Optimization of Citric Acid Production Using Ann

8/13/2019 Optimization of Citric Acid Production Using Ann

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BACKGROUND

1. THE IMPORTANCES OF CITRICACID PRODUCTION

Low toxic acidulant

Great demand Growing at the rate

35 %

In 2004:

1. Beverages 50%

2. Food 1520%

3. Soaps and detergents 1517%

4. Pharmaceutical / cosmetics 79%

5. Industrial 68%

Worldwide citric acid production is

around 1.4 million tones per year.

At current prices the market is worth

about $1.5 billion.

2. THE IMPORTANCE OF USINGSOLID STATE FERMENTATION

SSF) Used with agro-industrial residues

(E.g.: EFB) reduce

environmental problem regarding

disposal of solid waste

Lower energy requirement

Produce less wastewater

Use low volume equipment which

is lower in cost but can effectively

produce high concentrated

product

INTRODUCTION


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Fermentation process is always complex.

Many factors influence Citric Acid production

Must apply optimization to maximize the yield and profit

Common method used RSM

Lack usage of ANN

Comparisons between RSM and ANN

No available research used ANN to optimize Citric Acid

production by solid state fermentation

PROBLEMSTATEMENT

In this study.

ARTIFICIALNEURAL

NETWORK

1. Optimize medium composition

2. Optimizeprocessconditions

3. Predict the outputyield of citric acid

production

5. Compare

ANNs resultswith RSM

4. Validate the ANNmodel by running

experiment


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LITERATURE

REVIEWCITRIC ACID PRODUCTION

1. The cultures ofAspergillus nigerare fed on a

sucrose or glucose-containing medium to

produce citric acid.

2. In term of production type, the use of

submerged fermentation is still dominating.

3. But now, the solid-state fermentation is

creating new possibilities for producers.

4. The use of agro-industrial residues such as

EFB as support in solid-state fermentation is

economically important and minimizes

environmental problems.Rotary drum SSF

Oil palm empty fruit bunch fiberis a lignocellulosic waste from

palm oil mills.1. low cost,2. renewable and3. widespread sources of

sugars.


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OPTIMIZATION

To optimize the process parameters maximize productincrease

profit

Several factors influence fermentation process:

1. Medium compositions (sucrose, trace elements, simulator)

2. pH

3. Temperature

4. Agitation

5. Aeration

6. Moisture content - SSF

Methods used for optimization:

1. RSM

2. ANN

3. Others (Genetic algorithm, CCD, Factorial Design)


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WHAT IS RSM ?

1. Collection of mathematical and statistical techniques useful for the

modeling and analysis of problems in which a response of interest is

influenced by several variables and the objective is to optimize this

response.

2. Normally use quadratic relationship.

3. A first-order model with two independent variables can be expressed as:

y = 0+

1x1 +

2x2 + e

4. The approximating function with two variables is called a second-order

model: y = 0+

1x1+

2x2+

11x11

2+ 22x22

2 + 12x12+ e

5. Rapid and efficiently used with small amount of data,

6. The primary limitation of RSM occurs when the approximation offered by

the quadratic function is inadequate.


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WHAT IS ANN ?

Computational modeling system based

on the neural structure of the brain.

Consist of three layers:1. Input layer(s)2. Hidden layer(s)3. Output layer(s)

Resembles human brain in two respects:1. Learning from examples2. Stores knowledge

Major Components:1. Weighing factors2. Summation function3. Transfer function4. Scaling and limiting5. Output function6. Error function and back propagated value

7. Learning function

NEURONS

Output

LayerHiddenLayer

InputLayer

NEURAL NETWORKS

Dendrites

Cell Body

Axon

Applications in Engineering Field:1. Complex and non-linear

problems2. Prediction3. Classification4. Data Association5. Data conceptualization

6. Data filtering


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ProcessingElement

MajorComponents:1. Weighing

factors2. Summation

function3. Transfer

function4. Scaling and

limiting5. Output

function6. Error

function andbackpropagatedvalue

7. Learningfunction


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ARTIFICIALNEURAL

NETWORK

1. TYPES OFNEURAL NETWORKS

Single Perceptron

Multilayer Perceptron(MLP)

3. TYPES OF LEARNING

SUPERVISED LEARNING:with teacher

Provide correct output for

every inputpattern bydetermine and adjusting theweight to produce answer asclose as possible to known

correct answer .

UNSUPERVISEDLEARNING:

without teacherDoes not require a correct

answer associated with eachinput pattern in the training

data set. It explores theunderlying structure in the

data and organize categoriesbased on this data

HYBRID LEARNING:Combine supervised and

unsupervised learning

2. NEURAL NETWORKTOPOLOGIES

1. FeedForward Neuralnetworks

2. Recurrent neuralnetwork

4. FEED FORWARDBACKPROPAGATION NN

Compare

Input Output

Target

Adjust Weight

Neural Network,including connections

(weights) betweenneurons


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RESEARCH STUDIES

Karnik et al. ,2007A comparative study of the ANN andRSM modeling approaches forpredicting burr size in drilling.

Minimum absolute percentage errorfor ANN prediction is within range1% - 0.14% lower than RSM which is

12% - 4.8%.

Desai et al. ,2008Comparison of ANN and RSM in fermentationmedia optimization: Case study offermentative production of scleroglucan

Average percentage error of ANN is 6.5lower than RSM which is 20

Correlation coefficient of validation data for

ANN is 0.98 higher than RSM which is 0.89

Kandimalla et al. 1999Optimization of a vehicle mixture forthe transdermal delivery of

melatonin using ANN and RSM

ANN can easily handle more than 4input variables but for RSM, a largeno. of input variables lead to apolynomial with many coefficientthat involves tedious computation

Bagci and Isik, 2006Investigation of surface roughness in turningundirectional GFRP composites by using RSM

and ANN

It was found that the maximum test errorswere 6.30% and 6.36% by comparingroughness (Ra) values predicted from ANNmodel with those predicted RSM.


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PART 1: SOFTWARE APPLICATION

Materials and equipments

Building ANN for optimization

Sensitivity Analysis

MATERIALS AND EQUIPMENTS:1. Software = MATLAB Version 2008a

MATLAB:1. Command-line functions in M-file2. Toolboxes : nntool, nftool, nntraintool, nprtool

METHODOLOGY..


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nntool nftool

nprtool nctool

nntraintool


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1. Data:


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Run

number

Sucrose Mineral Solution Inoculum Citric acid yield (g/kg-dry EFB)

a b c

Experimental Predicted (RSM)(% w/w) (% w/w) (% w/w)

1 8 12 20 259.23 258.1

2 6 2 16 267.47 268.15

3 4 12 20 256.52 242.52

4 6 8 16 334.68 332.81

5 6 8 16 334.23 332.81

6 4 4 20 208.69 213.22

7 4 4 12 236.65 231.32

8 6 8 16 332.44 332.819 8 4 12 259.22 266.75

10 8 4 20 260.65 247.19

11 4 12 12 250.02 257.02

12 6 8 16 333.88 332.81

13 6 8 16 333.08 332.81

14 6 14 16 292.42 300.18

15 6 8 16 334.14 332.81

16 3 8 16 247.46 249.8

17 6 8 10 257.79 256.1218 8 12 12 285.06 274.06

19 9 8 16 278.9 288.06

20 6 8 22 217.41 230.58

a : 1% (w/w) = 33.3 g/kg-EFB

b: 1% (v/w) = Zn, 3; Cu, 3.3; Mn, 13.3 and Mg, 166.7 mg/kg-EFB

c: 1% (v/w) = 6.7 x 1010spores/kg-EFB

Table 2: Experimental design data using CCD with the experimental and predicted value

(using RSM) of citric acid production for medum compositions optimization.


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Run Initial pH, A1Moisture content,

B1Incubation temperature, C1

Citric acid production

(g/kg-EFB)

Predicted Experimental

1 4 (-1) 78 (+1) 28 (-1) 229.68 241.30

2 6.5 (0) 70 (0) 32 (0) 368.16 368.61

3 6.5 (0) 58 (-2) 32 (0) 195.36 190.26

4 6.5 (0) 70 (0) 32 (0) 368.16 367.42

5 6.5 (0) 70 (0) 32 (0) 368.16 368.40

6 4 (-1) 78 (+1) 36 (+1) 256.88 241.24

7 4 (-1) 62 (-1) 36 (+1) 233.46 240.86

8 3 (-2) 70 (0) 32 (0) 315.96 316.42

9 6.5 (0) 70 (0) 38 (+2) 263.75 274.33

10 6.5 (0) 70 (0) 32 (0) 368.16 368.14

11 6.5 (0) 70 (0) 26 (-2) 220.80 210.05

12 6.5 (0) 82 (+2) 32 (0) 233.97 238.90

13 6.5 (0) 70 (0) 32 (0) 368.16 368.81

14 4 (-1) 62 (-1) 28 (-1) 161.96 158.10

15 10 (+2) 70 (0) 32 (0) 285.19 284.53

16 9 (+1) 78 (+1) 36 (+1) 194.35 198.38

17 9 (+1) 62 (-1) 28 (-1) 180.53 196.3218 6.5 (0) 70 (0) 32 (0) 368.16 367.91

Table 3: Experimental design using CCD with the experimental and predicted value

(using RSM) of citric acid production for process conditions optimization


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RunAeration,

A2(L/min)

Agitation,B2

(times/day)

Citric acid (g/kg-EFB)

Experimental Predicted

1 4 (-1) 1 (-1) 130.40 130.86

2 12 (+1) 1 (-1) 239.42 235.61

3 4 (-1) 5 (+1) 198.94 204.31

4 12 (+1) 5 (+1) 131.91 133.00

5 4 (-1) 3 (0) 268.36 262.53

6 12 (+1) 3 (0) 276.54 279.25

7 8 (0) 1 (-1) 234.14 237.49

8 8 (0) 5 (+1) 229.37 222.91

9 8 (0) 3 (0) 323.81 325.14

10 8 (0) 3 (0) 324.35 325.14

11 8 (0) 3 (0) 324.16 325.14

Table 4: Experimental design using CCD with the experimental and predicted value

(using RSM) of citric acid production for aeration and agitation optimization.


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Building ANNData Collection, Processing and Analysis

Determination of Number of Hidden Layers

Training

Model VerificationThe best model is selected according to :1. Multiple linear regression, R2. Mean Squared Error (MSE)

Combination of

input variables

Variations of the

number of

hidden neurons

Carry out

several training

Results: R, MSE,

number of

hidden neurons,

number of

utilized weight

Best model

selection

Methodology for training and validation


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4 ANN models will be created using 4 sets of data

Training

Prediction

ANN MODEL 1:1. Sucrose2. Sago Starch

3. Cassava Flour4. Urea5. Methanol6. KH2PO47. Fe8. Zn9. Mn10. Cu

11. Mg

ANN MODEL 2:1. Sucrose2. Mineral

solutions(Zn, Cu, Mn, Mg)

3. Inoculum

ANN MODEL 3:1. Initial pH2. Moisture

Content3. Temperature

ANN MODEL 4:1. Aeration2. Agitation

Experimental valueof Citric Acid

Production for eachset of data(g/kg-EFB)

Predicted Citric AcidProduction(g/kg-EFB)

Makecomparisonand get thevalue of R


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% Building Neural Network Model

% Define the input parameters and values% Sucrose with unit (% w/w)S =[8 6 4 6 6 4 4 6 8 8 4 6 6 6 6 3 6 8 9 6];% Mineral Solution with unit (% w/w)MS=[12 2 12 8 8 4 4 8 4 4 12 8 8 14 8 8 8 12 8 8];% Inoculum with unit (% w/w)I =[20 16 20 16 16 20 12 16 12 20 12 16 16 16 16 16 10 12 16 22];% Assigning the input parameters[inputs]=[S;MS;I];

% Define the target values which is the citric acid production (CA)targets_CA=[259.23 267.47 256.52 334.68 334.23 208.69 236.65 332.44 259.22 260.65 250.02 333.88 333.08 292.42 334.14 247.46 257.79 285.06 278.9 217.41];% Creating the networknet=newff(inputs,targets_CA,50,{},'trainbfg');

% Train the network[net,tr]=train(net,inputs,targets_CA);

% Predicted Outputpredicted_CA = sim(net,inputs);predicted_CA';plotregression(targets_CA,predicted_CA)

EXAMPLE OF COMMAND-LINE FUNCTION


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SENSITIVITY ANALYSISLinear Correlation in MATLAB (corrcoef)

Correlation coefficient quantifies the strength of a linear relationship between

two variables.

The correlation coefficients range from -1 to 1, where

1. Values close to 1 suggest that there is a positive linear relationship between

the data columns.

2. Values close to -1 suggest that one column of data has a negative linear

relationship to another column of data (anti-correlation).

3. Values close to or equal to 0 suggest there is no linear relationship between

the data columnsThis command function will also give the p-value of each relationship. Each p-

value is the probability of getting a correlation as large as the observed value by

random chance, when the true correlation is zero. Small p-value give better

correlation (normally less than 0.05).


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% Sensitivity analysis using Linear Correlation% The response is the of Citric Acid Production in (g/kg-EFB)

Output=[30.56 77.49 5.995 7.537 5.267 64.84 23.16 66.12 7.577 18.83 128.9 13.11];

% The 11 independent variables as input variable are defined as follows:

S=[3 3 0 0 3 3 0 3 0 0 3 0]; % Sucrose - unit is [%(w/w)]

% Calculate the correlation and p-value.% If p(i,j) is less than 0.05, then the correlation r(i,j) is significant.

'S'[r,p]=corrcoef(S,Output)

EXAMPLE OF COMMAND-LINE FUNCTION

ans =

Sr =

1.0000 0.66650.6665 1.0000

p =1.0000 0.01790.0179 1.0000


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PART 1: EXPERIMENT

Preparation of materials, media and equipment

Experimental procedure for solid state bioconversionUsing optimal conditions obtained from ANN models

Harvesting and extraction of citric acid

Determination of citric acid

However, the experiment will only be run if theoptimum value of the parameters of each set are

different from the optimum value obtained from RSM


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PRELIMINARY RESULTS

Graph of correlation coefficient value (r)for each media constituent

Graph of correlation coefficient value (r)for each parameters of media optimization

SENSITIVITY ANALYSIS

RESULTS..


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Number of Hidden Neurons: 20 Number of Hidden Neurons: 30

Number of Hidden Neurons: 50

METHOD ANN MODEL RSM

No. of

hidden

neurons

20 30 50

R value 0.8464 0.94987 0.93218

0.985

ANN MODEL 2


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EXPECTED RESULTS

1. ANN Models more stable2. The value of R should be higher than the value of R

obtained by RSM

3. The best model can be selected from the best model of

each set of data that give highest value of R4. The optimum value of each parameter can be obtained

from the best model

5. The optimum output can be predicted using the optimum

value of parameters

6. The results of ANN cab be compared with the result from

RSM


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CONCLUSION

1. ANN can be used to optimize fermentation process.

2. Data from existing studies were used for training ANN model.

3. ANN can be used with multiple parameters as input.

4. ANN can be retrain with different number of hidden neurons

and other parameter to obtain better result.

5. MATLAB provide command-line functions and neural network

toolboxes that help to build ANN model.

6. Model from the preliminary work is still unstable and need

improvement


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No Tasks

2009 2010

November January February March April

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

1

Revise and improve

the existing preliminary

ANN model.

2Obtain the best model for

optimization

3

Get the optimum

parameters and predicted

output from the firstmodel

4

Build ANN models for

other data and perform

optimization

5

Determine the optimum

parameters and predicted

output from the ANN

models

6Plan and run experiment

for validation if necessary

7Analyse results and write

final report

8Preparation for FYP 2

presentation

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Optimization of Citric Acid Production Using Ann