact on those biscuits. Conclusions could thereafter be taken whether the acceptable concentration

range is correct or not. Furthermore, recommendations are given to evaluate and maybe change the

titration procedure for the Vol, a stronger acid will reduce the error caused by sample dilution.

Furthermore, a recommended redesign of the sugar control sequence was given in the project progress

report to stop graded sugar from following the icing sugar to the mixer. The recommendation for the

sugar system has been implemented and the outcome from the process change has been greatly

successful with a less percentage of biscuits with dimensions outside the specification range of +/- 2

mm.

The flour used in the biscuits in the factory is of high quality, the quality parameters are rarely outside

specifications. Due to the dough weight variation that occurs, the recommendation is to investigate the

possibility and the expenses of installing a weight transmitter on the flour hopper above the mixer. The

transmitter would make it possible to detect weight difference caused by problems with the flour system

valves on the lines before it turns in to a problem.

Due to the long list of parameters that have an impact on the biscuit spread, some parameters have a

greater impact than others and a high correlation between a parameter and the biscuit spread will most

likely not exist due to the significant variation of all the other parameters. Further project opportunities

are to continue the investigation of the remaining parameters on the list given in section 2.2.2 Baking.

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Acknowledgement

The success and the outcome of this project required guidance and support from many people and I am

very privileged to have got this all along the placement, which made completion of my project possible.

The success of the project, as well as the professional development I have gained through this placement,

is only due to such supervision and assistance. I am extremely thankful to all the people at Arnott’s

biscuits in Virginia and everyone involved in the engineering placement semester course, ENGG7290.

I would like to give a special thanks to Mr Christopher Price, my workplace supervisor and Business

Improvement Manager at Arnott’s biscuit in Virginia, for making sure my placement experience has

been as good as possible through his support, guidance and feedback on project work and reports.

I would also like to thank Ms Hayley Erasmus, my project supervisor and Manufacturing Manager at

Arnott’s biscuit in Virginia, for the support, guidance and wisdom she has provided to make this project

successfully conducted.

I would also like to thank the Business Improvement team, for their support and feedback. A special

thanks to the team member Ms Wern Tan for her guidance, patience, wisdom and encouragements

through my placement.

I would also like to thank the lab coordinators, Mr Ben Collie and Ms Yue Wu, for providing me with

support, feedback and continuous encouragement through my placement.

I would also like to thank the course coordinator Ms Beverly Coulter of the Faculty of Engineering,

Architecture and Information Technology at the University of Queensland, for arranging this placement

that has given me priceless professional practise experience.

I would finally like to thank my UQ supervisor, Dr Sophia Rodrigues, for providing me with constant

support and valuable feedback and recommendations for improvements in this project. I am so grateful

to all the people from my two Universities, The University of Queensland and Lund University in

Sweden, that made this double master’s degree possible.

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Table of Contents

Executive summary ................................................................................................................................. ii Acknowledgement .................................................................................................................................. iv List of Symbols ...................................................................................................................................... ix 1 Introduction .......................................................................................................................................... 1

1.1 Context ........................................................................................................................................... 1

1.2 Purpose .......................................................................................................................................... 1

1.3 Scope.............................................................................................................................................. 1

2 Technical Background .......................................................................................................................... 2 2.1 Biscuit A and Biscuit B ................................................................................................................. 2

2.2 Overview of Biscuit making ..................................................................................................... 3

2.2.1 Mixing ..................................................................................................................................... 3

2.2.2 Baking ..................................................................................................................................... 3

2.3 Structure of short doughs biscuits ........................................................................................... 4

2.4 Ingredients of importance ........................................................................................................ 5

2.4.1 Shortening ............................................................................................................................... 5

2.4.2 Sugar ....................................................................................................................................... 6

2.4.3 Ammonium Bicarbonate (NH4)HCO3 ..................................................................................... 7

2.4.4 Flour ........................................................................................................................................ 9

2.4.5 Statistical Analysis ................................................................................................................ 11

3 Project Plan ........................................................................................................................................ 12 3.1 Methodology ................................................................................................................................ 12

3.1.1 Biscuit Dimension and Packet Length Data Retrieval .......................................................... 12

3.1.2 Concentration of liquid Vol .................................................................................................. 13

3.1.3 Icing sugar ............................................................................................................................. 14

3.1.4 Flour ...................................................................................................................................... 15

3.2 Resources ..................................................................................................................................... 15

3.3 Work timeline .............................................................................................................................. 16

3.3.1 Deviations from the interim project report ............................................................................ 16

3.4 Project Risks and Opportunities .................................................................................................. 18

3.4.1 Risk Assessment ................................................................................................................... 18

3.4.2 Project Opportunities ............................................................................................................ 19

4 Results and Discussion ....................................................................................................................... 20 4.1 Vol Concentration - Results and Discussion ............................................................................... 20

4.1.1 Variation in Vol concentration in storage tanks .................................................................... 20

4.1.2 Vol concentration difference between make-up tank and storage tank ................................. 20

4.1.3 Variation of Vol concentration in buckets ............................................................................ 21

4.1.4 Correlation between a storage tank and biscuit dimensions.................................................. 21

4.1.5 Correlation between the bucket concentration and dimensions ............................................ 22

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4.2 Icing sugar - Results and Discussion ........................................................................................... 23

4.2.1 Icing sugar particle size ......................................................................................................... 23

4.2.2 Frequency of Sugar calls ....................................................................................................... 26

4.3 Flour – Results and Discussion .................................................................................................... 28

4.3.1 Protein level .......................................................................................................................... 28

4.3.2 Flour temperature variation ................................................................................................... 29

4.3.3 Dough weight variation ......................................................................................................... 29

5 Conclusions and Recommendations ................................................................................................... 32 5.1 Conclusions .................................................................................................................................. 32

5.1.1 Liquid Vol ............................................................................................................................. 32

5.1.2 Icing Sugar ............................................................................................................................ 32

5.1.3 Flour ...................................................................................................................................... 33

5.2 Recommendations ........................................................................................................................ 33

5.2.1 Liquid Vol ............................................................................................................................. 34

5.2.2 Icing Sugar ........................................................................................................................ 35

5.2.3 Flour .................................................................................................................................. 35

6 Project outcomes ............................................................................................................................ 35 7 Professional Development .................................................................................................................. 38

7.1 Key learnings and challenges ...................................................................................................... 38

7.2 Development of EA Competencies .............................................................................................. 38

8 References .......................................................................................................................................... 40 Appendix A .............................................................................................................................................. i Appendix B ............................................................................................................................................. ii Appendix C ............................................................................................................................................ iv Appendix D ............................................................................................................................................. v

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List of Tables Table 1: Scope of the project ................................................................................................................... 2

Table 2: Reasons for variations in dough spread during baking (Davidson, 2016). ............................... 4

Table 3: Variables that may cause quality and quantity variation of ingredients. ................................... 5

Table 4: Risk level identification matrix, (The University of Iowa, n.d.) ............................................. 18

Table 5: Possible risks and hazards for the project, how to avoid the risk and the risk level based on

table 6. .................................................................................................................................... 18

Table 6: Hazard information for chemicals (Ammonium Bicarbonate, u.d.; Sulfuric acid solution,

2018). ..................................................................................................................................... 19

Table 7: Average deviation of biscuit dimensions and packet length for the time-period .................... 22

Table 8: Average concentration of storage tank and buckets. ............................................................... 22

Table 9: Linear correlation coefficient between biscuit dimension deviation and the Vol concentration

for both biscuit A and B. ........................................................................................................ 22

Table 10: Correlation between packet length, biscuit dimensions and the Vol concentration in the

buckets. .................................................................................................................................. 23

Table 11: Average particle size distribution in bags and the IHS system. ............................................ 24

Table 12: Average particle size distribution for samples collected the 19th of September, when the

previous call was graded, when 2nd previous was graded and when the two previous calls

been icing sugar. .................................................................................................................... 25

Table 13: Average particle size distribution for samples collected the 27th of September, when the

previous call was graded, when 2nd previous was graded and when the two previous calls

been icing sugar. .................................................................................................................... 25

Table 14: Average particle size distribution for samples collected from the accumulation hopper the

12th of September. The average value for samples: when the previous call was graded, when

2nd previous was graded and when the two previous calls been icing sugar. ......................... 25

Table 15: Summary of measured data. The average dimension deviation and frequency of sugar calls

for each sample date............................................................................................................... 27

Table 16: Linear correlation between biscuit dimensions, packet length against the ratio of icing to

graded (G:I) sugar calls and the number of doughs with a previous call of graded sugar. .... 27

Table 17: Calculated correlation coefficient between dough weight and biscuit dimensions. .............. 31

Table 18: Average biscuit dimension deviations before the accumulation hopper was getting locked

out. ......................................................................................................................................... 36

Table 19: Average biscuit dimension deviations after the accumulation hopper was locked out. ........ 36

Table 20: Percentage of all biscuits over a time-period of 25 doughs that has a length that deviates

more than 2 mm from specification. The percentage is given per production-row A-H for

both length and width, the total shows the overall biscuit dimension percentage outside

specification. .......................................................................................................................... 37

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List of Figures Figure 1: Ratio between ingredients of importance in biscuit A. ............................................................ 2

Figure 2: Ratio between ingredients of importance in biscuit B. ............................................................ 2

Figure 3: Dimensions deviation of acceptance for biscuit A (mm). ........................................................ 3

Figure 4: Dimension deviation of acceptance for biscuit B (mm). ......................................................... 3

Figure 5: Biscuit height. .......................................................................................................................... 3

Figure 6: Packet length deviation acceptance (mm). ............................................................................... 3

Figure 7: Flowchart of the four main process steps for biscuit making. ................................................. 3

Figure 8: a) illustrates the gluten network in a structured product, b) the two phases in short dough, the

fat phase and the sugar solution with gluten and starch, c) the sugar phase are responsible for

the structure due to embedding and bridging of the other components. (Chevallier, Colonna,

Bule´on, & Della Valle, 2000) .................................................................................................. 5

Figure 9: A simplified process diagram for the bulk sugar system. ........................................................ 7

Figure 10: Liquid Vol is volatile and can easily decompose to CO2, H2O and NH3 vapour, as well as

recrystallise to solids. (Sutter, 2017) ......................................................................................... 8

Figure 11: A simplified process diagram for the Vol system. ................................................................. 8

Figure 12: Photo to the left is taken before cleaning is done on make-up tank number 1. Photo to the

top right is taken on one of the storage tanks before cleaning. Photo on the bottom right is

taken after cleaning of make-up tank 2. Photo credits: Corey Smith. ....................................... 9

Figure 13: A simplified process diagram for the flour system. ............................................................. 11

Figure 14: The correlation coefficient shows on the strength and direction of correlation. (Correlation

Coefficient 'r' and The Linear Model, 2018) ........................................................................... 12

Figure 15: The measured concentration is from the storage tank which is selected for delivery, current

day and time. Under the red line, the concentration is too low, under 16.4% is lower than the

accepted range. The accepted range is between the green lines.............................................. 20

Figure 16: Measured concentration of storage tank (ST) against the corresponding make-up tank (MT).

................................................................................................................................................ 20

Figure 17: The measured concentration in buckets are plotted with current storage tank (ST). The

green line shows the low limit of accepted concentration range. Dough= buckets. ............... 21

Figure 18: Concentration in buckets and dimension deviation for 11 doughs. ..................................... 23

Figure 19: Icing sugar particle size distribution in samples taken the 19th of September. ................... 24

Figure 20: Icing sugar particle size distribution in samples taken the 19th of September. ................... 24

Figure 21: Particle size distribution in icing sugar bags. ....................................................................... 24

Figure 22: Icing sugar particle size distribution in samples taken the 27th of September. ................... 24

Figure 23: Icing sugar particle size distribution in samples taken from the accumulator the 12th of

September. .............................................................................................................................. 26

Figure 24: Icing sugar particle size distribution in samples taken from the accumulator the 13th of

September. .............................................................................................................................. 26

Figure 25: Scatter plot of biscuit width against the number of sugar calls that had graded sugar as

previous call. ........................................................................................................................... 27

Figure 26: Average packet length deviation against number of sugar calls with graded sugar as

previous call. ........................................................................................................................... 28

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Figure 28: Time series of the biscuit length deviation the 30th-31st of October. A batch with a lower

protein level seems to generate a biscuit length deviation around 7pm. ................................. 28

Figure 29: Dough temperature for biscuit A between the 28th of October to the 2nd of November

together with corresponding flour sifter temperature.............................................................. 29

Figure 30: Scatter plot of dough temperature versus the dimension variation of the biscuit width. ..... 29

Figure 31: Dough weight distribution Biscuit B. .................................................................................. 30

Figure 32: Dough weight distribution Biscuit A. .................................................................................. 30

Figure 34: Dough weight for Biscuit B in November 2018. ................................................................. 30

Figure 33: Dough weight for Biscuit A in November 2018. ................................................................. 30

Figure 35: Dough weight variation for biscuit A together with information on which flour system that

was used for delivery, red for flour system A and blue for flour system B. ........................... 31

Figure 36: A scatter plot of the packet length deviation against the dough weight. .............................. 32

Figure 38: The figures to the right shows the flow profile with baffles and the figures to the right

shows the flow profile without baffles. (Nilsson, 2015) ......................................................... 35

Figure 37: Optimal ratio between dimensions of the tank, stirrer, 4 baffles and the liquid level.

(Nilsson, 2015) ........................................................................................................................ 35

Figure 39: The number of EA competencies achieved for each reflective journal. .............................. 39

List of Symbols

Symbol Variable

PL Packet length

l Biscuit length

w Biscuit width

Vol Ammonium Bicarbonate

ST Liquid Vol storage tank

MT Liquid Vol make-up tank

G Graded sugar

I Icing sugar

G:I Ratio of icing to graded sugar

IHS Ingredient handling system

𝑠𝑥 Standard deviation

r Person’s correlation coefficient

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1 Introduction

1.1 Context

Campbell Arnott's Biscuits is one of the largest food companies in the Asia Pacific and it is a subsidiary

of the Campbell Soup Company in the United States. The company started in 1865 as a small bakery in

Newcastle, producing bread, biscuits and pies to the local community. Arnott’s biscuits have become a

national icon, and its products are delivered to more than 40 countries around the world (About Arnott's,

2018).

Biscuit making has been an art for the employees at the factory since they started back in the 19th century,

the workers knew their biscuits, they could see what changes the dough required if the dough or biscuits

did not appear as desired. Therefore, the technology and the science behind the processes have been a

few steps behind. Many changes have been done at the factory the last decade and the stakeholders have

understood the importance of science behind biscuit making. The company works hard to interpret

science in their processes, and there is still much room for improvements in the factory.

This project will focus on ingredients impact on Biscuit dimension variation, mainly on two of Arnott’s

iconic biscuits. These biscuits have doughs which are sensitive to the variation of the ingredients, which

results in a variation of the biscuits’ dimensions. The tolerance for biscuit dimension variation is limited

due to the packaging system used on the production line where these biscuits are packed. Biscuits that

get dimensions outside the accepted range are dumped, which results in a high waste-ratio on the

production line. It is therefore of both economic and environmental interest to reduce the waste on this

line. The two biscuits will be called biscuit A and biscuit B in this report. Some impacts that result in

biscuit dimension variation for the two biscuits are known and some are suspected. The ingredients that

are known to contribute to variation are shortening, icing sugar, flour and Vol. How much they impact

on the variation on the dough is unknown and are therefore to be investigated in this project.

1.2 Purpose

The main objective of this project is to determine the impact and to recommend what process changes

and quality measurements that can be implemented to reduce the variation caused by ingredients. This

will result in less waste and increased profit for the production line.

The first step of the project is to understand the dough make-up for biscuit A and biscuit B, and the

ingredients make-up and delivery process, and how quality analysis and process variables can be

measured to determine ingredient variation. Second step is to decide what methods are appropriate to

collect the necessary data which can be analysed to determine the impact of ingredient quality and

quantity variation on the dough and biscuit variation. Third step is to analyse the data, determine what

effect of ingredients’ variation is and make recommendations.

1.3 Scope

The scope of this project is summarised in Table 1 below. Even though studies of other biscuits are out

of scope, the correlations found between the ingredients and the doughs for biscuit A and biscuit B may

also be useful for other crackers and biscuits on other lines in the factory. Other biscuits will not be

investigated in this project.

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Table 1: Scope of the project

In Scope Out of Scope

• Understand dough make-up for biscuit B and biscuit A

• Understand P&ID for each ingredient and make a simplified

version of the system from delivery to the mixer (Icing sugar,

flour, Vol)

• Understand each ingredient and its system

• Identify important variables

• Decide which methods to use for data collection

• Analysis of collected data

• Studies of other biscuits

• Studies of ingredients not

given in scope of this

project

2 Technical Background In this section, a literature review of interest will be presented. There will be necessary information

about biscuit A and biscuit B, the theory behind the baking process, the ingredients of interest and the

methods for data collection. The literature review and the methodology sections will be further extended

before the submission of the final report.

2.1 Biscuit A and Biscuit B Figure 1 and figure 2 shows how the ratio of ingredients of interest differ between biscuit A and biscuit

B. The doughs require almost the same amount of Vol. Biscuit B does not use icing sugar. Biscuit A

requires a little bit higher mass percentage of shortening compared to Biscuit B, but the ratio between

flour and shortening for Biscuit A is almost 3:1 while almost 2:1 for Biscuit B.

The biscuit dimensions for the two biscuits are shown in Figure 3- figure 5. The width and length for

biscuit A have a deviation acceptance of 2 mm and the width for biscuit B has a deviation acceptance

of 1.5 mm. Packet length is the length of biscuits stacked after each other in a package, see figure 6. The

packet length for both biscuit A and biscuit B have a deviation acceptance of 3 mm. Variation in

ingredients’ quality and quantity parameters generates variation in the dough. Which results in biscuits

dimension variation. Change of width, length and height of the biscuit will result in a packet length

above or below the acceptable range, which results in a rejection of the package.

Figure 2: Ratio between ingredients of importance in biscuit B.

Figure 1: Ratio between ingredients of importance in biscuit A.

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Figure 6: Packet length deviation acceptance (mm).

2.2 Overview of Biscuit making

The flowchart in Figure 7 below illustrates the main process steps for biscuit production. The first step

of the biscuit making process is the mixing where all ingredients are added and mixed together to create

the dough. After the mixing step there is a forming step and thereafter is the baking step. Finally, there

is the packaging step where the biscuits are packed. The technical background will mainly focus on the

mixing and baking step which are of more importance to this project.

2.2.1 Mixing It is important to find the optimum mixing time of a dough to secure a production with biscuits of good

quality. Both biscuit A and biscuit B are short dough biscuit. For short dough biscuits, the mixing time

after the flour has been added should be held at a minimum to restrict the opportunity for hydration of

the flour protein and the formation of gluten. (Manley, Technology of biscuits, crackers and cookies,

2000)

There is normally a two-stage mixing procedure where all ingredients except the flour are first mixed

together for a couple of minutes to dissolve and emulsify the ingredients before the flour is introduced

in the second stage. The flour should then be mixed gently for a minimum amount of time, desirably

less than one minute. Furthermore, it is of importance to choose the right type of mixer for the mixing,

to achieve the good quality and consistency of the dough. (Manley, Technology of biscuits, crackers

and cookies, 2000)

After mixing the dough should stand for a while, approximately 30 min. The liquid in the dough will be

absorbed by the starch and the protein in the flour, which results in a less soft and sticky dough. Dough

consistency is greatly affected by changes in the temperature, which affects the level of solid fat,

dissolution of the sugar and the viscosity of the syrup. The temperature of the dough should preferably

be between 18-22 °C. (Manley, Technology of biscuits, crackers and cookies, 2000)

2.2.2 Baking The baking process can be described with three main changes occurring from dough piece to the baked

biscuit. In the first half part of its duration in the oven, the formation of the biscuit’s structure and texture

x +/-2

x +/-2

x +/- 1.5

x +/- 1.5

x +/- 3

Figure 3: Dimensions deviation of

acceptance for biscuit A (mm). Figure 4: Dimension deviation of

acceptance for biscuit B (mm).

Figure 5: Biscuit height.

MIXING BAKING FORMING PACKAGING

Figure 7: Flowchart of the four main process steps for biscuit making.

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occur. The moisture loss occurs mainly in the middle part of the oven and the colour is finally developed

in the last third part of the oven. (Davidson, 2016)

During baking, the temperature of the dough will rise and the gluten web will swell and get stronger.

Gas and air bubbles will be formed, which results in an increased Volume of the dough piece. Air

bubbles are saturated with water and will expand at higher temperatures, at a temperature of 95 °C the

increase of the Volume can be up to 50%. The gluten proteins will swell between 30 °C and 50 °C.

Above 50 °C the denaturation of the protein starts and above 70 °C the protein will coagulate.

Furthermore, some of the moisture will be released from the gluten proteins above 70 °C, which will

contribute to the starch hydration and then gelatinisation. Since there is a limited amount of water in

biscuit dough, the gelatinisation process will only be partial. (Davidson, 2016)

In Table 2 below there are several reasons presented which may cause base cake dimension variation,

based on the literature.

Table 2: Reasons for variations in dough spread during baking (Davidson, 2016).

The increased dough spread during baking The reduced dough spread during baking

• Coarse flour particles

• Minimum mixing after flour addition

• A lower particle size of the sugar

• Higher dough temperature

• Higher fat content

• More Vol (result in higher pH)

• Very fresh dough

• High dough piece weight (density)

• Greasy or cold oven band

• Low temperature in first oven section

• Chlorinated flour

• Too long mixing time

• Higher mean sugar particle size

• A lower quantity of sugar

• Lower dough temperature

• Lower fat content

• Old doughs

• Lower dough piece weight (density)

• Higher baking temperature and faster

baking

2.3 Structure of short doughs biscuits The structure of the biscuit is developed during the baking step where the heat transforms the viscoelastic

dough piece into a solid biscuit with a characteristic structure. The dough expands due to gases from

leaving agents and vaporised water (Chevallier, Colonna, Bule´on, & Della Valle, 2000). The oven

temperature is of importance to allow the biscuit to expand slowly, a too fast expansion will lead to an

undeveloped structure that cannot hold the gas bubbles, which lead to collapse (Manley, Biscuit, Cracker

and Cookie Recipes for the Food Industry, 2001). Complex chemical and physical changes occur when

the dough is baked. Rheological properties are changed by water losses, and by melting and thermal

denaturation of ingredient components. The ingredients in short doughs are separated between two

phases, one fat phase and one phase of sucrose solution with flour and starch particles. (Chevallier,

Colonna, Bule´on, & Della Valle, 2000).

A study was performed by Chevallier et al. on short doughs to investigate sugars, starch, proteins and

lipids contribution to the cohesiveness of the dough and to the biscuit final structure. Results presented

in the report conclude that the biscuit structure can be defined as a composite matrix made of protein

aggregates, sugars, lipids and starch. Furthermore, that protein and starch are not involved in its

cohesiveness and that lipids may be involved but are not alone responsible. Consequently, sugar is

involved in short dough cohesiveness. The sugar melts during baking and becomes glassy when cooled,

the sugar forms bridges between aggregated proteins, lipids and starch granules. Sugar develop both

5

texture and colour during baking. Figure 8 shows a representation of the matrix structure in the dough

and baked biscuit. (Chevallier, Colonna, Bule´on, & Della Valle, 2000)

Figure 8: a) illustrates the gluten network in a structured product, b) the two phases in short dough, the fat phase and the sugar

solution with gluten and starch, c) the sugar phase are responsible for the structure due to embedding and bridging of the other

components. (Chevallier, Colonna, Bule´on, & Della Valle, 2000)

2.4 Ingredients of importance

In the previous section, several possible reasons for the variation of dough spread during baking were

presented, based on literature. In Table 3 below, further variables and factors are presented which are

possible reasons for the variation of the quality and quantity of the ingredients in the factory.

Table 3: Variables that may cause quality and quantity variation of ingredients.

Ingredient Variable and factors of interest

Shortening • The fat is stored in different tanks in two different

locations

• The storage time before delivery to the mixer

• Temperature

• Consistency

Icing sugar • The delivery system from storage to the mixer is shared

with graded sugar, may cause greater mean particle size

• Moisture

Ammonium Bicarbonate (Vol) • Variation of the concentration

• Usage of both dry and liquid Vol (diluted in water)

Flour • Temperature (storage and delivery)

• Flour level

• Quality parameters

2.4.1 Shortening Fat contributes to the structure of a biscuit as well as its eating quality and flavour, which make fat as

one of the most important ingredients in biscuits. Chemical and physical properties, oil refining process,

physical properties are modified to achieve the requirements of products. The term shortening is used

to describe how the fat in biscuits shortens the dough to give the biscuit its crumbly texture

characteristics. (Manley, Technology of biscuits, crackers and cookies, 2000)

6

The fat is added to the dough during the mixing phase. Fat forms a coat around the flour which prevent

the water to get in contact with the flour when it is added to the mixing phase. Flour in contact with

water and with the addition of energy will allow the protein gluten to be more extensible through the

mixing phase. As a result, more water in contact with flour will contribute to a harder and more brittle

dough. Moreover, the fat forms a coat around air bubbles which prevent the bubbles from rupturing and

coalescing into larger bubbles, this function is not as important but may be a factor in maintaining a

good structure in the dough. (Manley, Technology of biscuits, crackers and cookies, 2000)

Fats are composed of three fatty acids attached to a glycerol molecule, forms a triglyceride. What

characterise different fats are the number of carbon atoms in the fatty acid and if the fat is saturated or

unsaturated. Saturated fats have only single bonds between carbon atoms while unsaturated fats also

have double bonds between its carbon atoms. The double bonds can have different configurations, they

can be either in ‘cis' or ‘trans' formation. (Manley, Technology of biscuits, crackers and cookies, 2000)

The melting point decrease with the increased degree of saturation in the fat. Fat does not consist of one

single molecule, there are several different triglycerides present with different fatty acids which result

in a melting point that is not exact, as a consequence the fat melts over a temperature range. (Manley,

Technology of biscuits, crackers and cookies, 2000)

Fats can exist in several different forms and are therefore known as polymorphic. When the fat is cooled

it will first form α crystals which is an unstable form, depending on process and storage conditions the

fat will thereafter quickly recrystallize to β’ or β form. Chemical breakdown of the fat can occur if it is

exposed to oxygen or air, which is difficult to avoid. The breakdown reaction is catalyzed by factors

like temperature, light and certain metals. The fat is therefore ideally not to be stored at a temperature

of 10-15 °C above its melting temperature, in a darker location and stored away from metals like copper

and brass. If the fat is stored at a higher temperature β' crystals will transform to β crystals which result

in bigger crystals that are less functional, and the fat will look grainy will become difficult to handle.

(Manley, Technology of biscuits, crackers and cookies, 2000)

Palm oil consists of an equal amount of unsaturated and saturated fats. The unsaturated fatty acids are

1% Myristic acid (C14), 45% Palmitic acid (C16), 4% Stearic acid (C18) and saturated are 40% Oleic

acid (C18) and 10% Linolenic acid (C18). (Manley, Technology of biscuits, crackers and cookies, 2000)

2.4.2 Sugar Sucrose is a disaccharide which consists of the two monosaccharides, glucose and fructose (Davidson,

2016). Sucrose is primarily derived from sugar cane or sugar beet. Sugar is an important ingredient in

biscuits, it generates sweetness and flavour to the biscuit. Sugar retard the fat from rancidity, which

results in a longer shelf life of the biscuit. Furthermore, sugar affects the texture of the biscuit. Sugar

partially dissolves during mixing and baking, the amount of sugar that dissolves depends on the amount

of water. More water results in more dissolved sugar. After the baking step, the sugar recrystallises as a

supercooled liquid and forms an amorphous glass. A higher quantity of sugar will generate a harder

biscuit. The dissolution of sugar also depends on the particle size of the sugar. The sugar affects the

appearance of the biscuit, how much it spread when baked and the crunchiness. Furthermore, sucrose

rises the starch gelatinisation temperature, which results in a longer time for the dough to rise in the

oven. (Manley, Technology of biscuits, crackers and cookies, 2000)

Icing sugar has a fine particle size and is generated by milling of coarser sugar. The size of the crystal

is generally expressed in term of mean aperture (MA) and coefficient of variation (CV). Granulated

sugar has normally a value of MA between 570-635 µm and CV 26-30%. Smaller crystals dissolve

quicker in the mouth, as mentioned earlier. A particle size greater than 40 µm can feel gritty between

the teeth while a size greater than 20 µm can be detected by the tongue. (Manley, Technology of biscuits,

crackers and cookies, 2000)

Moisture of sugar is normally around 0.4%. It is important to store sugar at the place with even

temperature and with a temperature close to the delivery temperature. Furthermore, the humidity should

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be low otherwise, the sugar will build lumps or stick to the silo wall. (Manley, Technology of biscuits,

crackers and cookies, 2000)

There is a relationship between short dough spread during the baking step and the particle size of the

sugar. Anything that can cause variation in the particle size is therefore of importance. The conveying

system can reduce the particle size, it depends on which conveying system that is used. There could be

a chain and bucket-type conveyor system or a pneumatic conveyor system which may reduce the particle

size more. The dust from the sugar should be collected and removed, the dust should not be used as

sugar in the mixing. The sugar should be kept insulated and at a constant temperature to minimize the

effect of fluctuations in temperature. (Manley, Technology of biscuits, crackers and cookies, 2000)

2.4.2.1 Icing Sugar at Arnott’s

The icing sugar process at Arnott’s is a controlled process and goes under the ingredient handling system

(IHS) at Arnott’s. The production line which produces biscuit A and B changed earlier this year from

using bags of icing sugar to using the IHS system. The sugar IHS system is a shared system between

icing and graded sugar. See the simplified process diagram in Figure 9. The motor keeps running when

a sugar call is finished to empty remaining sugar in the line. The remaining sugar accumulates in an

accumulation hopper together with collected dust from the system. A couple of kilos gets accumulated

in the hopper, exact quantity is unknown due to no weight control. The sugar in the accumulation hopper

gets emptied before the valve opens to the icing or graded sugar. A short description of each process

step can be found in Appendix B together with a decision tree for the current programming sequence

for an icing sugar call.

Figure 9: A simplified process diagram for the bulk sugar system.

2.4.3 Ammonium Bicarbonate (NH4)HCO3

Ammonium bicarbonate as a biscuit ingredient is called ‘Vol’, which derives from ‘Volatile salt'. Vol

decomposes completely to carbon dioxide gas, ammonia gas and water at a temperature between 45-65

°C. Vol is a leaving agent that causes the dough piece to rise and expand during baking due to the

produced gas. Vol is soluble in water and generates softer doughs which result in a smaller amount of

water needs to be added to the dough for a given consistency. This is a result of interactions between

the ammonium ion and the protein in the flour. (Manley, Technology of biscuits, crackers and cookies,

2000)

The salt is purchased as a white crystalline solid. Even though the salt is stored in a dry place it gets

easily lumps, the salt should preferably be used soon after delivery. There is a recommendation that Vol

should be dissolved in water before it is added to the mixer. Vol has a significant effect on the spread of

the dough during baking. (Manley, Technology of biscuits, crackers and cookies, 2000)

The liquid Vol is made up by mixing of hot water and Ammonium bicarbonate, see equation 1 below.

The reaction is endothermic, which cause a temperature drop in the water. The solution is clear and

colourless. (Sigma-Aldrich, Retrieved: 10/08/2018)

NH4HCO3 45−65 °C→ NH3 + H2O + CO2 (Equation 1)

8

The formation of solid compounds in the Vol system is an important challenge since it will result in a

concentration drop in the liquid that is transferred to the mixers and the solids also cause clogging in the

system and regular cleaning of the process are therefore necessary. Figure 10 shows the four different

solids that can be formed in the liquid Vol solution. (Sutter, 2017)

Figure 10: Liquid Vol is volatile and can easily decompose to CO2, H2O and NH3 vapour, as well as recrystallise to solids. (Sutter,

2017)

2.4.3.1 The liquid Vol process at Arnott’s

The liquid Vol is made up by mixing the ammonium bicarbonate salt with warm water. Two different

tanks can be used for the make-up, and the make-up procedure is different between the two tanks due to

the different design of the stirrers. The concentration of the solution is checked before it is transferred

to one of the three storage tanks. The concentration should be within a concentration range of 16.4-16.8

%. Approximately 2100 kg of liquid Vol is prepared in a make-up tank and transferred up to one of the

three storage tanks when the concentration is within range. There are 200-300 hundred kilos of liquid

Vol remaining in the storage tank when the fresh Vol enter. There is a solid sediment in the bottom of

the storage tanks, below the tank outlet point, which is removed when the cleaning is performed. There

is no stirring or temperature control in the storage tanks. A simplified process diagram of the Vol system

is given in Figure 11.

Figure 11: A simplified process diagram for the Vol system.

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The liquid Vol has a temperature that is higher than ambient temperature when it is delivered up to the

storage tanks. When the tank is waiting to be activated for delivery, the temperature in the tank will drop

to a lower temperature. The solubility of the salt decrease with a decrease in temperature (Sutter, 2017).

The process is manual and almost no control is performed after the Vol is delivered.

Photos below are taken by a team leader for ingredients services at Arnott’s factory in Virginia. The

figure to the left shows the two impellers in make-up tank number 1, the photo is taken before cleaning.

The figure to the bottom right shows the impeller in make-up tank number 2, after cleaning. The figure

to the upper right is taken before cleaning of a storage tank. There is obviously quite an amount of

sediment accumulated in the tanks before cleaning. The sediment is recrystallized solids and the

insoluble free-flow agent Magnesium Carbonate. There is 0.55-0.9% of Magnesium Carbonate in the

dry Vol, according to data from the supplier.

2.4.4 Flour Flour is the main ingredients in most biscuits and it strongly contributes to the baked texture, hardness

and shape of biscuits. The gluten proteins in wheat flour contribute to the dough forming capacity of the

biscuit dough. Wheat flour contains the proteins gliadin and glutenin which in presence of water is

combined with the protein gluten (Davidson, 2016).

The wheat grains are milled to either soft, medium or hard flour. Harder flour has a higher protein

content, hard flour has approximately protein content of 10-14% and the levels for soft flour are typically

between 8-11%. When the wheat is milled to produce hard flour, the grain shatters and the starch

granules partly damages. Which results in a higher amount of water is needed to get a standard

consistency of the dough. On the other hand, soft flour has less damaged starch, lower water absorption

and lower level of gluten protein. As a result, the gluten is less elastic and resistant to deformation.

(Manley, Technology of biscuits, crackers and cookies, 2000)

Figure 12: Photo to the left is taken before cleaning is done on make-up tank number 1. Photo to the top right is taken on

one of the storage tanks before cleaning. Photo on the bottom right is taken after cleaning of make-up tank 2. Photo credits:

Corey Smith.

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Soft flour is preferable for biscuit making while hard flour suits better for bread making. The mean

particle size for biscuit flours is generally around 50 µm. After milling, the flour changes its dough-

making properties during the first two weeks due to oxidation. The oxidation of the flour is undesirable

for some biscuits, the proteins changes and it generally makes the gluten not as extensible. Different

storage time of flour may have an impact of the biscuit since there will be different properties of the

flour. (Manley, Technology of biscuits, crackers and cookies, 2000)

Wheat flour consists of 70-75% starch. Starch consists mainly of two glucose polymers, the linear

polymer amylose and the highly branched polymer amylopectin. The quality of a dough and the gluten

contained can be tested with rheological instruments. Flour quality is of interest to produce repeatable

consistencies, appropriate for forming and to produce a satisfactory biscuit. A cookie spread test is a

baking test for short dough cookies, to reduce the spreadability of the dough the miller can treat the flour

with chlorine gas. There are several different ways to determine the quality of the delivered flour. A

quick check is to dip some of the flour in water and compare the colour with a standard sample. If the

sample gets a different colour, further investigation is needed. Furthermore, moisture test and a baking

test could be used to confirm that the quality of the flour is acceptable. The moisture content should be

around 14%. The packaging density of flour is approximately 487 kg per cubic meter. Generally, strong

flour has a higher density than weak flour. (Manley, Technology of biscuits, crackers and cookies, 2000)

2.4.4.1 The flour system at Arnott’s

Flour gets delivered several times each day to the factory. By studying time series in Historian, each

flour batch is generally used in the biscuits within 24 hours, except for deliveries just before the

weekend. The flour in transit silo 5 generally gets transported to storage silo 2, and the flour in transit

silo 6 gets transferred to storage silo 1. The flour is getting transferred from the storage silo to the

weigher hopper when a mixer is calling for flour. The flour is then transferred through system A or

system B to a transfer hopper and then to a flour sifter and then finally to the mixer. A simplified process

diagram is given in Figure 13.

The weight transmitters in the silos are not accurate. Through discussion with operators down in the

ingredient handling area, the reason is due to that the flour may be distributed unevenly when transferred

into the storage tanks. The level may be significantly higher on one side of the tank while lower on the

other side. The weight transmitter measures the weight by a distance from the sensor to the level of the

flour. So due to the uneven distribution the level where the transmitter measure will probably not

represent the real weight in the silo. For example, a batch weight to the transit silos is often around 32-

33 ton, while the measured mass of flour ending up in the storage silos can vary between 5-25 ton.

Obviously, the weight sensor in the storage silos is very inaccurate.

Quality parameters of the flour are very controlled by the supplier. Information about the flour is given

for every delivery and recorded by Arnott’s. The parameters are almost never outside specification and

the transition to new season flour occur smoothly by mixing the first couple of flour batches with 30 %

of the new season flour with old season flour, after a couple of weeks, 60% of new season flour is mixed

in and finally after a couple of weeks the full batch contains only of new season flour.

The impact of the flour on the biscuits will be investigated through evaluation of the delivery process,

storage and the mixing time, which according to literature may have an impact on biscuit dimension

variation. Each delivered order of flour have recorded data on quality specifications, and the flour is

rejected if outside specification range, which rarely happens.

The quality specifications recorded are:

• Protein (%)

• Moisture (%)

• Water absorption (%)

• Rmax (BU) (maximum resistance)

• Ratio of new season flour to old season flour

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Figure 13: A simplified process diagram for the flour system.

2.4.5 Statistical Analysis The Pearson’s correlation coefficient, r, is a dimensionless index. It measures the linear relationship

between two sets of variables or the strength of a straight line. If an observed pattern between two

variables seems to be linear, the correlation coefficient will measure the strength of the relationship.

Pearson’s correlation is one of the most used statistics, after the mean. The coefficient is defined as the

mean product of paired standardised scores of the two variables. The correlation coefficient assumes an

underlying linear relationship between the two variables. The coefficient is calculated with the equation

below, x and y are the two variables. (Ratner, 2009)

𝑟 = (∑(𝑥 − �̅�) (𝑦 − �̅�)

√[ ∑(𝑥 − �̅�)2 ∑(𝑦 − �̅�)2])

The correlation coefficient takes any value between and inclusive of -1 and +1. A value of +1 indicates

a perfect strong positive relationship, the two variables increase in their values through an extract linear

rule. On the other hand, a value of -1 indicates a perfect strong negative relationship, when one of the

variables increase, the second variable decrease through an exact linear rule. A value of 0 indicates no

linear relationship between the two variables. A rule of thumb is that values between 0 and +/- 0.3

indicates a positive/ negative weak linear relationship through a shaky linear rule. Values between +/-

0.3 and +/- 0.7 indicate a moderate positive/ negative linear relationship through a fuzzy linear rule.

Values between +/- 0.7 and +/- 0.9 indicate a strong positive/ negative linear relationship through a firm

linear rule. Figure 14 below illustrates examples of how scatter plots may appear for different

correlations. (Ratner, 2009)

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Figure 14: The correlation coefficient shows on the strength and direction of correlation. (Correlation Coefficient 'r' and The Linear

Model, 2018)

3 Project Plan

In this section planned methods will be described. Furthermore, this paragraph will present the necessary

resources, the work timeline, risks with the project and the project opportunities.

In the laboratory, there are several different instruments that are available to use for different purposes

and tests. Possible instruments that may be used is a texture analyser that can measure the firmness and

stickiness of the dough. Furthermore, there is a Mettler Toledo T70 available in the lab which can

measure the concentration of free ammonia ions through titration. Data will also be collected and

analysed with the software Historian which is used in the factory to measure specific control variables.

The collected data from sampling and trials will then be analysed together with the data from the

historian.

3.1 Methodology The methodology of data collection and retrieval for the four ingredients will be described. Collection

time, mixing number and dough mixing start time are recorded for each sample that is collected for

testing. Additional data will be recorded for each ingredient, dependent on what parameters that are of

interest. The ingredients’ impact on the biscuit dimension variation will be evaluated by calculation of

the correlation between collected data, recorded quality data and corresponding dimensions of the

biscuits.

3.1.1 Biscuit Dimension and Packet Length Data Retrieval Packet length, biscuit length and biscuit width are measured and recorded with a software on the

production line.

3.1.1.1 Selection of data collection times

Different sizes of time series will be of interest, dependent on which parameters that are analysed. There

will be different sizes of the time series dependent on if there is one dough, several doughs or a storage

tank that is analysed. Furthermore, the different parameters will generate biscuit dimension results on

the line after different time-lags.

A new dough is getting mixed with a frequency of approximately 30-35 minutes for both Biscuit A and

Biscuit B. When a dough is tipped, it takes approximately 45 minutes until the first dough piece has

gone through the forming and baking step and generates dimension results in the software Wonderware/

Historian. Data for a specific dough will be retrieved 75 minutes after the dough mixing started and

finished when next dough should generate results on the line.

The Vol process is manual and the time when a bucket is filled, and the trolley is prepared is not

recorded. It is therefore not possible to know the exact time between filled bucket to biscuit dimension

results on the line. There are normally two to four ingredient trollies prepared for dough mixing, which

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corresponds to a time-lag of one to two hours, from filling to emptying in the mixer. The total time-lag

from storage tank changeover to biscuit dimension results are therefore roughly 135 to 200 minutes.

When the concentration in a bucket is analysed, data will be matched with current storage tank and data

from the line retrieved 75 minutes after mixing start time. Data will be retrieved 3 hours after storage

tank changeover.

3.1.1.2 Data analysis

The results of the packet length (PL), biscuit width (w) and biscuit length (l) will be presented as

millimetre deviation from the target value. Average dimension deviation (∆x) was calculated with

equation 1, standard deviation (𝑠𝑥) with equation 2 and the Pearson’s correlation coefficient can be

calculated with equation 3. Furthermore, the coefficient is sensitive to outliers. Due to the sensitivity of

the Person’s correlation coefficient, outliers are investigated, and obvious outliers removed.

Additionally, a larger set of data will result in a more accurate correlation due to less impact of possible

outliers (Rodgers & Nicewander, 1988). The standard deviation is calculated for all data points over the

8 rows. The standard deviation and the correlation were calculated by in excel. The target value is

𝑥𝑇𝑎𝑟𝑔𝑒𝑡, for the width, length and packet length.

∆𝑥 = ∑𝑥

𝑛 − 𝑥𝑇𝑎𝑟𝑔𝑒𝑡 , 𝑥 = 𝑃𝐿, 𝑙, 𝑤 Equation 1

𝑠𝑥 =√∑(𝑥

2)− ∑(𝑥)2

𝑛

𝑛−1 Equation 2

𝑟 = (∑(𝑥−�̅�) (𝑦−�̅�)

√[ ∑(𝑥−�̅�)2 ∑(𝑦−�̅�)2]) Equation 3

The collected data will be evaluated. The data points that are possible outliers are controlled and

removed. Since the average value and standard deviation of a time series are of interest, outliers may

affect the results to be misleading. The data is recorded from 8 rows on the production line, row A-H.

Situations where few rows show a very low or high value, it is most likely not caused by ingredient

variation, more likely due to complications on the line, and the data points are therefore removed. Data

points from periods of downtime on the line are removed. If a set of data shows too many outliers and

downtime, it is rejected.

3.1.2 Concentration of liquid Vol Through investigation of the liquid Vol system, the concentration of the Vol was decided to be

investigated. A P&ID for the system was studied and control variables and units identified. Samples of

the liquid Vol was decided to be collected from storage tanks and from the buckets on the waiting trollies

next to the mixer.

3.1.2.1 Sample collection

Samples of storage tanks are collected with plastic cups and the time, level of the tank and corresponding

make-up tank together with its concentration are recorded. Samples from the buckets on the trollies will

be taken with 2-ml plastic pipettes and emptied in plastic cups. The bucket is stirred for 10 seconds

before a sample is collected. Time, current storage tank, dough mixing time and trolley number are

recorded. Data on concentration in makeup tanks will be taken from a control sheet in the laboratory.

• Samples from the storage tanks will be collected to analyse if the concentration changes between

the make-up tank and the storage tank.

• Concentration in the buckets will be taken to investigate whether the concentration changes

from the storage tank to buckets.

3.1.2.2 Sample testing

Determination of the concentration of the liquid Vol is done by measuring the concentration of free

ammonia ions in the sample. Concentration is measured through titration with 0.5 M sulfuric acid. The

titration is done with the instrument Mettler Toledo T70. Two tests are performed for each sample. The

two tests are prepared by measuring a 2 ml sample with a glass pipette and diluted in a titration sample

14

cup with 50 ml water measured with a 50-ml glass cylinder. The results of the concentration are

recorded.

3.1.2.3 Sample results

The collected data will be analysed and the results will be studied to see if the concentration of the Vol

is below or above the accepted concentration range, and the impact on biscuit dimension variation will

be evaluated. If there is a correlation, further investigation will be performed to find possible solutions

to eliminate or reduce the variation.

3.1.3 Icing sugar According to literature, the sugar particle size has an impact on the dough spread. Due to the use of the

current sugar system, more or less graded sugar will most likely be present in the icing sugar batches.

What goes into the accumulation hopper after a sugar call gets emptied out with next sugar call. That

translates to a few kilos of graded sugar in the batch of icing sugar, if the previous call was graded sugar.

On the other hand, there will almost only be icing sugar in the batch if the previous call was icing sugar.

Parameters to investigate:

• Comparison of the sugar particle size in the accumulation hopper after an icing sugar call and a

graded sugar call.

• Comparison of the sugar particle size in the mixer, when previous sugar call was an icing sugar

call or a graded sugar call.

• Correlation analysis of average biscuit dimensions variation for a certain amount of doughs, to

the ratio of icing sugar calls per graded sugar calls.

• Correlation analysis of average biscuit dimensions variation for a certain amount of doughs, to

the number of doughs which had a graded sugar call before the mixer called for icing sugar.

3.1.3.1 Sample collection - Particle size icing sugar

Samples of the sugar in the mixer will be collected by the operator at the mixer. A plastic bag will be

filled with sugar and the current trolley number recorded. The time for the sugar call, mixing time and

if previous calls in the system were graded or icing sugar will be generated from Historian.

Samples of the sugar in the accumulator will be collected between sugar calls. Time and previous sugar

calls will be recorded. The sample collection from the accumulator will give a smaller sample size

compared to the sample from the mixer due to a small spoon which collects the sample in a combination

of a fan which blows away some of the sugar.

3.1.3.2 Sample testing - Particle size icing sugar

Particle size is assessed by dry sieving, 50 grams of the sugar sample from the mixer is being sieved,

for the accumulation hopper, the whole sample is tested since the sample weight is less than 50 grams.

The sieves will separate the sugar particles: less than 75 µm, 75-150 µm, 150-250 µm and particles

greater than 250 µm. During procedure follows:

1. The weights of each sieve will first be measured and recorded.

2. The weight of the sample is weighted and recorded.

3. The sieves are sieved for 5 minutes with the amplitude set to 6.

4. The weight of each sieve together with the sugar on each sieve is checked and recorded.

5. The sieves are cleaned between sample testing with a brush and compressed air.

3.1.3.3 Sample results - Particle size icing sugar

Particle size distribution is checked by calculation of the weight percentage of the sugar sample present

on each sieve. The results will be visualized with a histogram for each sample.

3.1.3.4 Sample collection - Frequency of sugar calls

This data will be taken from the software Historian. A time period of 25 Biscuit A’s icing sugar calls

will be analysed.

Procedure:

1. Select a start and finish time which covers 25 icing sugar calls from Biscuit A’s mixer.

2. Count number of icing sugar calls and graded sugar calls during the selected period.

3. Count how many of the 25 selected sugar batches had graded sugar as the previous call.

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4. The results are assumed to give dimension results on the line 75 minutes after the first sugar

gets called. Packet length, biscuit length and width during the period are generated from

Historian.

5. The average value and the standard deviation of the packet length, biscuit width and biscuit

length are calculated as single values of the average the 8 rows during the time-period. Obvious

outliers are removed.

3.1.4 Flour

3.1.4.1 Protein level

The protein level in the flour have deviated slightly from the specification range for some deliveries

during 2018. The impact on the biscuit dimension variation was evaluated by identifying a flour batch

with significant lower or higher protein level compared to previous flour batch. Time series of the biscuit

dimensions that corresponds to the period for when the flour should give a result on the line was then

analysed to see whether the protein level change may have a significant impact on the biscuit

dimensions.

3.1.4.2 Temperature

A higher dough temperature can according to literature increase the dough spread during baking. Flour

is the main ingredient in the two biscuits and represents more than half the weight in biscuit A. The

impact caused by temperature fluctuations of the flour delivered to the mixer will be evaluated against

the variation in the dough temperature. There is a temperature transmitter in sifter A and one in sifter B,

and the data is recorded in Historian. The dough temperature is measured and recorded by the mixing

operator before the dough is transported to the forming section. The temperature of the dough together

with corresponding flour sifter temperature will be retrieved for a couple of days.

3.1.4.3 Quantity

The weight of each dough batch is checked before it is sent to the forming step. The measured dough

weight deviates often from the expected weight. Through investigation of the weighing procedure,

almost everything is scraped out from the mixer, there could only be a few kilos left in the mixer. There

could be several different reasons for the weight variation. It could be due to that the scale is not accurate,

or that the operators are performing the procedure in different ways. A time series of the dough weight

is of interest to investigate if the weight variation could be due to that the operators are performing the

task differently.

The bulk ingredients are suspected to cause the variation. Ingredients which are manually added to the

mixer may vary slightly, but not enough to cause a great weight difference. Sugar, flour and shortening

are added through the bulk system. Due to the high ratio of flour in the dough, it may be the ingredient

that causes the weight difference. Furthermore, it has been previous issues with valves in the flour

system that do not close properly which causes the flour to leak into the wrong mixer. Ingredients must

have gone into another mixer or the weight transmitter or the flowmeter are not working properly, on

the other hand when the weight is getting too high ingredients from other lines may end up in the mixer.

The dough weight is generally either too high or too low compared to expected weight, the flour system

may cause the weight difference. The dough weight is to be analysed towards both flour system A and

system B.

3.2 Resources Several different types of resources have been necessary for the development of this project outcome.

The book Technology of biscuits, crackers and cookies by Duncan Manley has been used to generate

knowledge about biscuit making. The book has been very useful for the literature review and covers

much important information about biscuit manufacturing.

16

Operators and technical personnel in the factory have been very important resources. Demonstrations

and shared knowledge have been helpful to gain an understanding of how the different processes and

instruments work in the factory, which has been of importance for the project. Furthermore, it has been

necessary to study previous project reports completed by personnel at Arnott’s which were relevant to

the project. For the Vol testing, a previous report done at Arnott’s was very useful to get information

about the Vol used in the factory, Style of Vol addition received by Ken Brown.

Another important sources for this project have been Wern Tan who did her placement at Arnott’s

previous year and are now employed by Arnott’s. Christopher Price who has been my workplace

supervisor and Hayley Erasmus who has been my project supervisor have been important sources for

the project. My UQ supervisor Sophia Rodrigues has been an important resource for the success of the

project outcome, both for the practical performance, the literature review and the reports writing.

University of Queensland’s online library and Lund University’s online library have been used as

resources to further extend the literature review.

3.3 Work timeline

The project timeline is shown in the Gantt chart on the next page. This timeline was created upon the

project proposal report with the purpose to be followed as good as possible to make sure the progress of

the project went efficient and ensure tasks were finished prior to deadlines. Already when the timeline

was created, assumptions were taken that some activities may require longer or shorter time, due to the

limited knowledge about the project when the project proposal was written. Contribution to other

projects in the plant have been conducted simultaneously as this project work, which have contributed

to the development of my professional experience as a graduate engineer. Furthermore, the contribution

in side projects have been of benefit for the company.

3.3.1 Deviations from the interim project report There have been a few deviations from the project timeline which was created upon the project proposal

report. Due to a large number of variables to understand, collect and analyse, the variables icing sugar,

Vol and flour have only been investigated in the project. The ingredient shortening was not evaluated

due to that the other ingredients required more time than planned. No time was planned in the Gantt

chart to evaluate the implemented changes of the icing sugar system, based on recommendations given

in the project proposal. Shortening is known to be an ingredient that has a significant impact on biscuit

dimension variations on the line for both biscuit A and biscuit B, but also for other lines. Other work is

currently conducted to evaluate the shortening system and the focus on that ingredients in this project

was therefore excluded. The focus was instead moved to collect the results gained by the implementation

of the icing sugar recommendations. Furthermore, sections have been extended in this report since the

Project Progress Report and a section for project outcomes have been added.

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3.4 Project Risks and Opportunities

3.4.1 Risk Assessment The level of the risk and hazard has been identified with the risk identification matrix given in

Table 4. Risks and hazards for the project are shown in Table 5. Hazard information on chemicals which

will be used during experiments are shown in Table 6. Necessary personal protective equipment will be

used when recommended.

Table 4: Risk level identification matrix, (The University of Iowa, n.d.)

Table 5: Possible risks and hazards for the project, how to avoid the risk and the risk level based on table 6.

Activity and associated risk/

hazards

Hazard Control Risk Level

Injury or cuts caused by broken

glass in the lab

Handle equipment with care. Use

protective equipment when in contact

with broken glass

Moderate

Walking on site, loud noise,

slippery floor and forklifts

Wear ear protection. Be careful when

walking in the factory and avoid

walking where the floor is wet and on

tracks where forklifts drive

Moderate

Injuries caused by moving parts on

equipment

Be careful when close to moving parts.

Do not touch equipment without

operator permission.

Low

Manual valve not closed properly

after sample collection, leakage of

liquid Vol

Make sure valves are properly closed

and safety lock attach if present

Moderate

Missed calibration of equipment

which results in misleading results

Make sure equipment is calibrated and

be careful when analyzing the data for

outliers and measurement mistakes

Moderate

Unsuccessful data collection due to

line shutdown

Plan data collection for long periods.

Be aware of rescheduling if shutdown

stops the ability to collect data

Moderate

Final recommendations are

diminished due to budget or

approval from stakeholders.

Investigate several opportunities for

changes, provide recommendations

with information and data that support

my recommendations.

Moderate

19

Project tasks are not finished in

time according to the project

timeline

Reschedule timeline and priorities own

project before side projects

High

Injury due to physical contact with

a machine in the factory

Be careful when handling new

equipment and machines. Always

confirm with operators. LOTO (lock

out tag out) training. Follow safety

instructions.

Low

Table 6: Hazard information for chemicals (Ammonium Bicarbonate, u.d.; Sulfuric acid solution, 2018).

Ammonium bicarbonate

Symbol: Exclamation Mark GHS

07

Acute toxicity (oral, dermal, inhalation), category 4

Skin irritation, category 2

Eye irritation, category 2

Skin sensitisation, category 1

Specific Target Organ Toxicity – Single exposure, category 3

Hazard statement: H302 Harmful if swallowed

Precautionary statements:

P301 + P312 + P330

If swallowed, rinse mouth, call a poison centre or doctor/

physician if you feel unwell.

Personal protective equipment Dust mask type N95 (US), Eyeshields, Gloves

Sulfuric acid 0.5 M

Symbol: Corrosion GHS 05 Oxidizing gases, category 1

Oxidizing liquids, categories 1,2,3

Hazard statement: H290 May be corrosive to metals

3.4.2 Project Opportunities This project has given me the opportunity to apply my knowledge which I have received during my four

years at University and to gain knowledge in a field which I am not familiar with but very interested in;

the food industry.

This project has also resulted in an opportunity to reduce waste and increase the profit on one production

line which produces two iconic biscuits at Arnott's. Furthermore, the findings in this project are of

interest for other production lines in the factory.

The broad scope of the project has resulted in findings of possible future project opportunities at

Arnott’s, which are given under section 5.2 Recommendations. A few smaller projects have been in

action as side-projects, for example, measurements of the dough pH and evaluation of the greasing of

the oven band, as well as investigation of the icing sugar impact on other biscuits.

20

4 Results and Discussion This section covers results and discussions of the three ingredients Vol, sugar and flour.

4.1 Vol Concentration - Results and Discussion The results from the testing of the concentration in storage tanks will first be presented. Next up is the

concentration difference between the make-up tank and corresponding storage tank, and then the

concentration difference between storage tank and buckets. An analysis will thereafter be presented on

the correlation between biscuit dimension variation and the concentration variation in storage tanks, and

finally also between the bucket concentration and its corresponding biscuit dimensions on the line.

4.1.1 Variation in Vol concentration in storage tanks The concentration in the storage tank was measured during a couple of weeks, the results are plotted in

Figure 15. Several samples of the concentration in one tank were measured the 17th, 28th, 10th and 11th

to evaluate whether the concentration increase or decrease when the tank is being emptied. The

concentration should appear between the two green lines which represent the acceptable range (16.4 -

16.8 wt%). Concentration above and below this range is too high or too low, concentrations below the

red line are very low. By studying the results, it can be concluded that the concentration often is too low

or just within the accepted range. The concentration may vary in the tank when it is being emptied, but

it is hard to tell whether the actual concentration gets lower or higher when a tank is being emptied since

these measurements show both an increase and decrease of the concentration.

Figure 15: The measured concentration is from the storage tank which is selected for delivery, current day and time. Under the red

line, the concentration is too low, under 16.4% is lower than the accepted range. The accepted range is between the green lines.

4.1.2 Vol concentration difference between make-up tank and storage tank Measured storage tank concentration can be seen in Figure 16, together with corresponding make-up

tank concentration. There is obviously a concentration drop between the make-up tank and storage tank,

which occur in 8 of 9 tests. Sample 4 had a significant concentration drop, this could possibly be due to

that the tank was emptied on a Monday, it could be due to the stand-time over the weekend. The

temperature in the tank will drop over the weekend when fewer and no production lines and ovens are

running.

Figure 16: Measured concentration of storage tank (ST) against the corresponding make-up tank (MT).

15.5

16.0

16.5

17.0

17.5

CO

NC

(%

)

DATE

V O L C O N C ENTR AT I O N - S T O R AG E T AN K

ST conc (%) Below is too low conc. Below is low conc. Below is range of conc.

1 2 3 4 5 6 7 8 9

MT conc. 16.44 16.37 16.43 16.43 16.82 16.47 16.74 16.55 16.53

ST conc. 16.08 16.34 16.48 15.68 16.53 16.34 16.21 16.17 16.07

15.5

15.7

15.9

16.1

16.3

16.5

16.7

16.9

Vo

l co

nc. (%

)

Vol Conc. MT → ST

21

4.1.3 Variation of Vol concentration in buckets Figure 17 shows the results from measurements of the concentration in buckets together with the

concentration in corresponding storage tank.

The concentration in the buckets is often lower than the current concentration in the storage tank.

Furthermore, buckets are usually below the accepted concentration range. The concentration of the

buckets seems to drop for almost every second bucket. A hypothesis of the reason to this could be the

stand-time in the pipeline between buckets being filled at the manual tapping point. If the pipe is not

emptied before a new bucket is being filled and it has been a long stand-time for the liquid Vol in the

pipeline system, the Vol may recrystallise easier in the pipes. Furthermore, the concentration variation

could be due to errors when the concentration is measured, due to the high likelihood of getting errors

when the sample is diluted before it is titrated. Further investigation is needed to understand why the

concentration drops down to so low values and what causes the concentration drop. Based on these

results, the concentration to drops from the make-up tank to the storage tank and even more from the

storage tank to buckets.

4.1.4 Correlation between a storage tank and biscuit dimensions Data were retrieved from Historian to investigate the correlation between the biscuit dimensions and the

packet length against the concentration in the storage tank and buckets. The time-period was selected as

the period when a storage tank is selected for delivery to mixers. Summary of the retrieved data can be

seen inError! Reference source not found. Table 7, which gives the average dimensions. The average

concentration of the storage tank and the average concentration in the buckets were analysed against

corresponding dimensions on the production line, data on the concentration can be seen in Table 8. Table

9 shows the calculated correlation.

11

12

13

14

15

16

17

18

CO

NC

(%

)

DATE

V O L C O N C S T - B U C K E T S

ST Dough low conc

Figure 17: The measured concentration in buckets are plotted with current storage tank (ST). The green line shows the low limit of

accepted concentration range. Dough= buckets.

22

Table 7: Average deviation of biscuit dimensions and packet length for the time-period

of one storage tank (dimensions are given in milli meter deviation from specifications).

A negative correlation is noted for almost all measured correlations between the dimensions and the Vol

concentration in table 9. According to literature, a higher concentration of the Vol will allow the dough

piece to expand more during baking which results in increased dimensions. Furthermore, the Vol has an

effect of the dough spread, Vol is very alkaline due to interactions between the protein in the flour and

the ammonium ion, which generate softer doughs that spread more. The correlation between Vol

concentration and biscuit dimensions should be positive, not negative. It is only biscuit A which has a

low positive correlation of 0.26 towards the packet length deviation. Both biscuit A and biscuit B have

other rising agents in the dough recipe, as well as biscuit A has both liquid and dry vol. The liquid vol

in biscuit A and biscuit B covers almost 30wt% of the dry mass of the total rising agents in the recipe,

so for biscuits with not only liquid vol as the rising agent source the impact of concentration change

may not be significant. The concentration variation will most likely have a greater impact on biscuits

with only liquid vol as the rising agent.

Table 8: Average concentration of storage tank and buckets.

Table 9: Linear correlation coefficient between biscuit dimension

deviation and the Vol concentration for both biscuit A and B.

4.1.5 Correlation between the bucket concentration and dimensions The concentration was analysed in 11 buckets against corresponding results on the production line for

biscuit B which has a slightly higher volume of liquid vol compared to biscuit A. The correlation

between the parameters is shown in Table 10. The average dimensions for each dough are plotted against

Dimension deviation

Δl (mm) Δw (mm) ΔPL (mm)

Biscuit Date average average average

A 9/10 0.04 0.90 0.36

A 10/10 -0.44 0.68 0.27

A 2/10 1.36 1.52 0.09

A 3/10 1.03 1.25 0.18

A 27/9 0.76 1.40 0.38

B 4/10 -0.24 -1.72 0.52

B 5/10 -0.11 -1.55 0.63

B 26/9 -0.15 -1.71 -0.01

B 25/9 0.06 -1.64 0.56

B 24/9 -0.24 -2.06 0.03

B 20/9 0.10 -1.52 0.23

Biscuit Date Concentration Bucket (%)

Concentration ST (%)

A 9/10 16.85 17.38

A 10/10 15.94 16.34

A 2/10 16.12 16.49

A 3/10 16.04 16.76

A 27/9 15.37 16.36

B 4/10 16.20 16.35

B 5/10 16.69 16.78

B 26/9 16.11 16.23

B 25/9 14.48 16.17

B 24/9 16.90 16.95

B 20/9 16.49 16.07

Linear Correlation Biscuit A Biscuit B

Δl Conc. ST -0.16 -0.65

Δl Conc. Bucket -0.26 -0.50

Δw Conc ST -0.26 -0.53

Δw Conc. Bucket -0.40 -0.35

ΔPL Conc ST 0.26 -0.05

ΔPL Conc. Bucket -0.04 -0.37

23

corresponding bucket concentration in Figure 18. There is a low negative correlation between the biscuit

dimensions and the vol concentration, similar results as in previous section. Due to the low negative

correlation, vol seems to not have an impact on the biscuit dimension variation. As a rising agent, a

higher concentration would not result in both smaller biscuit dimensions and smaller packet length.

Figure 18: Concentration in buckets and dimension deviation for 11 doughs.

Table 10: Correlation between packet length, biscuit dimensions and the Vol concentration in the buckets.

Linear Correlation

ΔPL Δw -0.62

ΔPL Δl -0.60

ΔPL Conc. bucket -0.37

Δl Conc. bucket -0.28

Δw Conc. bucket -0.24

4.2 Icing sugar - Results and Discussion Results will first be presented from the testing of the icing sugar particle size distribution. Followed by

the results from measurements of the correlation between biscuit dimension variation and the frequency

of graded sugar calls.

4.2.1 Icing sugar particle size The particle size was tested in four icing sugar bags the week before the line started to use the IHS

system instead of manually tipping bags of icing sugar into the mixer. The results from the sieving can

be seen in table 11 and Figure 22, samples were taken from trolley number 124-127 the 31st of August.

Figures and tables included in this result section cover the results of the particle size distribution in the

IHS system and in the accumulation hopper from the 13th, 17th and 27th of September.

The change in the particle size distribution was investigated for when the previous call been icing sugar

and when the previous call been graded sugar. The ‘G’ in the figures is used for a previous call of graded

sugar and the ‘I’ for a previous call of icing sugar. Moreover, a call with G-I-G, had graded as the

previous call, icing sugar as the 2nd previous call and graded sugar as the 3rd previous call. Sample

collection from more days can be found in Appendix A.

Table 11 shows the average particle size distribution for the bags and the IHS system, together with

interim and supplier specifications. Figures 19 - 22 shows histograms of the particle size distribution in

samples collected for the four different days. The results from the 13th do not show similar results as on

the 19th and 27th of September. The distribution of the sugar particle size from the 13th is quite different,

both regarding the results from other days and also based on the interim specification. This is probably

due to something with the supplier, it seems like the sugar has not been milled enough. By comparison

of the particle size distribution in the bags with the IHS system the 19th and the 27th, the IHS shows on

a slightly higher ratio of the smallest particle size range and less within the range of 150-250 µm. This

15.0

15.5

16.0

16.5

17.0

-2.0

-1.0

0.0

1.0

2.0

3.0

1 2 3 4 5 6 7 8 9 10 11

Co

nce

ntr

atio

n (

%)

Dim

ensi

on

dev

iati

on

(m

m)

Sample

Biscuit B - Vol Concentration

Packet Length width length conc. bucket

24

could be due to the new sugar supplier or that the sugar breaks down to smaller particles in the pipes,

furthermore it could be because of the dust collected in the accumulator. Interim specifications are based

on historically collected data. Due to the different methods of testing the sugar particle size distribution,

the interim specifications are more reliable since the same equipment and methodology are used.

Table 11: Average particle size distribution in bags and the IHS system.

The difference in the particle size distribution for samples collected the 19th and 27th of September were

analysed by grouping the samples in three groups. First one for samples with graded sugar as previous

call, second for calls where the 2nd previous call was graded sugar and last one for calls where the two

previous calls been icing sugar. The results are shown as an average distribution for each date, given in

Table 12 and Table 13.

Particle size

range (µm)

BAG IHS

13-Sep

IHS

19-Sep

IHS

27-Sep

Interim

Specification

Supplier

specification

< 75 17.6% 8.1% 23.1% 20.6% 25-35% 54%

75-150 52.9% 45.7% 54.0% 54.3% 45-60% 36%

150-250 27.5% 44.2% 21.1% 20.9% 10-20% 10%

> 250 2.0% 2.0% 1.9% 4.1% 0-3% <1%

0%

10%

20%

30%

40%

50%

60%

I-I-I I-I-G I-I-G I-G-G G-G-I G-G-I I-G-I I-G-I

%

IHS 19/09/2018

<75µm 75-150µm 150-250µm >250µm

0%

10%

20%

30%

40%

50%

60%

G-G-I I-G-I G-I-I G-G-I G-I-I G-I-G

%

IHS 13/09/2018

<75µm 75-150µm 150-250µm >250µm

0%

10%

20%

30%

40%

50%

60%

I-G-I I-G-I G-I-G G-G-I G-I-I G-I-I G-I-G I-I-I

%

IHS 27/09/2018

<75µm 75-150µm 150-250µm >250µm

Figure 19: Icing sugar particle size distribution in samples taken

the 19th of September.

0%

10%

20%

30%

40%

50%

60%

124 125 126 127

BAG 31/08/2018

<75 75-150 150-250 >250

Figure 22: Icing sugar particle size distribution in samples

taken the 27th of September.

Figure 20: Icing sugar particle size distribution in samples taken the 19th of

September. Figure 21: Particle size distribution in icing sugar

bags.

25

Table 12: Average particle size distribution for samples collected the 19th of September, when the previous call was graded, when 2nd

previous was graded and when the two previous calls been icing sugar.

HIS 19-Sep

(µm)

Previous call graded

(G-x-x)

2nd Previous call graded

(I-G-x)

2 previous call icing

(I-I-x)

< 75 21.5% 22.3% 24.9%

75-150 52.9% 52.8% 55.8%

150-250 23.8% 23.2% 17.1%

> 250 1.7% 1.8% 2.1%

Table 13: Average particle size distribution for samples collected the 27th of September, when the previous call was graded, when 2nd

previous was graded and when the two previous calls been icing sugar.

HIS 27-Sep

(µm)

Previous call graded

(G-x-x)

2nd Previous call graded

(I-G-x)

2 Previous call icing

(I-I-x)

< 75 19.9% 15.3% -

75-150 53.5% 37.7% -

150-250 22.3% 11.2% -

> 250 4.3% 2.4% -

By studying the results from the 27th of September there is obviously a higher quantity of large sugar

particles when the previous sugar call has been graded sugar. The percentage of sugar particles with a

size greater than 250 µm is around 7%, compared to normally around 2%. Furthermore, the percentage

of particle size range 150-250 µm is greater than the range of 75-150 µm when previous sugar call was

graded sugar and lower when the previous call was icing sugar. Anyway, the results from the 19th

confirm the theory of more particles in the range of 150-250 µm compared to the range of 75-150 µm

when the previous call was graded sugar. The results demonstrate that the remaining sugar from previous

call has an significant effect on the sugar particle size distribution which is fed to each dough.

The results from sampling of the accumulation hopper are shown in Figure 23 and Figure 24, and the

average particle size distribution are shown in Table 14 for data collected the 12th of September. What

must be taken in to account when studying these results is that the sampling time after a call will affect

the amount of dust which collects in the hopper, the longer time between a finished call and a sample

will most likely result in more dust and smaller particles in the hopper. As expected there are more large

sugar particles when previous sugar call been graded sugar.

Table 14: Average particle size distribution for samples collected from the accumulation hopper the 12th of September. The average

value for samples: when the previous call was graded, when 2nd previous was graded and when the two previous calls been icing

sugar.

ACC 12-sep

(µm)

Previous call graded

(G-x-x)

2nd Previous call graded

(I-G-x)

2 Previous call icing

(I-I-x)

< 75 14.9% 25.4% 21.0%

75-150 44.5% 49.3% 62.1%

150-250 29.8% 22.8% 15.3%

> 250 10.7% 2.5% 1.5%

26

4.2.2 Frequency of Sugar calls The impact of the graded sugar on the biscuit dimension variation was evaluated by analysing the

correlation between data sets from 12 different production days (sample 1-12) and 25 doughs from each

day. Each sample-period corresponds to the total time of 25 selected doughs. A dough will generate

biscuit dimension results on the line approximately 75 minutes after each sugar call. The average

deviation from biscuit dimensions and packet length from each sample-period are given in table 15

together with corresponding sugar information. The ratio between the number of graded sugar calls to

icing sugar calls for the selected time period is given in the table together with the number of doughs

which had a graded sugar call as previous sugar call.

A higher mean particle size of the icing sugar will result in less dough spread during baking, according

to literature. Less dough spread will result in a smaller biscuit length and biscuit width, and a longer

packet length due to increased biscuit height. A lower ratio of graded sugar calls to icing sugar calls

should most likely generate a larger mean particle size, as well as when a previous sugar call is graded

sugar. The average packet length should increase with a higher ratio of G:I sugar and for a higher number

of previous calls with graded sugar. The correlation between the parameters is given in Table 16. There

is quite a strong correlation between the ratio of G:I sugar calls and the number of previous sugar calls

which was graded sugar, a value of 0.79, which is expected since a higher ratio of graded calls should

most likely increase the possibility of having a graded sugar before Biscuit A’s mixer is calling for icing

sugar.

The correlation coefficient between biscuit dimensions and the frequency of sugar calls is generally

negative and quite low. Despite the low correlation coefficient, it is a correlation between the average

biscuit width and the number of sugar calls which had graded sugar as previous call, see scatter plot in

Figure 25. A negative correlation between graded sugar and the width is expected since a greater mean

particle size will result in less spread and smaller biscuit width and length. On the other hand, there is

no correlation between the biscuit length and the frquency of sugar calls. There is a low positiv

correlation between packet length and the number of sugar calls which had graded sugar as previous

call, see Figure 26.

Figure 23: Icing sugar particle size distribution in samples taken

from the accumulator the 12th of September. Figure 24: Icing sugar particle size distribution in samples

taken from the accumulator the 13th of September.

0%

20%

40%

60%

%

ACC 13/09/2018

<75µm 75-150µm 150-250µm >250µm

0%

20%

40%

60%

I-G-I I-I-G G-I-I G-I-I G-I-I I-G-I

ACC 12/09/2018

<75µm 75-150µm 150-250µm >250µm

27

Table 15: Summary of measured data. The average dimension deviation and frequency of sugar calls for each sample date.

Average dimension deviation Frequency of sugar calls

Sample

Date

Δl

(mm)

Standard

deviation

(mm) l

Δw

(mm)

Standard

deviation

(mm) w

ΔPL

(mm)

Standard

deviation

(mm) PL

Icing

calls

(nr)

Graded

calls (nr)

Ratio

(G: I)

Nr. previous

graded

19-Oct -0.16 0.58 0.12 0.56 0.06 0.38 92 55 0.60 15

18-Oct -0.60 0.76 -0.10 0.61 0.25 0.33 83 25 0.30 11

10-Oct -0.46 0.59 -0.30 0.33 0.30 0.23 66 44 0.67 17

9-Oct 0.61 0.74 -0.05 0.33 0.39 0.22 62 39 0.63 20

8-Oct 0.39 0.80 0.04 0.62 0.21 0.47 100 43 0.43 14

3-Oct 1.12 0.55 0.24 0.47 0.28 0.32 51 20 0.39 12

2-Oct 0.46 0.73 0.43 0.33 0.33 0.27 64 55 0.86 19

19-Sep -0.15 0.69 0.92 0.47 0.89 0.48 91 32 0.35 4

18-Sep -0.18 1.21 1.56 0.38 0.56 0.29 72 28 0.39 10

13-Sep -0.36 0.56 0.71 0.28 0.67 0.36 69 43 0.62 19

12-Sep 0.02 0.85 0.73 0.42 0.63 0.26 66 21 0.32 13

11-Sep 0.75 0.69 0.40 0.52 0.21 0.27 106 22 0.21 10

Table 16: Linear correlation between biscuit dimensions, packet length against the ratio of icing to graded (G:I) sugar calls and the

number of doughs with a previous call of graded sugar.

Figure 25: Scatter plot of biscuit width against the number of sugar calls that had graded sugar as previous call.

Linear Correlation

Ratio G:I Previous call graded 0.79

ΔPL Ratio G:I 0.13

ΔPL Previous call graded 0.27

Δw Ratio G:I -0.26

Δw Previous call graded -0.44

Δl Ratio G:I -0.06

Δl Previous call graded -0.07

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

Δx

(mm

)

Nr previous call Graded

Standard deviation width - Number of Previous calls with Graded Sugar

width Linear (width)

28

Figure 26: Average packet length deviation against number of sugar calls with graded sugar as previous call.

The interim results generally indicate on a weak correlation. A high correlation between the dimension

variation and the sugar will most likely not be possible to obtain, due to the variation of all the other

parameters that have an impact on the biscuit dimension variation. At least due to the low correlation,

there may be another parameter that has a greater impact on the biscuit spread than the icing sugar

system. But based on the current measurements, the trend observed agrees with our hypothesis that the

accumulated graded sugar in the system has an impact on the dimension variation of biscuit B.

4.3 Flour – Results and Discussion

4.3.1 Protein level The protein level dropped from 8.9% to 8.6% on the 29th of October. The flour will start to be used

roughly within a day, by tracking the weight in the silos it seems like the new flour batch should have

started being used in the afternoon around 5-9 pm the 30th October. By studying the time series of the

biscuit length, it seems like the average biscuit length dropped around 7 pm with almost 1.5 mm, see

Figure 27. Due to that, the flour has a lower protein level and thereby a lower water absorption capacity,

the dough will get softer and most likely spread more. In this case, the time series shows on less spread

when the protein level drops. A protein level drop of 0.3% will probably not show great results on the

biscuit spread and the dimension drop could most likely be described with another parameter that

changed around the same time.

Figure 27: Time series of the biscuit length deviation the 30th-31st of October. A batch with a lower protein level seems to generate a

biscuit length deviation around 7pm.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0

5

10

15

20

25

19-Oct 18-Oct 10-Oct 9-Oct 8-Oct 3-Oct 2-Oct 19-Sep 18-Sep 13-Sep 12-Sep 11-Sep

Avera

ge P

L d

evia

tion (

mm

)

Nr.

of p

revio

us c

all

Gra

de

d

SAMPLE

Average Packet Length - Number of Previous calls with Graded Sugar

Nr previous call Graded packet length

-3

-2

-1

0

1

2

3

4

5

10am

11am

12pm

1pm

2pm

3pm

4pm

5pm

6pm

7pm

8pm

9pm

10pm

11pm

12am

1am

2am

3am

4am

5am

6am

7am

8am

9am

Devia

tio

n fro

m s

et poin

t

Time

Biscuit length - Protein level changes from 8.9% to 8.6%

29

4.3.2 Flour temperature variation The temperature of the dough was retrieved from the 28th of October to the 2nd of November. The

corresponding flour call was identified for each dough, and witch flour system it went through to retrieve

corresponding sifter temperature. The temperatures are plotted in Figure 28. The temperature rises for

both the flour system and for the dough throughout the week. There is a positive correlation of 0.60

between the flour sifter temperature and the dough temperature. A higher temperature in the sifter will

most likely result in a higher temperature of the flour that gets into the dough, which results in a higher

temperature of the dough. On the other hand, the temperature is increasing quite consistent throughout

the week, which is most likely due to the increased temperature in the mixing area, with more mixers

and ovens running, the temperature in the factory will increase.

Figure 28: Dough temperature for biscuit A between the 28th of October to the 2nd of November together with corresponding flour

sifter temperature.

The variation of the dough temperature was analysed against the corresponding biscuit dimensions on

the line. Calculations showed no correlation between the temperature and the dimension variation of

the biscuit. The scatter plot in Figure 29 illustrates the almost not existing correlation between the

dough temperature and the biscuit dimensions.

Figure 29: Scatter plot of dough temperature versus the dimension variation of the biscuit width.

4.3.3 Dough weight variation Figure 31 below illustrates a box diagram of the dough weight for 176 Biscuit A’s doughs produced in

November. Figure 30 illustrates a box diagram of the dough weight for 166 Biscuit B’s doughs produced

21

21.5

22

22.5

23

23.5

25.5

27.5

29.5

31.5

33.5

35.5

37.5

39.5

41.5

43.5

45.5

Sun

22:0

0

Mon

10

:00

Mon

22

:00

Tue

10

:00

Tue

22

:00

We

d 1

0:0

0

We

d 2

2:0

0

Thu

10

:00

Thu

22

:00

Fri 1

0:0

0

Te

mp

era

ture

Te

mp

era

ture

Dough temperture Bicuit A - Flour sifter temperature

-1.5

-1

-0.5

0

0.5

1

1.5

22.0 22.5 23.0 23.5 24.0 24.5 25.0

Devia

tion w

idth

(m

m)

Temperature

Dough Temperature - Width Biscuit A

30

in November. The dough for biscuit B should have a weight of 660 kilos if the quantity of each ingredient

were correct, on the other hand, biscuit B should have a weight of 705 kilos. Both doughs have an

average weight which is almost the same as the expected weight. Both doughs have a variation of 10

kilos plus or minus the expected weight and a few doughs with a significant weight difference. It does

not seem like there is more common that the weight is either too heavy or too light. When the dough

weight is differing with almost 20 kilos we could almost expect that there will be a noticeable impact

on the line due to a too high or less quantity of ingredients.

Time series of the dough weight variation are shown in Figure 32 and Figure 33. It does not seem to be

a trend based on which operator that are performing the weight check. The trend line for biscuit A is

quite stable, while it is decreasing for biscuit B. If the dough consistency is changing, the quantity of

some basic ingredients in the recipe may sometimes be changed to compensate for the dough consistency

change. The lower average dough weight for the last couple of Biscuit B’s dough in November had a

total weight of ingredients which were lower.

Figure 31: Dough weight

distribution Biscuit A. Figure 30: Dough weight

distribution Biscuit B.

695

700

705

710

715

720

725

Dough w

eig

ht

(kg)

Dough nember 1→166

Biscuit B Dough weight

635

645

655

665

675

Dough w

eig

ht

(kg)

Dough nember 1→176

Biscuit A Dough weight

Figure 32: Dough weight for Biscuit B in November 2018.

Figure 33: Dough weight for Biscuit A in November 2018.

31

Due to that two systems are used to deliver the flour to the mixer, the dough weight was analysed towards

the corresponding flour delivery system. Data were retrieved for biscuit A the 5-6th of November. In

Figure 34 the dough weight is described on the y-axis, the dough number on the x-axis and each bar are

coloured in either red or blue depending on which system that delivered the flour. A red bar represents

a flour batch delivered through system A and a blue bar represent a dough with flour delivered through

system B. There is not an obvious pattern or correlation between the dough weight variation and the

different flour delivery systems.

The dough weight variation was thereafter evaluated against biscuit dimension variation. Data on biscuit

dimensions and dough weight were retrieved between the 5th and the 6th of November for a number of

55 doughs. The calculated linear correlation values are given in Table 17. There is a quite low

correlation, due to the negative correlation between width and length and the positive towards the packet

length, increased dough weight decrease the biscuit width and length. A decreased width and length

results in a longer packet length. A scatter plot of the dough weight against the packet length is given in

Figure 35. The low correlation is noticeable in the scatter plot, a few doughs with significant lower

weights generate a shorter packet length while a few doughs with significant higher weights generate a

longer packet length.

Table 17: Calculated correlation coefficient between dough weight and biscuit dimensions.

Figure 34: Dough weight variation for biscuit A together with information on which flour system that was used for delivery, red for

flour system A and blue for flour system B.

Linear Correlation

Dough weight (kg) Biscuit width -0.20

Dough weight (kg) Biscuit length -0.13

Dough weight (kg) Packet length 0.28

620

630

640

650

660

670

680

Dough w

eig

ht

(kg)

Dough number 1-28

Dough weight variation - Flour system A/B

B

A

32

Figure 35: A scatter plot of the packet length deviation against the dough weight.

5 Conclusions and Recommendations

5.1 Conclusions

5.1.1 Liquid Vol Through an investigation of the liquid Vol system, it is obvious that the process needs some changes to

get a consistent concentration within the accepted concentration range, delivered to mixers and

buckets. Below are interim conclusions and recommendations given to get a more consistent

concentration of the liquid Vol.

According to literature Vol will make the dough to spread and rise, so a negative correlation should not

be possible. Other parameters must be the reason to a negative correlation. Due to the many parameters

that have an impact on the dough and the biscuit dimensions, there are most likely other parameters that

have a greater impact on the dough than the concentration of the Vol.

Adding dry Vol instead of liquid Vol could be a solution to eliminate the variation of the Vol

concentration. Based to an internal study on different ways of Vol addition to the dough, performed in

Huntingwood, biscuits made with dry Vol should generate less dough spread and a significant higher

packet length. Dry Vol is not popular to dissolve for the operators at the mixers, it smells and is toxic if

inhaled and PPE is recommended when handling of dry Vol.

What can be concluded from the interim results are that the concentration drops from the make-up tank

to the storage tank and even more from the storage tank to buckets. Furthermore, the concentration drop

in the tapped buckets are not constant. The reason to the variation is still unknown and is to be further

investigated. A hypothesis is that it is due to stand-time of the liquid Vol in the manual tapping line.

Stand-time varies dependent on the frequency of manual tapping of liquid Vol to buckets.

5.1.2 Icing Sugar The first conclusion to be taken is that it is a higher quantity of graded sugar in the accumulation hopper

after a graded sugar call. The mean sugar particle size distribution is higher in the accumulation hopper

after a graded call, compared to after an icing sugar call. The graded sugar in the accumulation hopper

result in a higher mean particle size of the icing sugar that goes into biscuit B. Due to literature, a higher

mean particle size reduce the dough spread, results to an decrease of biscuit length and width, increase

in height and packet length.

640

645

650

655

660

665

670

675

-1.5 -1 -0.5 0 0.5 1 1.5

Dough w

eig

ht

(kg)

PL deviation (mm)

Packet length - Dough weight

33

The particle size of icing sugar is most likely one of the parameters that have a significant impact on the

biscuit dimension variation, due to the results from the investigation of the correlation between the

frequency of graded sugar calls and the dimension variation of the biscuit. On the other hand, there are

clearly other ingredients that have a strong impact as well. More data is to be collected to draw an

accurate conclusionn from our measurements.

5.1.3 Flour The flour system is a well-controlled process. Quality parameters are in good control and due to a

minimal slow change of quality parameters between batches, small and simple recipe changes can be

done to adjust the dough to fit the new flour without getting a significant problem on the line.

The dough temperature variation seems to have no correlation towards the biscuit dimension variation,

apparently, the dough temperature does not have a great impact on the biscuit dimensions. On the other

hand, the dough temperature is given as a dough spread mechanism in literature. Apparently, the

temperature difference that occurs is probably not big enough to cause a noticeable impact on the dough

spread. Furthermore, the dough temperature and the temperature of the sifters in the floor system has a

moderate correlation. But due to no correlation between biscuit spread and dough temperature, the

temperature of the flour is obviously not a parameter with great impact on the biscuit dimension

variation.

A low correlation between the dough weight and the biscuit dimensions prove that the dough weight is

not one of the main parameters that have an impact on the dough spread. On the other hand, the low

correlation may have an impact on doughs that have a significant weight difference. Furthermore, there

is not an obvious trend that shows any problem with the two delivery systems for flour.

5.2 Recommendations Recommendations are to continue the work on which process and ingredient parameters that have a

significant impact on the biscuit dimension variation on biscuit A and biscuit B. Furthermore, my

recommendation is to build a time series with parameters of interest together with biscuit dimensions

and then evaluate the data set through multivariate analysis. To simplify a multivariate analysis, there

may be of interest to analyse the data in Matlab with PCA (Principal Component Analysis) and/ or PLS

(Partial Least Squares/ Projection to Latent Structures). (Kresta, MacGregor, & Marlin, 1991)

By performing a PCA it would be possible to put parameters against each other and analyse which ones

that have a more significant impact on the biscuit dimension variation. Creating such a data set would

require a lot of work but it may explain better on how the line is performing based on process and

ingredients changes. The figure below shows parameters that have an impact on biscuit spread according

to literature. The parameters with a red circle are the parameters that have been investigated in this

project.

34

5.2.1 Liquid Vol The make-up procedure needs to be consistent. Currently, the procedure is manual and varies slightly

between the shifts. A flowmeter will in the upcoming weeks be added to the water pipes which enters

the make-up tanks. This will make it possible to add a consistent amount of water to the make-up tanks.

Furthermore, the titration procedure is quite sensitive for errors, due to the dilution of each sample. It is

easy to add a little bit too much Vol sample, which changes the concentration a lot and since there are

several people testing the Vol on different shifts, there is most likely a human error in the results. Due

to the low and the not expected correlation, a conclusion can be taken that the variation of the liquid vol

do not has a great impact on the dough spread for biscuit A and biscuit B.

A redesign of current make-up tanks should result in a more efficient make-up process. As well as

generate a more consistent result of the concentration and result in a less time required mixing process.

Furthermore, it should eventually reduce the requirements of retesting and adjustments of the liquid Vol.

Tank dimension recommendations for the make-up tanks are given in Figure 37 below.

Recommendations are to change the diameter of the axial-flow impeller to optimal diameter, install four

baffles to increase the turbulence flow. Without baffles, results to circulation flow around the stirrer,

which results to poor mixing performance. Baffles, right dimension and position of the impeller together

with the right level will increase the mixing performance. If the tank level needs to be higher than the

diameter, a 2nd impeller is recommended, but it will require a higher energy consumption. A heating

jacket is recommended to keep the temperature within the right temperature range, recommended by the

supplier that the temperature of the tank should be kept at 32-40 °C. Figure 36 shows how the flow

pattern looks like in an unbaffled tank and how four baffles will change the flow pattern.

35

Recommended dimensions:

Impeller, baffles and heating jacket should be installed in storage tanks to keep the concentration

constant. At least the impeller and baffles would keep the concentration even in the tank. Further

investigation needs to evaluate why there is a variation of the Vol concentration in the buckets. The

concentration should be checked after stand time in the line. (If stand-time has an impact; When a bucket

is being filled at the manual tapping station after a long stand-time, the first litres of the Vol should be

cleared out to reduce the variation).

5.2.2 Icing Sugar The recommendation is to eliminate the variation the graded sugar causes when the mixer is calling for

icing sugar to biscuit B. The suggestion is to redesign the control sequence for a sugar call. Biscuit B is

the only biscuit on its line that uses IHS for sugar, so a redesign would not cause an impact on other

biscuits on the same line. The recommendation is to not open the accumulation hopper valve when

Biscuit B’s mixer is calling for icing sugar, which results to only icing sugar enter each dough. A

decision tree for the recommended program sequence can be found in Appendix C.

5.2.3 Flour The flour system is controlled and there is not much change regarding quality parameters of the flour.

The temperature variation of the dough has no impact on the biscuit dimensions, but if the dough

temperature should ever start to vary more, it may give an effect on the biscuit width and may be caused

by the flour system. Due to the weight variation that occurs, suggestion is to install a weight transmitter

on the flour hopper above the mixer. This would be helpful for flour hopper on other production lines

as well. Width a weight transmitter it would be possible to detect problems with the valves on the line

before it turns in to a problem.

6 Project outcomes The icing sugar system was redesigned based on the recommendations given in the project progress

report. The change was implemented on the 2nd of November, 9:47 am. Data was retrieved to evaluate

the improvements generated by the process change. To evaluate whether the biscuit dimension variation

was lower after the changed was implemented the percentage of biscuits with a width or length outside

specifications were calculated. Seven data sets with biscuit dimensions used in section 4.2 Icing Sugar

Dt = HL

1/3 Dt = Hi = Di

1/10 Dt = Wb

Figure 37: Optimal ratio between dimensions of the tank, stirrer,

4 baffles and the liquid level. (Nilsson, 2015)

Figure 36: The figures to the right shows the flow profile with baffles

and the figures to the right shows the flow profile without baffles.

(Nilsson, 2015)

36

– Results and Discussion was used to compare with 6 sets of data retrieved the week after the system

change was implemented. The data sets represent dimensions for 25 doughs with an average value of

the width and length every minute. Time series of the dimensions were compared to see if there was an obvious improvement by changing

the sugar system. By studying time series and the overall line performance the week after, it seemed to

be less biscuit variation on the line by locking out the accumulation hopper. The dimensions seem to

stay more constant.

By analysing the data given in Table 18 and Table 19Table 20. The deviation and average dimension

values are quite similar. What is obvious is that the results vary more between the dates before the IHS

change compares to the dimensions the week after the IHS change. This improvement could be due to

the IHS change. On the other hand, it could be because of that the data after the IHS change is captured

the same week. Maybe other parameters did not vary as significant between these dates as between the

different dates before the IHS change.

The percentage of biscuits with dimensions outside specification was greater before the IHS change

compared to quite low after the IHS change, see Table 20. On the other hand, the percentage of biscuits

outside specification does not seem to correlate with the frequency of the graded sugar calls.

• Based on the results from the week after the IHS change, the sugar system change seems to

result in reduced dimension variation.

• The sugar particle size seems to have an impact on the biscuit dimension variation. Due to the

wide range of parameters that affect the biscuit dimension, it is impossible to reduce the

variation significant by a single change of one parameter. Icing sugar is not the only parameter

that has an impact.

Table 18: Average biscuit dimension deviations before the accumulation hopper was getting locked out.

Before accumulation hopper locked

out

Average deviation

(mm) Length

Standard deviation

(mm) Length

Average deviation

(mm) Width

Standard deviation

(mm) Width

Average deviation (mm) PL

Standard deviation (mm) PL

11-Sep -0.16 0.58 0.12 0.56 0.06 0.38

12-Sep -0.60 0.76 -0.10 0.61 0.25 0.33

8-Oct -0.15 0.69 0.92 0.47 0.89 0.48

9-Oct -0.18 1.21 1.56 0.38 0.56 0.29

10-Oct -0.36 0.56 0.71 0.28 0.67 0.36

18-Oct 0.02 0.85 0.73 0.42 0.63 0.26

19-Oct 0.75 0.69 0.40 0.52 0.21 0.27

Table 19: Average biscuit dimension deviations after the accumulation hopper was locked out.

After accumulation hopper locked

out

Average deviation

(mm) Length

Standard deviation

(mm) Length

Average deviation

(mm) Width

Standard deviation

(mm) Width

Average deviation (mm) PL

Standard deviation (mm) PL

6/11/2018 AM 0.53 0.80 -0.10 0.52 0.13 0.56

6/11/2018 PM 0.30 0.50 0.01 0.45 0.28 0.52

7/11/2018 AM 0.26 0.63 -0.22 0.34 0.29 0.23

7/11/2018 PM 0.25 0.56 -0.19 0.40 0.42 0.35

37

8/11/2018 AM -0.43 0.51 -0.17 0.39 0.27 0.35

8/11/2018 PM 0.00 0.51 0.24 0.41 0.37 0.51

9/11/2018 AM -0.19 0.72 0.06 0.37 0.40 0.32

Table 20: Percentage of all biscuits over a time-period of 25 doughs that has a length that deviates more than 2 mm from

specification. The percentage is given per production-row A-H for both length and width, the total shows the overall biscuit

dimension percentage outside specification.

LENGTH (Δ > +/- 2 mm) The percentage of biscuits in each row which biscuit width deviated more than 2 mm from set point

Aft

er

accu

mu

lati

on

ho

pp

er

lock

ed

ou

t

Row Line A Line B Line C Line D Line E Line F Line G Line H

6/11/2018 0.00% 0.00% 0.00% 0.00% 1.32% 0.00% 0.88% 0.00%

7/11/2018 AM 1.20% 0.00% 0.00% 0.27% 8.28% 0.00% 3.60% 0.00%

7/11/2018 PM 1.77% 0.00% 0.00% 0.00% 4.90% 0.54% 3.81% 0.27%

8/11/2018 AM 0.78% 1.83% 2.87% 3.91% 0.13% 0.00% 0.00% 0.00%

8/11/2018 PM 0.00% 0.98% 0.42% 0.14% 0.14% 0.00% 0.00% 0.00%

9/11/2018 AM 1.01% 3.36% 1.17% 4.70% 0.50% 0.00% 0.00% 0.00%

Be

fore

acc

um

ula

tio

n

ho

pp

er

lock

ed

ou

t

11/09/2018 4.79% 0.00% 0.14% 0.00% 17.32% 10.00% 10.56% 11.27%

12/09/2018 1.00% 0.00% 0.57% 2.73% 5.31% 2.15% 4.88% 3.73%

8/10/2018 1.35% 2.96% 2.43% 4.18% 9.57% 4.58% 4.85% 5.93%

9/10/2018 3.21% 9.24% 0.00% 0.13% 7.70% 4.24% 3.59% 6.29%

10/10/2018 4.39% 0.61% 8.48% 10.15% 0.15% 0.00% 0.61% 0.00%

18/10/2018 3.87% 5.52% 25.55% 25.55% 0.28% 0.83% 0.14% 0.00%

19/10/2018 0.00% 0.00% 3.25% 2.97% 0.00% 0.71% 0.00% 0.00%

WIDTH (Δ > +/- 2 mm) The percentage of biscuits in each row which biscuit width deviated more than 2 mm from set point.

Also, the total percentage of biscuits which had either a length or width dimension greater than 2 mm from set point.

TOTAL

Aft

er

accu

mu

lati

on

ho

pp

er

lock

ed

ou

t

Row Line A Line B Line C Line D Line E Line F Line G Line H

6/11/2018 0.00% 0.00% 0.00% 0.15% 0.00% 0.00% 0.00% 3.37% 0.71%

7/11/2018 AM 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 1.67%

7/11/2018 PM 0.00% 0.00% 0.00% 0.27% 0.00% 0.41% 0.00% 2.72% 1.84%

8/11/2018 AM 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.13% 1.21%

8/11/2018 PM 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 6.97% 1.08%

9/11/2018 AM 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 7.72% 2.31%

Be

fore

acc

um

ula

tio

n

ho

pp

er

lock

ed

ou

t

11/09/2018 0.00% 0.00% 0.00% 8.31% 0.00% 0.00% 0.00% 0.00% 7.80%

12/09/2018 0.14% 0.00% 0.00% 2.73% 0.00% 0.00% 0.00% 1.72% 3.12%

8/10/2018 4.44% 1.08% 0.00% 0.27% 0.00% 1.35% 0.13% 0.00% 5.38%

9/10/2018 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 4.30%

10/10/2018 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 3.05%

18/10/2018 0.69% 1.66% 0.00% 3.59% 1.38% 2.76% 2.49% 1.93% 9.53%

19/10/2018 0.00% 0.00% 0.00% 1.98% 0.00% 0.28% 0.00% 0.00% 1.15%

38

7 Professional Development Engineers Australia has a list of competencies that are expected by an engineer to be fulfilled before

graduation. Stage 1 competencies cover elements of competency within 3 main categories 1) knowledge

and skill base, 2) engineering application ability and 3) professional and personal attributes. (Engineers

Australia, 2017)

Monthly reflections have been done to analyse and evaluate my professional development based on the

EA competencies. The monthly reflections have made me realise what competencies I have gained at

the workplace and what more I need to practice, to be able to demonstrate all competencies at the point

of graduation and entry to engineering practice. Some key learnings that have been gained during the

placement so far are stated below. Five monthly reflection journals that have been handed in and can be

found in Appendix D.

7.1 Key learnings and challenges • Experience in working in an FMCG environment and understand how a factory system work.

• Understanding and investigation of a real process and its units.

• Identify the problem, its associated risks and opportunities, and to formulate a solution.

• Relate prior knowledge gained at University to identify a problem and tackle the problem.

• Conduct and lead smaller projects, increased sense of responsibility and ownership.

There have been several challenges through the placement. During the first couple of weeks, the

challenges were to navigate around the plant and trying to understand all the abbreviations used at

Arnott’s. Furthermore, I found it a challenging and frustrating to deal with the feeling that I could not

really show 100% of myself due to knowledge gaps and language limitations. By staying a step behind

the first couple of weeks I analysed and gained an understanding of the environment and the culture in

the factory, I finally found myself very comfortable after a couple of weeks at the workplace. My

experience is that people sometimes assume there is a lack of knowledge when language limitations

sometimes result in communication problems. I have developed my communication skills during the

placement and this challenge has made me dedicated to continuing to improve my language and

communication skills.

Another challenge was to decide what and how to investigate and analyse the ingredients, it was much

more of a challenge than I first expected. It was challenging due to that this was the first time I have

conducted a project by myself and that there was not an obvious right or wrong path to solve the

problems, instead, I had to redo or change the methods when weaknesses in the methods were found.

This was a challenge that has developed my professional engineering skills, I will most likely face the

same kind of challenges in my future career.

7.2 Development of EA Competencies 4-6 situations have been described in each monthly reflection journal. Furthermore, there been one

situation per journal that has been described more in-depth with the SEAL format. The Engineer

Australia (EA) competencies that have conducted for each situation are also given. A summary of the

number of competencies within the three EA categories is given in Figure 38 below.

The number of EA 3 competencies achieved before the first journal was very high and slightly less for

the second journal but clearly, EA 3 competencies were successfully achieved the first weeks at the

workplace. The EA 3 competencies cover professional and personal attributes. For example,

professional oral and written communication, professional use and management of information and

effective team membership and leadership. Many of these competencies were new for me during the

first weeks at the workplace, and the situation around these competencies clearly occurred during the

first weeks when I was new to working in an FMCG environment. The number of EA 3.6 competencies

is significantly higher than the other competencies, effective team membership and leadership, which is

obviously the competency that I have conducted and mostly developed my skills in through the

39

placement. The EA 2 competencies cover the ability of engineering application, which for example

include a fluent application of engineering techniques, resources, and synthesis and design processes.

These competencies are obviously something I could prove after a couple of weeks. It takes time to

understand and learn all the theory and processes when you are new to a workplace, furthermore, when

you get an new field of knowledge, as the food industry is for me, it takes time to establish the

engineering applications.

Figure 38: The number of EA competencies achieved for each reflective journal.

0

1

2

3

4

5

6

7

8

9

10

1.1 1.2 1.3 1.4 1.5 1.6 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 3.6

Element of compentency

EA Compentencies

Journal 1 Journal 2 Journal 3 Journal 4 Journal 5

40

8 References About Arnott's. (2018). Retrieved July 22, 2018, from Arnott's: https://www.arnotts.com.au/about-

arnotts/

Ammonium Bicarbonate. (n.d.). Retrieved from Merck:

https://www.sigmaaldrich.com/catalog/search?term=Ammonium+bicarbonate&interface=All

&N=0&mode=match%20partialmax&lang=en&region=AU&focus=product

Arnott's. (Retrieved: 10/08/2018). Style of Vol addition. Virginia.

Chevallier, S., Colonna, P., Bule´on, A., & Della Valle, G. (2000). Physicochemical Behaviors of

Sugars, Lipids, and Gluten in Short. Journal of Agricultural and Food Chemistry, 1322−1326.

Correlation Coefficient 'r' and The Linear Model. (2018). Retrieved from Nayland College -

Mathematics :

http://maths.nayland.school.nz/Year_13_Maths/3.9_Bivariate_data/7_Corr_Coefficient_r.html

Davidson, I. (2016). Biscuit Baking Technology 2nd Edition. Academic Press.

Engineers Australia. (2017). Stage 1 Competency standard for professional engineer. Australia:

Engineers Australia.

Kresta, J., MacGregor, J., & Marlin, T. ( 1991). Multivariate Statistical Monitoring of Process

Operating Performance. The Canadian J. Chem Eng, 35-47.

Ludger, O. F., & Arthur, A. T. (2007). Food Physics. Berlin, Heidelberg: Springer.

Manley, D. (2000). Technology of biscuits, crackers and cookies. Cambridge: Woodhead Publishing

Limited.

Manley, D. (2001). Biscuit, Cracker and Cookie Recipes for the Food Industry. Burlington: Elsevier

Science.

Ratner, B. (2009). The correlation coefficient: Its values range between +1/−1, or do they? Journal of

Targeting, Measurement and Analysis for Marketing, 139–142.

Rodgers, J. L., & Nicewander, A. W. ( 1988). Thirteen Ways to Look at the Correlation Coefficient.

The American Statistician, 59-66.

Sigma-Aldrich. (Retrieved: 10/08/2018). Product Information - Ammonium Bicarbonate. Saint Louise:

Sigma-Aldrich.

Sulfuric acid solution. (2018). Retrieved from Merck:

https://www.sigmaaldrich.com/catalog/product/saj/285940?lang=en&region=AU&gclid=EAI

aIQobChMIq-v2-9yE3QIVQnRgCh05XQc5EAAYASAAEgJ5t_D_BwE

Sutter, D. M. (2017, Crystal Growth and Design, 7 June 2017, Vol.17(6), pp.3048-3054). Solubility

and Growth Kinetics of Ammonium Bicarbonate in. Crystal Growth and Design, 3048-3054.

The University of Iowa. (n.d.). System Risk Analysis. Retrieved from IT Security & Policy Office:

https://itsecurity.uiowa.edu/resources/everyone/determining-risk-levels

i

Appendix A

Sugar Particle Size - IHS system

0%

10%

20%

30%

40%

50%

60%

26 27 28 30 31 32 33 34 35

Trolley Number

IHS 3/09/2018

<75

75-150

150-250

>250

0%

10%

20%

30%

40%

50%

60%

123 124 125 126 127 128

Trolley Number

IHS 5/09/2018

<75

75-150

150-250

>250

ii

Appendix B Current decision tree approach for an icing sugar call on the IHS system. LS = low-level sensor accumulation hopper

Sugar weight x% of called sugar weight

iii

Description of an icing sugar call approach for biscuit B

1. The mixer calls for icing sugar.

2. Blowers motor starts, and the accumulation hopper valve opens. The motor has a ‘soft start’

command for a couple of seconds.

3. Approximately, the weight in mixer’s sugar hopper rises quickly to 3 kg (most likely

leftovers in the system/ pipes).

4.

a) If the low-level sensor in the accumulation hopper is on: runs for 5 seconds, read

the level sensor a second time. If the level sensor is off the 2nd time: it runs for

additionally 10 seconds before step 5 (if the level sensor is still on: it runs for

additionally 5 seconds).

b) If the low-level sensor is off: Runs for 10 seconds before step 5.

5. Icing sugar hopper valve opens (pressure in the system is approximately 43-50 kPa).

6. When the weight is around 90% of the required weight, the power of the valve motor gets

lower, so less sugar enters the system.

7. When weight is 100% of the required weight, the motor to the accumulator hopper valve

and the icing sugar valve are turned off.

8. Blower runs for 30 seconds.

9. Blower turns off.

Unknown sugar weight gets accumulated in the accumulation hopper. The 2-3 kg of sugars that

enters the mixer hopper before sugar valves are opened are sugar that was not cleared out from the

system after the previous call. Furthermore, the high-level sensor in the accumulation hopper is not

always covered after a call. The level sensor gets sometimes covered several minutes after the call,

which may be a result of the dust that gets collected in the dust collector.

iv

Appendix C Decision tree approach display requested programming sequence for an icing sugar call on the IHS

system for Biscuit B. LS = low-level sensor accumulation hopper Sugar weight x% of called sugar weight

v

Appendix D Reflective Journal 1

3/08/2018

Learning events Description of Learning Event EA Stage 1

Competencies

1. Planning and

prioritizing

side projects

See analysis with SEAL format below. • 1.6

• 3.5

• 3.6

2. Introduced to

the site

leadership

team

During my first two weeks at the workplace I had a

meeting with every manager that are responsible for

different lines and departments within the Company.

These meetings gave me insight in the different

departments’ responsibilities. I also gained some advice

and shared knowledge from the meetings, which will be

very helpful in the future. Furthermore, these meetings

broke the barrier that can exist between the hirik within a

company, between an employee and the boss. These

meetings made me feel like I am “a person in the team”

more than just an intern at a workplace.

• 3.5

• 3.6

3. Present results

and work

through oral

discussion and

report writing

I have present the tasks I finished through both oral

discussion and by writing shorter reports. Skills that I

learnt during my previous years at the University. But at

the workplace it feels different. I do not do the report to

achieve a certain grade in a course. I do it for the

company and the person who asked me for my help, the

work feels more important. I have developed my

professional communication skills through discussion

and feedback.

• 3.2

• 3.6

4. Data entry and

analysis

I did some data entry to help one person in my team. The

purpose of the data entry was to see if we could see any

pattern, trend or behavior through analysis of the data.

At University I have done some statistical analysis in

MATLAB, especially last semester in CHEE7111. I

pointed out my interest in how he was going to analyse

the data. If I had not shown my interest I would not have

been introduced to the software Minitab. My team

member showed me how Minitab could be used to

analyse data and do forecasting. MATLAB is not a

common software at the workplace, it is therefore

necessary to be aware of other software programs that

can be used instead of MATLAB.

• 1.2

5. Learnt about

the biscuit

manufacturing

process

I learnt the basic theory behind biscuit manufacturing

processes through meetings with operators on different

production lines. To get a deeper understanding I started

to read a book that explains the theory behind biscuit

making. Understanding of the science behind biscuit

making and the ingredients impacts on the dough and

final product will help me in my future project work at

Arnott’s.

• 1.1

• 1.3

• 3.4

Analysis of event with SEAL format

vi

Situation

What was the new experience or challenge you faced and what happened to you?

The first week was full of meetings with different people at the company and there were a lot of things

to learn. I really wanted to start work on something, so I could feel that I did something useful and to

not only be the person that everyone should show around and babysit. I was therefore very excited

when I started to get requests from employees at Arnott’s who asked for my help with different

projects. I said happily yes. Shortly after other employees had heard that I was available to help with

different projects, so I started to get a lot of requests. Which resulted to confusion and I did not know

what to start with and what to priorities.

Effect

What impact did it have on you and what were the consequences of this impact?

I got a small feeling of panic, I did not know how to priorities and if I should say yes to every request I

received. I was very confused and did not know how to really handle the situation. I wanted to do a

good job and make a good impression as an intern. And I wanted to do everything since it was hard to

decide what to say no to, since every task would gain me experience and I would also develop

relations with different people in different departments at the plant.

Action

What actions did you take to deal with the challenge you faced and why did you do those things?

When I got the feeling of panic I told the person who asked me for help that I had got a lot of requests

that day so I needed to talk with my supervisor since I was not sure what actions I should take when I

got so many requests. I talked with Christopher about the situation. I asked if I should ta asked him for

advice about how to decide which project to priorities and when to say yes and no.

Learning

What have you learnt from the experience and how will you apply this in the future?

At University prioritizing and planning was a daily task, but it is mainly to the favor of myself. I learnt

that at the workplace it is more complex, I could have realized it would be more complex but from this

situation I truly gained experience in how prioritizing and communication are important when

planning and accepting projects. From the conversation with Christopher I learnt that I should

priorities my own projects in the future. And that I can help other employees when I have time, since it

contributes to a wider range of knowledge and experience. In the future I will focus on my own

projects and accept requests when I have time. I said yes to all the requests I got, by conversation with

the persons involved I could plan which task to prioritize. By doing side projects I gain knowledge

about other departments work and get to know more employees at Arnott’s which contribute to my

professional development and will benefit me in the future both with my work at Arnott’s but also in

my future career as an engineer.

Reflective Journal 2 3/09/2018

Learning events Description of Learning Event EA Stage 1

Competencies

See description of EA Stage 1 competencies in table on page 3.

1. Writing a

project

proposal report

See analysis with SEAL format below. • 1.1

• 1.4

• 1.6

• 3.1

• 3.2

2. Read a P&ID I have worked with P&IDs before, both in courses at

Lund University and in courses at UQ. But I have never

been able to read a P&ID and identify all the units for a

real process. I knew how to read a P&ID but I have

never had the opportunity to study a process with the

P&ID. This was very interesting, and I learned a lot by

studying the different P&IDs for the ingredient systems

in the factory. It was very interesting to see how a

• 1.3

• 2.2

vii

control and sensor can look like. I could identify both

temperature, pressure and flow sensors and it was very

interesting to see the different valves.

3. Familiarization

with a process

and its units

When I first got my project my project supervisor told

me to first familiarise with the different ingredients. She

told me to learn as much as possible, to become the

expert on the four ingredients. It was challenging to

know how to learn everything. Obviously, literature is

not the only source. To understand the processes, I

talked to the operators who are responsible for the

different ingredients. They showed me around and told

me about the process, what changes that have been made

and what issues they knew. It was an effective and very

good way to get much knowledge in a short period of

time. Furthermore, I understand how important it is to

have a good relation and communication with the

operators. I will work in their area this semester and by

having a good relationship with them, we can share both

help and knowledge.

• 3.3

4. Conduct and

lead smaller

projects

In the previous reflective journal, I mentioned that I have

been given a few side projects. These projects have

given me a lot of knowledge and experience already, and

they keep me constantly busy. Furthermore, they give

me experience in leading a trail as well. Since I have

attended a trial for a project a couple of times I got to

lead the last trial. This was a great experience since I had

to organize and plan before the trail. And during the trial

delegate instructions to the operators who worked in the

area. It was an easy trail and the operators involved have

been a part of these trails before. But it felt good to be

given responsibility which proof that my supervisor trust

and believe that I will perform a good job.

• 2.4

• 3.1

• 3.6

Analysis of event with SEAL format Situation

What was the new experience or challenge you faced and what happened to you?

Writing a report is not a new experience, but for this report, the circumstances made it to a new

experience and challenge. I felt comfortable when I first started to write the project proposal report.

Search for relevant literature and plan the structure of a report are tasks that I have done for every written

report at University, and the two given example reports were very useful as well. I started with the report

writing in week 3 even though my project was not defined yet. I started with a general project plan,

report structure and I started with the literature review. I knew that my project would be about

ingredients impact on the dough so the literature review was possible to start with.

I finally got my project properly described after a few weeks. There is a quite wide project scope, four

ingredients impact on the dough. I got the project quite close to the deadline for the project proposal

report. I understood that I would not be able to plan the methodology for all ingredients before the

deadline. Furthermore, I have never done something similar before. I have done lab work and lab reports

by myself but never something this big, not a project.

Effect

What impact did it have on you and what were the consequences of this impact?

I felt that it was impossible to get everything ready before the hand-in of the project proposal report.

Which made me very stressed. The project felt almost overwhelming. I did not know where to start,

what to focus on and how I would be able to present this project in a report.

viii

Action

What actions did you take to deal with the challenge you faced and why did you do those things?

I worked hard to understand as much as possible about the four ingredients. But still, it felt

overwhelming since I was unsure of how I would be able to present the methodology for the project in

the project proposal report. I finally did my first data collection the same week as the report was due. It

required a lot of time and I had to stay late at work that week to be able to finish my lab work and to

write the last parts of the project proposal report. It was hard to write the report since the methodology

was not finished. Since the project scope is quite wide it was hard to know what to write about and what

to focus on, since I could include so much more. Furthermore, I excluded a few pages from the report.

Which included information that included confidential information about the company. I was very

stressed the days before the hand-in, and I did not want to go to my supervisor one day before hand-in

to ask if he could read through the report and see whether the information was confidential or not. I

knew that I probably would not have time to change the report. I excluded everything I thought may be

confidential. After the hand-in of the report I talked to my workplace supervisor and my project

supervisor, I asked them for feedback on the report and told them that I appreciate if they want to give

me feedback which I can include for next deadline, the project progress report.

Learning

What have you learnt from the experience and how will you apply this in the future?

I will finish the project progress report at least a week before. Finishing a report a few days before the

deadline is a wish that is common among students, but often stays as a wish. But to make sure I keep

my word, I also told my supervisor that I will be done with the report early for next hand in, since it may

contain more confidential information due to results from tests. By telling her that she will get it one

week early, it works as a guarantee for myself that I will not be to be stressed the last days before hand-

in. This situation gave me experience in how it is to get a wide project which is not perfect defined as

in University. Furthermore, to handle the stress and solve the problem mainly by myself, something I

have almost never done before. I have solved greater problems and have written bigger reports in groups

but never done a project of this size by myself.

EA Stage 1 Competencies

EA 1.1 Comprehensive, theory based understanding of the underpinning natural and

physical sciences and the engineering fundamentals applicable to the

engineering discipline

EA 1.3 In-depth understanding of specialist bodies of knowledge within the engineering

discipline

EA 1.4 Discernment of knowledge development and research directions within the

engineering discipline

EA 1.6 Understanding of the scope, principles, norms, accountabilities and bounds of

sustainable engineering practice in the specific discipline

EA 2.2 Fluent application of engineering techniques, tools and resources

EA 2.4 Application of systematic approaches to the conducts and management of

engineering projects

EA 3.1 Ethical conduct and professional accountability

EA 3.2 Effective oral and written communication in professional and lay domains

ix

EA 3.3 Creative, innovative and proactive demeanor

EA 3.6 Effective team membership and team leadership

Reflective Journal 3 3/10/2018

Learning events Description of Learning Event EA Stage 1

Competencies

See description of EA Stage 1 competencies in table on page 3.

1. Conduct a project

by myself

In University we do most of the projects together in

groups and the tasks and expectations are often

clear described. My placement project scope is

quite wide and I have had to take many decisions

and actions on my own. Which been very

challenging. It is easier to take decisions by a

discussion with a group member contrary to a

discussion with yourself. But I feel that I have

learned so much these last couple of weeks at my

workplace working on my project. More than I

possibly could lean at the University in a course. I

already feel much more comfortable in manage

projects and tasks by myself, compared to when I

first started at the workplace. Even though I

probably will perform projects in groups in the

future and I will always do projects and tasks by my

own. I really feel that I so far have developed these

skills a lot and I am hoping to be even more ready

for future workplaces when this placement is

finished.

• 2.1

• 3.5

2. Data results are not

always as expected

See analysis with SEAL format below. • 1.4

• 2.2

3. Familiarization

with the software

Historian, and how

to use it together

with collected data

There is a software at the workplace which stores

control data from the plant. When I first started to

use the software it was a little bit tricky. I got a

brief introduction several weeks ago, during my

second week at the workplace. So I had forgotten a

lot when I opened the program last week. Anyway,

by testing around in the program and by using

google when I got stuck, I started to figure out how

to use it. When you start to learn something, it gets

really interesting. I have learned how to download

the data to excel and how to add my testing results

from laboratory tests to match the control data time

series. I have started to build data files that I soon

will start analyse in MATLAB by using the skills I

learned in CHEE7111 last semester.

• 1.2

x

4. Plan, lead and

present results from

a side project

I have been involved in a couple of side projects. In

one of the projects, I have been responsible for the

data collection. I have performed three trials so far

and I had to plan each trial in detail. I had three

people helping me. I had to plan all the details like

when, how, what instruments we needed and how

to record the results. Furthermore, evaluate possible

risks with the trial. It felt good to be responsible for

the trial, it proves that they trust my ability to plan

and organize a trial. The trials have all been

successful and it was a good way to practice

leadership and teamwork skills at the workplace.

• 1.6

• 2.4

• 3.3

• 3.6

5. Creative thinking I have been collecting samples from the sugar

accumulation hopper. To be able to do this I had to

figure out a way to reach the sugar through a pipe

on the side of the hopper. Since no one had done it

before I had to find an instrument which made the

sample collection possible. I went to the

maintenance guys and asked them for help. I

explained my ideas of building a long scope or

spoon which would make it possible to collect the

sugar from the bottom of the hopper. I developed an

idea and prototype with one of the maintenance

guys who built it for me. The sample collection was

successful, and I got credit for being creative on

how to make it possible.

• 1.5

• 2.2

• 2.3

Analysis of event with SEAL format Situation

What was the new experience or challenge you faced and what happened to you?

By doing experiments in the lab at University I do know that the results are not often exactly as expected.

This happened for me during experiments for my project work. My results were varying a lot, much

more than I expected.

Effect

What impact did it have on you and what were the consequences of this impact?

I was first sure that I did something wrong when I performed the tests. I felt disappointed that the way I

have chosen to perform the test probably was not right or a good way of doing it. In University you

perform tests in the laboratory where the tutor knows how the results should look like. Here at the

workplace, I had an idea of how I thought the results will look like. So, when my results did not match

my expectations I had no tutor who could tell me how the results should look like. I felt confused, I

asked myself questions like; am I doing mistakes? Or are the results just not as expected? Why do the

results look like this?

Action

What actions did you take to deal with the challenge you faced and why did you do those things?

I evaluated the way of performing my measurements and found that I performed the tests the right way,

according to the procedure the people in the lab have shown me. So, I talked to the people in the lab and

I showed them my results. Even the lab person could not really tell why the results looked like they did.

At least I knew that I did not do something wrong.

I talked to my project supervisor and we evaluated the methods of sample taking. We decided that more

data needs to be collected, and with more data, we could hopefully be able to see a trend and maybe find

out why the concentration was varying.

Learning

What have you learnt from the experience and how will you apply this in the future?

xi

Through this experience, I learned that also in the workplace the results may not always appear as

expected. And in the workplace, you often do not have a person that can tell why the results are

different or wrong like the lab assistant does at University. So, through this situation, I gained

experience in handling unexpected situations and how to evaluate the methods and results on my own.

Furthermore, I learned from my project supervisor that in cases like this I need to collect more data to

be able to make conclusions and hopefully I will see a trend and/ or find the reason for the variation.

EA Stage 1 Competencies

EA 1.2 Conceptual understanding of the mathematics, numerical analysis, statistics, and

computer and information sciences which underpin the engineering discipline.

EA 1.4 Discernment of knowledge development and research directions within the

engineering discipline

EA 1.5 Knowledge of engineering design practice and contextual factors impacting the

engineering discipline.

EA 1.6 Understanding of the scope, principles, norms, accountabilities and bounds of

sustainable engineering practice in the specific discipline

EA 2.1 Application of established engineering methods to complex engineering

problem solving

EA 2.2 Fluent application of engineering techniques, tools and resources

EA 2.3 Application of systematic engineering synthesis and design processes.

EA 2.4 Application of systematic approaches to the conducts and management of

engineering projects

EA 3.3 Creative, innovative and proactive demeanor

EA 3.5 Orderly management of self, and professional conduct.

EA 3.6 Effective team membership and team leadership

Reflective Journal 4 3/11/2018

Learning events Description of Learning Event EA Stage 1

Competencies

See description of EA Stage 1 competencies in the table on page 3.

1. Use old

knowledge to

give

recommendations

I have started to develop some

recommendations based on my interim project

results. I decided to give recommendations on

how the mixing tank could be redesigned and

also how the sugar control sequence could be

redesigned and improved. I have been doing

several courses in University about tank

design and also courses in how to write a

decision tree diagram and design a control

sequence.

So I went back to lecture presentations to

refresh my knowledge in these areas, which

was an easier way to gain knowledge compare

to searching in new literature. I have been

able to use my engineering skills at the

workplace, moreover, I have understood the

• 1.3

• 2.4

• 3.3

xii

value in keeping coursework, literature and

lecture presentations for future situations

where course literature can help me solve a

problem at University.

1. Excel course I had very limited knowledge in excel before I

started my placement at Arnott's since I have

barely used excel at UQ or Lund University.

But I have learnt heaps during these months at

Arnott's. Excel is such a great and easy

software when you understand how to use it,

and I realise how useful it is to get skilled in

excel.

All team leaders and management people had

the opportunity to conduct an excel course

one day, and I got the opportunity to join one

of the sessions. I decided to join an

intermediate 3-hour course that morning. I

learnt a lot during that course and when they

asked me if I wanted to stay for the 3-hour

advance course that afternoon I said happily

yes. I understand how useful excel is, it is

such a widely used software and I understand

the importance of improving my skills in

excel. I wish we had more excel education in

our engineering degree.

• 1.2

• 3.2

2. Data presentation See SEAL format • 1.2

3. Project presentation I attended a project presentation meeting.

Every second week there is a project

presentation meeting at Arnott's. A couple of

projects perform a presentation for the

managers and other important people in the

factory to present the current status of the

project. This is an opportunity for everyone to

understand the project and what the outcome

may be, furthermore it is of value for the

project team to get feedback and questions.

I will present my project at one of these

upcoming meetings. I was therefore invited by

my supervisor to join for one meeting, so I

could feel more prepared for my own

presentation. Now I know what is going to be

expected by me when I do the presentation.

So I am very excited to get feedback on my

project and also nervous. I enjoy attending

meetings like this, I get the opportunity to see

what kind of projects that are going on at

Arnott's. Furthermore, it makes me prepared

for future project presentations in my career.

• 3.2

• 3.5

4. Redesign of a control

sequence

As I mentioned in learning event 1 I have

redesigned a control sequence for the sugar

system in the factory. This is something I

have done before at the University. But it was

really interesting to see how this knowledge

• 2.2

• 2.3

xiii

can be applied at the workplace. Furthermore,

I got to use my engineering skills to actually

define and solve a problem. I watched the

electrician when he redesigned the code that

we tested on the system this morning. I am so

excited to evaluate the results from the trial.

Analysis of event with SEAL format Situation

What was the new experience or challenge you faced and what happened to you?

I started to investigate the flour system at Arnott's to see if any quality parameter or process parameter

for the flour that may result in biscuit dimension variation for the two biscuits I am analysing. Prior to

flour, I have been investigating baking soda and the sugar system. The key parameters (that may cause

biscuit dimension variation) for the baking soda process and the sugar system were obvious when I

investigated the systems. Contrary to the flour system, where quality parameters and the system is highly

controlled, it was very hard to decide which parameters to investigate. According to literature, flour has

a huge impact since the ratio of flour to other ingredients in the recipe is quite big. But due to issues

caused by the flour several years ago, the flour system is today very controlled and quality parameters

are almost never outside specifications.

What impact did it have on you and what were the consequences of this impact?

I felt a little bit lost, I got the feeling that whatever parameter I could choose to investigate, I would not

find the reason for the biscuit dimension variation in the flour system.

What actions did you take to deal with the challenge you faced and why did you do those things?

I went out to the operator to go through the process a second time, I asked him if he could have any

ideas of something in the system that may cause trouble on the lines. But no, he did not. I thereafter

asked a man that has been working for Arnott's for ages, who are known to have a lot of knowledge and

have investigated the ingredients systems during many years. Also, he told me that I will probably not

find the flour to be the reason for the biscuit dimension variation at Arnott's since the process is very

controlled. So I sat down, trying to figure out what to do. I then realised that if flour does not have an

impact on the biscuit dimensions I can instead of proving flour guilty, try to prove that flour doesn't

have an impact on the dimension variation. Or if I would see an impact, I need to investigate the

ingredient further.

What have you learnt from the experience and how will you apply this in the future?

This was a good reminder that a project and an investigation do not always have to find a solution and

a root cause of a problem. Sometimes it can be performed just to prove that something is not the root

cause. I always want to deliver good results, and in the workplace, almost all project goals are to

finding improvements, either economical or efficiency improvements. So it feels wrong to investigate

something you do not think will result in any improvements. This situation learnt me that I do not have

to find possible improvements in every project, sometimes it is necessary to prove that something is

nor the root cause. And who knows, maybe I am wrong. The flour may have an impact even if no one

believe so.

EA Stage 1 Competencies

EA 1.2 Conceptual understanding of the mathematics, numerical analysis, statistics, and

computer and information sciences which underpin the engineering discipline.

EA 1.3 In-depth understanding of specialist bodies of knowledge within the engineering

discipline.

EA 2.2 Fluent application of engineering techniques, tools and resources

EA 2.3 Application of systematic engineering synthesis and design processes.

xiv

EA 2.4 Application of systematic approaches to the conducts and management of

engineering projects.

EA 3.2 Effective oral and written communication in professional and lay domains.

EA 3.3 Creative, innovative and proactive demeanour

EA 3.5 Orderly management of self, and professional conduct.

Reflective Journal 5 3/12/2018

Learning events Description of Learning Event EA Stage 1

Competencies

See description of EA Stage 1 competencies in the table on page 3.

6. RDO (Rostered day

off) Session –

Salaried staff

meeting

An RDO is rostered once a month on a Friday.

Salaried staff members have either meetings or

necessary training. Twice a year the RDO is a full

day meeting for all the salaried staff members where

the business current state is presented, a summary of

improvements, KPI update, big projects are

presented, and staff members are given rewards for

doing a really good job. I got the opportunity to

attend this meeting. It was interesting to understand

more about the big projects that other employees in

the Company have been working on. They shared

their learnings, failures and successes. Furthermore,

it was interesting to understand how the business is

performing and what information that is of interest

to share with all the salaried staff members.

• 1.3

• 3.1

• 3.2

• 3.6

7. Sales presentation I attended a presentation performed by Arnott’s

international sales manager. He described how the

sales department are working, he explained how

much the market can vary and how they forecast the

market and the demand. Also, how Arnott’s is

performing compared to other companies in the

biscuits, sweets and savoury sector. It was

interesting to listen to his presentation, partly

because I learned a lot about how the sales

department works and I also because I found him

very inspiring, really interesting to listen to and he

was just such a good speaker.

• 1.2

• 3.1

• 3.4

8. Being a part of the

community at

Arnott’s

See SEAL format • 3.1

• 3.2

• 3.3

• 3.6

9. Helped the quality

department with a

project

I helped one of my colleges and friends at work with

a biscuit shelf life project. We were analysing the

shelf life for different biscuits. She explained the

project in detail, why and how she is doing it. She

asked me to help her, so we could perform the trial

together and discuss the results. It was very

interesting to discuss the results, both because I

• 1.1

• 2.1

• 2.2

• 2.4

• 3.6

xv

learnt a lot on how they are working in the quality

departments and because we have different

experiences and knowledge which was a very good

combination when discussing the results. She also

gave me instructions on how to do a sensory test of

the biscuits, which was very interesting to learn. Her

background is food science at UQ, so she made

interested to select a food science course as an

elective course next semester.

Analysis of event with SEAL format Situation

What was the new experience or challenge you faced and what happened to you?

When I started to work at Arnott’s in July I did not like Brisbane as much as I do today. I felt quite

lonely since almost all my friends had left the country, the friends I got during my first semester at UQ

were international students which were in Australia for only one semester. Furthermore, I did not like

my accommodation, the people I lived with were very unfriendly. So, I felt that it was very challenging

when I started at Arnott’s, I felt lonely and I almost wanted to go back home to Sweden.

What impact did it have on you and what were the consequences of this impact?

I did not feel very well, and I was questioning my decision of staying in Australia. I am a person that

does not like failure, so I was questioning myself if I stayed in Australia only because I would see it as

a failure to not finish my two years exchange. “What doesn’t kill you, makes you stronger”, I was

questioning whether it was worth fighting for or not. I found myself not acting and behaving as usual, it

was frustrating. I wanted the people at Arnott’s to see who I really am.

What actions did you take to deal with the challenge you faced and why did you do those things?

I smiled, I gave everyone a smile and a hello. I talked a lot with the operators and other salaried staff

members. I was longing for more deep relations with people. So, I started to ask people more personal

questions, not only the standard question “how are you?”. Sometimes I wrote up the name of the person

together with the story, just to make sure I remembered and could ask him or her some following

questions the next time we had a chat. I started to feel that some people really liked and cared about me

as well. It is hard to explain how good it felt, feeling lonely on the other side of the world, is just so

lonely. After 5 months at Arnott’s, I do really feel that I am a part of the work community, the people

here almost feel like a second family. I have got many friends with different positions and different ages

in the workplace, and truly love working here at Arnott’s.

What have you learnt from the experience and how will you apply this in the future?

I have learnt how important it is to have a good working environment. When the environment

is positive, and people are friends with their work colleagues, it is easier and fun to go to work,

people work harder and with passion. People spend so much time at work, the working culture

is therefore so important. Furthermore, I have learnt a lot about myself through this experience.

I went through a hard part of my life and the outcome is just so much better than I expected. I

feel that I have developed and improved my social skills, something I will apply both in my

future career but also just in life in general. I will always try to contribute to a good working

environment. It is important to be surrounded by good people, people that care about you. It

will be hard to leave Arnott’s in three weeks, they have been so good to me.

EA Stage 1 Competencies

EA 1.1 Comprehensive, theory based understanding of the underpinning natural and

physical sciences and the engineering fundamentals applicable to the

engineering discipline

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EA 1.1 Comprehensive, theory based understanding of the underpinning natural and

physical sciences and the engineering fundamentals applicable to the

engineering discipline

EA 1.2 Conceptual understanding of the mathematics, numerical analysis, statistics, and

computer and information sciences which underpin the engineering discipline.

EA 1.3 In-depth understanding of specialist bodies of knowledge within the engineering

discipline.

EA 2.1 Application of established engineering methods to complex engineering

problem solving

EA 2.2 Fluent application of engineering techniques, tools and resources

EA 2.4 Application of systematic approaches to the conducts and management of

engineering projects.

EA 3.1 Ethical conduct and professional accountability

EA 3.2 Effective oral and written communication in professional and lay domains.

EA 3.3 Creative, innovative and proactive demeanour

EA 3.4 Professional use and management of information.

EA 3.6 Effective team membership and team leadership