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The effect of biomass on PF combustion

efficiency and ash properties

during coal/biomass co-combustion

BCURA Project B77 Final Report March 2007

Period of Report: 01-10-2004 to 31-03-2007 Duration of Project: 30 months Start date: 01-10-2004 End date: 31-03-2007

BCURA Project Officer:

University Project Managers:

Mr Alf Malmgren, RWEnpower, nPower One, Electron, Windmill Business Park, Whitehill Way Swindon SN5 6PB Tel:01793 893164 Fax: 01793 896251 Email: alf.malmgren@rwenpower.com

Dr Ed Lester, SChEME University of Nottingham Nottingham NG7 2RD Tel: 0115 951 4974 Fax: 0115 951 4115 Email: edward.lester@nottingham.ac.uk

Prof. Colin Snape SChEME University of Nottingham Nottingham NG7 2RD Tel: 0115 951 4166 Fax: 0115 951 4115 Email: colin.snape@nottingham.ac.uk

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EXECUTIVE SUMMARY Introduction & Background to Problem The recognised effects of climate change have prompted an urgent appraisal of realistic alternative power generation options. One of these is the replacement of part of the fossil fuel, i.e. coal, with a renewable and carbon-neutral energy source such as biomass. The combustion characteristics of biomass materials need to be thoroughly investigated to allow the main technology users, i.e. the power generators, to proceed towards the confident and safe maximum utilisation of these fuels. The UK Government has imposed a target of the replacement of 10% of fossil fuel usage by renewables by 2010 and 20% by 2020. This is a challenging, not to say improbable target, which has required that the UK power generators fully investigate how these targets may be met. The successful combustion of coal / biomass blends is important in a number of ways. Firstly, how does the biomass actually burn in the presence of pulverised coal? Two important questions are, 'How much biomass can be burned with coal without causing unacceptable burnout problems?' and 'At what size of biomass particle are burnout problems likely to arise?' Secondly, 'How will the ash-forming constituents from the biomass interact with the coal minerals during and after combustion?' The composition of biomass ash is very different from bituminous coal ash and the potential for sintering and deposition in parts of the convective sections of the boiler will need to be examined. Thirdly, 'How will changes in composite blend ash composition, including potentially higher than normal unburnt carbon contents, affect ash collectability in the electrostatic precipitator [ESP]?' Finally, 'Would the current practice of selling fly ash be jeopardised as a result of burning coal / biomass blends arising from changes in the overall ash composition?' Aims This project was designed to help power generators burn biomass in blends with coal in a safer, more reliable manner. It has focused on a number of important technical challenges. The specific aims of the research programme included:- i. the characterisation of currently used biomasses, pulverised fuel and blends of both. ii the preparation of chars from both the biomasses and coal / biomass blends and their subjection to a number of analytical techniques such as thermogravimetric, proximate and image analysis. iii the exploration of the effect of biomass particle size, char preparation method and blend composition on char morphology. iv the development and use of a coal char burnout model from a previous BCURA project [B58] and its application to coal / biomass char. v the preparation of coal / biomass blend ash samples and the performance of ash sintering tests to determine whether any enhanced fouling might result from the presence of biomass ash.

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vi the testing of fly ash obtained from full-sized boiler plant in the University of Nottingham's Electrostatic Precipitator Test Facility [ESPTF] to establish whether the co-firing of coal / biomass blends would have any adverse effect on the collectability of fly ash in the ESP's of full- sized boiler plant. vii the collating of information derived from the work on biomass / coal co-combustion and their ashes which will be useful to power producers in purchasing and utilisation of biomass for electricity generation. Main Results & Conclusions One of the main objectives of this work was to determine the effects of biomass quantity and size on carbon burnout by the development of a coal combustion model for application to coal / biomass blends. This was achieved and biomass char morphological data was obtained using image analysis techniques. The new model, known as the ChB model, was found to predict that the lower heat content of biomass, compared with coal, would result in a reduction in operating temperature of the boiler furnace. However, this effect could be partially offset by the use of those biomass materials of highest heat content, as this was shown to result in smaller furnace temperature reductions. A further aim was to investigate how the ash-forming constituents from the biomasses affected the coal ash in terms of its overall depositional tendency. This was accomplished by the development of a test method followed by the measurement of ash sinter strength of the blend ashes. Most of the coal / biomass ashes increased the fouling tendency as measured by the sinter strength tests with cereal co-product being the worst. This test appears to be a more promising discriminator of ash depositional behaviour than previously reported compositional indices [Skorupska & Crouch]. Increased levels of carbon in fly ash affect its resistivity and hence its collectability in an electrostatic precipitator. The extent to which this occurs formed another aim of this project. The overall collection efficiency of fly ash from burning coal / biomass blends was found to be similar to that from coal-only ash. However, the ash and carbon from biomass / coal firing was collected further downstream in the ESPTF than for coal-only ash. This is attributable to differences in the resistivities of the two types of ash. The marketability of fly ash is important to many generators and the effect of burning coal / biomass blends was investigated. Changes to the ash composition resulting from the combustion of 10% by weight of biomass with the coal were found to be small. This suggests that the utilisation of such ashes will, in most cases, continue. In addition to the completion of the original work programme there has been the development of an innovative method which can determine the biomass content of any coal / biomass blend. This data is unobtainable by any other means and the methodology has already been published in the scientific press and successfully 'blind-tested' by one of the industrial partners. It will provide the generators with a useful new tool in the development of coal / biomass firing technology.

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Economic & Strategic Issues This project has produced a significant amount of data which will be of benefit to power generators and BCURA member equipment suppliers. It has demonstrated the effects of biomass addition on burnout, how these might be mitigated and how the use of biomass affects ash collectability, boiler fouling and ash saleability. It will allow the generators to consider the use of a wide range of currently available biomasses with the knowledge that additional data on their combustion behaviour is now available. The combustion of higher amounts of biomass can now be confidently assessed using a computer model and without the risk and costs of running a boiler trial. An additional bonus from this project is the development, validation and availability of a unique method for the determination of biomass content in coal/biomass blends.

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CONTENTS

Page

EXECUTIVE SUMMARY 2 CONTENTS 5 1. INTRODUCTION 7 2. EXPERIMENTAL 9 2.1 Thermal analysis & testing of coal, biomass & blends 10 2.1.1 Samples 10 2.1.2 Sieve and proximate analysis 10 2.1.3 Thermogravimetric analysis [TGA] 10 2.2 Determination of biomass content of coal/biomass blends 10 2.2.1 Slow heating tests 10

2.2.2 Source apportionment using TGA data 11 2.3 Drop-tube furnace [DTF] testing 11 2.3.1 Modifications to DTF for large biomass particles 11 2.4 Ash sinter strength studies 12 2.4.1 Ash preparation 12 2.4.2 Pelletisation 13 2.4.3 Sintering & crushing tests 13 2.4.4 Chemical analysis of coal / biomass ash blends 14 2.5 Electrostatic precipitation testing 14 2.6 Burnout modelling studies 15 2.6.1 Sample characterisation 15 2.6.2 Char preparation 16 2.6.2.1 DTF chars 16 2.6.2.2 Slow pyrolysis chars 16 2.6.3 Thermogravimetric analysis of chars 16 2.6.3.1 DTF chars 17 2.6.3.2 Slow pyrolysis chars 17 2.6.4 Scanning electron microscopy of chars 17 2.6.4.1 DTF chars 17 2.6.4.2 Slow pyrolysis chars 17 3. RESULTS 18

3.1 Determination of biomass content of coal/biomass blend 18

3.1.1 Slow heating rate testing 18 3.1.2 Source apportionment using TGA data 18 3.1.2.1 Heating rate experiments 18 3.1.2.2 Individual biomass and coal heating profiles 19 3.1.2.3 Blend profiles 20 3.2 Ash sinter strength studies 20 3.3 Electrostatic precipitation testing 20 3.4 Burnout modelling studies 21

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3.4.1 Characterisation of char morphology 21 3.4.1.1 Char geometric and morphological features 21 3.4.1.2 Automated char image analysis 21 3.4.2 Char morphology studies on biomass / coal chars 22

3.4.2.1 A representation of sectioned biomass/coal derived chars 22

3.4.2.2 Char morphology results 22 3.4.3 Burnout modelling of biomass / coal blends 23 3.4.3.1 Basics of co-firing 23 3.4.3.2 Modifications to ChB Model 23

3.4.3.3 Structure of the ChB Model for coal / biomass blends 24 3.4.3.4 Model validation 24 3.4.3.5 Properties of biomass / coal blends and their chars 25

4. DISCUSSION 25 4.1 Thermal analysis & testing of coal, biomass & blends 25

4.1.1 Proximate analysis 25 4.1.2 Thermogravimetric analysis 26

4.2 Determination of biomass content of coal/biomass blends 26 4.2.1 Source apportionment using TGA data 26 4.2.1.1 Using peaks to identify components 26 4.2.1.2 Using profile mapping 27 4.3 Drop-tube furnace testing 27 4.3.1 Using coal/biomass from power plant 27 4.3.2 Using coal/biomass samples prepared in the laboratory 28 4.3.3 Using biomass samples 28 4.4 Ash sinter strength studies 28 4.5 Electrostatic precipitation testing 30 4.6 Burnout modelling studies 30 4.6.1 Discussion of results 31 4.6.2 Observations 33 5. CONCLUSIONS 34 6. PROPOSED WORK FOR A FUTURE PROGRAMME 35 7. PUBLICATIONS ARISING FROM THE WORK 36 8. REFERENCES 36 9 LIST OF TABLES 40 10. LIST OF FIGURES & APPENDIX 41 TABLES 42 FIGURES & APPENDIX 72

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1. INTRODUCTION The recognised effects of climate change have prompted an urgent appraisal of realistic alternative power generation options. One of these is the replacement of part of the fossil fuel, i.e. coal, with a renewable and carbon-neutral energy source such as biomass. The combustion characteristics of biomass materials need to be thoroughly investigated to allow the main technology users, i.e. the power generators, to proceed towards a safe, maximum utilisation of these fuels. The UK Government has imposed a target of the replacement of 10% of fossil-fuel usage by renewables by 2010 and 20% by 2020. This is a challenging, not to say improbable target, which has caused the UK power generators to investigate fully how these targets may be met. The presence of a small number of large, coal-fired power stations in the UK and the availability of Renewable Obligation Certificates [ROCs] has motivated most of the generators to implement biomass / coal co-firing. Each ROC certificate, which a generator earns, is equivalent to 1MWh of electricity generated by an approved renewable source. What might appear, at first sight, to be one of the simpler options to meeting the UK Government target, namely that of replacing a proportion of the coal by a renewable fuel such as biomass is not necessarily the case. The current regulations are that any biomass can be co-fired until March 2009 with no minimum percentage of energy crops. 25% of co-fired biomass must be energy crops from April 2009 until March 2010 and 50% of co-fired biomass must be energy crops from April 2010 until March 2011. 75% of co-fired biomass must be energy crops from April 2011 until March 2016.Co-firing ceases to be eligible for ROCs after 31 March 2016. In view of the situation, a wide range of biomass materials will be needed for use by the generators over the next few years and there is a need to establish a suite of combustion data that can be used to assess the suitability or otherwise of different types of biomass. There are a number of aspects of the properties of biomass fuels which need to be considered including:

• Preparation and introduction of biomass with PF into the boiler furnace. • The effect of biomass on ash deposition, sintering etc. • The effect of biomass on the unburnt carbon content of fly ash. • The effect of biomass on ash collectibility and its resale potential.

This project was designed to study aspects of the effect of biomass on the combustion process. It is important in a number of ways. Firstly, how would the char from the biomass actually burn in the presence of pulverised coal? Two factors are important in this respect; 'How much biomass can be burned with coal without causing unacceptable burnout problems?' and 'What size biomass particle may start to cause burnout problems?' Secondly, how will the ash-forming constituents from the biomass interact with the coal minerals during and after combustion? The composition of biomass ash is very different from bituminous coal ash and the potential for sintering and fouling will need to be examined. Thirdly, changes in overall ash composition, including higher than normal unburnt carbon, could affect ash collectability in the electrostatic

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precipitator [ESP] and, most importantly, the disposal of the ash as a product into the construction industry could be affected by the resulting ash composition. The specific aims of the research programme were as follows: I. Characterisation of Biomass and Coal Samples of biomass, selected after consultation with the power generators, together with a typical power station grade coal will be obtained and characterised for baseline data acquisition. The data obtained will include chemical analysis of the fuels and their ashes. II. Preparation & Pyrolysis of Coal / Biomass Chars The biomass samples will be prepared in a number of different size ranges, where possible. They will then be mixed with PF to a range of blend compositions and devolatilised using a number of different techniques, such as drop-tube furnace and volatile matter crucible tests. A comparison of some of the measured volatile yields with data from available predictive models will be made. A particular feature of this work programme will be the development of the use of the drop-tube furnace for coal / biomass blend pyrolysis and combustion studies. In particular, this will involve designing a DTF feeder to handle biomass samples and investigating limits on particle size. The drop-tube furnace at E.ON UK's Power Technology Centre will be made available for this project. III. Characterisation of Coal / Biomass Chars The characteristics of chars from devolatilisation of coal / biomass blends will be investigated using TGA and image analysis. The effect on burnout of sample size, preparation method, morphology and blend composition will be explored. IV. Modelling of Coal / Char Burnout Work will be carried out which will use results and methodologies arising from another BCURA project which has studied advanced analysis of coals and chars [BCURA Project B58]. These techniques will be applied to the study of biomass char morphologies. Biomass char will be sampled, mounted using a liquid resin and then analysed using a newly-developed advanced automatic char analysis (AACA) technique. Char morphology data produced by AACA will then be adopted as inputs to a modified char burnout model to give a better prediction of char burnout of biomass and coal blend combustion. The investigation of char morphology for biomass and coal blend will be conducted in three different ways, on pure biomass char, coal char and chars derived from biomass and coal blend co-pyrolysis. The effect on burnout of biomass size as a component in coal co-combustion will therefore be more easily assessed using this technique.

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V. Preparation & Characterisation of Coal / Biomass Ashes Coal / biomass blend ash samples will be prepared using a number of techniques such as conventional ashing and using the drop-tube furnace. The ash samples will be analysed to establish their composition and likely effects on other coal mineral particles. Additionally, image analysis techniques, already developed at the University of Nottingham, will be used to investigate carbon contents and form in the ashes. This, together with the relative oxide contents, has a direct bearing on both the ash potential for sintering and fouling and potential use in the construction industry. VI. Sintering Tests on Coal / Biomass Ashes The sintering strength of the coal / biomass ash mixtures will be measured over several temperatures in the range 800oC to 1,100oC. The sintering test, which was developed by [Gibb] involves the preparation of low temperature ash which is then compressed into pellets using a standardised procedure. The pellets are sintered at a number of temperatures and the crushing strength measured. From a plot of the sintering temperature against strength an assessment of the effect of fouling on boiler tubing can be made. VII. Electrostatic Precipitation of Coal / Biomass Ashes Samples of flyash from full-sized boiler plant will be tested in the University of Nottingham Electrostatic Precipitator Test Facility (ESPTF). The tests will compare the performance of coal-only fly ash with that from coal / biomass firing. The purpose of the work will be to establish whether the co-firing of coal / biomass blends had any adverse effects on the collectability of fly ash in the ESP's of full-sized boiler plant. VIII. Recommendations for Power Generators Information derived from the work on biomass / coal co-combustion and their ashes will be made available to power generators in a form which will enable them to use it for improved biomass purchasing and utilisation. All of the equipment to carry out this project is either in place at the University of Nottingham or at the sites and laboratories of E.ON UK. 2. EXPERIMENTAL The experimental work can be divided into a number of separate but interlinked activities. They are presented chronologically in the sequence in which the project proceeded:-

• characterisation of biomass samples; • the development of a new technique to quantify the biomass content of

coal / biomass blends; • sinter strength measurement of ash from coal / biomass blends, • electrostatic precipitation studies of fly ash from coal / biomass blend

firing and • burnout modelling of coal / biomass blends.

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2.1 Thermal analysis & testing of coal, biomass and blends

2.1.1 Samples A total of 17 samples were analysed during the course of the project comprising 9 different biomass samples, 4 PFA samples, 3 coal/biomass blends and one pulverised coal sample. A database of all samples received during the project is shown as Appendix 1.

2.1.2 Sieve and proximate analysis

Four of the biomass samples, PKE, two samples of CCP and a wood sample were air-dried, ground and then separated into sized fractions. The whole sample and the fractions were subjected to proximate analysis, [see Table 1.] Three coal / biomass mixtures were also sieved and subjected to size fractionation, [see Table 2.], and proximate analysis, [see Table 3.]. These samples were not, however, air-dried before testing. 2.1.3 Thermogravimetric analysis [TGA] The four biomass samples which had been separated into sized fractions were subjected to thermogravimetric analysis [TGA]. Data on the whole samples and the sized fractions is shown in Table 4. TGA was also performed on the coal / biomass blends, both the whole sample and the sized fractions, [see Table 5]. The differences between the Peak Loss Temperature [PLT] and Burnout Temperature [BOT] parameters were compared for the whole sample and the sized fractions. 2.2 Determination of biomass content of coal/biomass blends It is important for power generators to be able to measure the proportion of biomass in an actual coal/biomass blend. Using simple techniques such as density separation has been shown to be completely unsatisfactory. This is believed to be due to the very intimate mixing and grinding which results from coal/biomass blend preparation when using full-sized milling equipment. To address this important requirement it was decided to investigate the response of such blends to a variety of different, but mainly very slow heating rates, using a thermogravimetric analyser. It was hoped that the volatile matter in the biomass would be evolved at a different rate and temperature to that from coal and that this could be used to quantify the biomass content of the blends. A series of blends were made up using four different biomass samples which had been sieved into three different sizes. Proximate analysis of the blends was then carried out to compare the actual blend composition with the theoretical value. TGA was then carried out on the blends. 2.2.1 Slow heating tests Four biomasses, palm kernel expeller [PKE], sawdust, olive cake and cereal co-product and Daw Mill coal were sieved and three size fractions were separated. The sizes were -75+53microns, -150+106microns and -212+150microns.

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Proximate analysis was performed on each of the samples using a method based on TGA. The results are shown in Table 6. The purpose of analysing the different sized fractions was to highlight what differences, if any, existed within the size distribution of the samples. A series of blends were made using the -150+106 size fractions of biomass and coal. Additions of 5%, 10%, 15% and 20% by weight were made and the theoretical or calculated proximate analysis data is shown in Table 7. Actual proximate analysis data on the well-mixed fractions and obtained by TGA is shown in Table 8. To investigate the effect of slow heating on the evolution of volatile matter a blend of 10% cereal co-product in Daw Mill coal, [both of size -150+106microns], was heated in a controlled manner. The chosen heating rates were 1OC/m, 5OC/m, 10OC/m and 50OC/m. The blended fractions were subjected to proximate analysis using a heating rate of 5OC/m.

2.2.2 Source apportionment using TGA data

The four biomasses chosen for this study were cereal co-product [cereal], palm kernel expeller [PKE], olive cake, and sawdust, see Table 6. The coal used was Daw Mill. Samples of 15-20mg were taken and heated using different ramp rates [1-50oC/min] in nitrogen [30ml/min] to a maximum temperature of 900oC. Once this temperature was reached, the instrument gas was changed to air at a flow rate of 30ml/min. Although different ramp rates were investigated initially, the 5oC/min ramp rate was eventually chosen as the standard for the remainder of the work. 2.3 Drop-tube furnace [DTF] testing

2.3.1 Modifications to DTF for large sized biomass particles This project required the preparation of a number of char samples from biomass and coal / biomass mixtures and the most appropriate method with which to do this is the use of the drop-tube furnace [DTF]. The DTF used for this work was, at that time, located at E.ON UK’s Power Technology. It has subsequently been re-located to the University of Nottingham’s School of Chemical, Environmental & Mining Engineering. In order to produce biomass char samples of a suitable size for image analysis and modelling activities it was essential that the DTF should be able to pyrolyse biomass samples up to 2mm or 3mm in size. The original configuration of the DTF, [for firing pulverised coal], was with an inlet feeder tube of 3mm and a larger diameter feeder was needed. Fortunately, a similar DTF, formerly owned and operated by ABB Combustion Services Ltd. in Derby, was available for spares and the top box containing the feeder was removed for modification. The inlet feeder was used as a pattern to manufacture a replacement with a wider, i.e. 6mm inlet diameter. This was then refitted to the spare top box so that it will be interchangeable with the original smaller feeder arrangement.

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The new inlet feeder, with an ID of 6mm was refitted, the temperature profile of the DTF was measured and a series of chars were prepared from larger sized biomass particles. The details of their composition are described in section 2.6.2.1. 2.4 Ash sinter strength studies

2.4.1 Ash preparation It is recognised that fly ash produced under laboratory conditions does not resemble that which is produced in a full-sized, pulverised coal-fired boiler. However, in order to carry out controlled experiments the only practical option was to make use of laboratory-prepared ash samples. In the case of ash sinter strength studies using coal biomass blends, a further potential problem arises. It is also recognised that during the ashing process inorganic materials in the fuel may interact with one another. The degree of interaction and the resulting compounds will depend upon the proximity of individual mineral particles with other different species. For this reason it could be argued that to mix ashes which had been prepared from separate single substances such as coal and biomass would be unacceptable as it would not allow for the potential interactions described previously. It would be preferable, therefore, for coal and biomass blends prepared in a full-sized milling plant to be used to prepare ashes for testing. However, the collection of large amounts of coal/biomass blends from operational boiler plant is time-consuming and much of the relatively small samples supplied by the generators were used for char preparation and characterisation. The residual amounts of coal/biomass blends proved to be insufficient to permit any study of ash sinter strength. For this reason it was decided that the only realistic and practical option was to prepare well-mixed samples of coal and biomass in the correct proportions and then to ash them. It is recognised that the degree of homogeneity will not be as great as in a full-sized milling system but it remained the best available alternative. Some of the biomass samples were supplied as pellets and as such proved to be unsuitable for fine grinding. These were crushed in a pestle and mortar and reduced to as small a size as possible, [100% -2.80mm], before mixing and ashing. Other biomasses were crushed to 100% -1mm. The preparation of bulk quantities of biomass ash involved the mixing of 90.0g of Daw Mill coal, supplied by E.ON UK plc, with 10.0g of the chosen biomass material. The coal / biomass mixtures [100.0g] were placed in a 500ml sealed plastic jar and shaken vigorously for several minutes. The blends were then placed on a large silica tray and heated slowly in a muffle furnace. This was a slow procedure and generally took a total of 24 hours for each sample. The final ashing temperature was kept at 550OC to minimise loss of volatile ash constituents. A quantity of around 12 to 13 g of ash was produced for each sample. This procedure that was adopted was similar to that reported by (Fernandez Llorente and Carrasco Garcia, 2005).

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2.4.2 Pelletisation

The low-temperature ash samples were homogenised by passing them through a 500 micron sieve. Pellet formation was carried out using a lever press and die system from which the required load could be easily applied to ensure consistent pellet quality. The press is of a similar construction to that reported by (Gibb, 1981). In this work Gibb determined that a force of 200psi [1.37MPa] produced pellets which, when sintered, had similar strengths to those found in superheater deposits in boilers. The die was 1.0cm in diameter and by the correct positioning of a suitable weight on the fulcrum arm, a force of 1.37MPa could be applied consistently thereby producing similar pellets. The preliminary work was carried out on Daw Mill coal ash and first attempts to produce dry pellets were unsuccessful. This was partly due to the bulky nature of the ash and also because it was necessary to apply only a moderate force in order to simulate ash deposition inside the boiler. The dry pellets proved to be too friable to handle. The possibility of using a pelletisation aid was considered and rejected as it was recognised that it would probably affect the final strength of the pellet and thus lead to misleading results. To produce a cohesive pellet it was necessary to moisten the ash with deionised water and mix well. It was found that a minimum of 30% to 32% water was required to produce pellets which could be handled. A number of test pellets were produced using 1.0+ 0.1g and 1.5+ 0.1g. It was found that the 1.0g pellets were 8.0 to 9.0mm in height and the 1.5g pellets were 13mm in height. Once the procedure had been validated a quantity of pellets [12] from each coal / biomass were produced.

2.4.3 Sintering & crushing tests

The pellets were allowed to air dry and then sintered. A high temperature tube furnace was used which had been acquired for an earlier research project. Four pellets were heated to 800OC, held at this temperature for 8 hours and then cooled to ambient temperature. A small flow [50ml per minute] of air was passed through the furnace at this time. Further batches of pellets were sintered and the experiment was repeated at 900OC, 1,000OC and 1,100OC were produced. The maximum crushing strength was determined using a Lloyd J30K tensometer. The crushing speed was 2mm/m and data was collected using a data logger. The files were transferred to a PC and from the Excel spreadsheets, which were produced, the maximum force needed to crush the pellet was established. The preliminary work using the Daw Mill samples was successful and the remaining ash pellets were crushed and the maximum load was recorded.

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Plots were produced comparing the crushing strength of the blend ashes with that of Daw Mill at the four sintering temperatures. The mean of three determinations was taken and the data converted into MPa. 2.4.4 Chemical analysis of coal / biomass ash blends In order to see if there was any correlation between the sinter strength measured from the pellets and their chemical composition, the ash samples were subjected to analysis by an x-ray fluorescence technique. The results are shown in Table 9. The elements have been shown as their oxides and an empirical Fouling Index has also been included. 2.5 Electrostatic precipitation testing The electrostatic precipitator test facility [ESPTF], located at the University of Nottingham, consists of a parallel plate precipitator with a normal ambient gas flow of 0.1 m3/s, which was constructed under the guidance of Lodge-Sturtevant; a major supplier of dust extraction equipment. The unit is 4.5m long, 0.35m high and has a width between the collector electrodes of 0.3m. The length and width are typical of full-scale commercial precipitators but the height is much less. It can be energised with direct voltages up to 40 kV. It is a single-stage device comprising a high-voltage supply, a bag filter, an induced draught fan, a mixing chamber, a sample feeding system, a heat exchanger, and external lagging. The fly ash particles are conveyed and dispersed into the ESPTF by a rudimentary feeder. An induced draught fan provided a flow of air through the test facility. Previous work had shown that most of the ash particles precipitate within the test facility and adhere to the surface of the collector plates. The escaping gas from the test facility outlet passes through a bag filter for further cleaning before being released into the atmosphere. A schematic diagram of the ESPTF is shown in Figure 1. Economiser dusts samples taken during coal/biomass co-firing were received from SSE and RWEnpower. A total of seven small cyclone samples were received from E.ON UK’s Combustion Test Facility, [CTF], which were taken during coal/sawdust test firing. Since LOI values were similar the samples were combined and well mixed to give a total weight of ~20kg. This quantity was sufficient to enable testing to be carried out using the University of Nottingham’s ESPTF. The ESPTF has been used to compare the collectability of fly ash from firing coal and biomass with that from coal alone. Much data has been recently obtained on the ESPTF on recently completed studies (ECSC Project 7220-PR-123, 2005). Three samples of fly ash were tested in the ESPTF and data on the collectibility and distribution of LOI has been obtained.

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2.6 Burnout modelling studies The work described here has made use of methodologies and some results arising from another BCURA project which studied advanced analysis of coals and chars, (BCURA Project B58, 2003). The objective of this part of work was to take the char burnout model [ChB] developed at the University of Nottingham and make necessary modifications to make it suitable to predict the overall char burnout of biomass-coal blends under typical pulverised coal combustion conditions. For modelling purposes, it is of great significance to know the morphology of the char since it affects gas phase diffusion during char combustion (Williams, 2001, Wu et al., 2006b). Many attempts have been made to characterise coal-derived chars (Bailey et al., 1990, Lester et al., 1996, Alvarez et al., 1997, Wu et al., 2006a). However, comparatively less work has been done to characterise biomass-derived chars (Sharma et al., 2001, Sharma et al., 2004, Cetin et al., 2005, Guerrero et al., 2005, Gani and Naruse, 2007). In this study, biomass/coal-derived chars were produced in order to provide detailed information on char morphology for combustion modelling. The morphologies of coal- and biomass-derived chars were studied using the advanced automated char image analysis [AChIA] technique developed at the University of Nottingham (Wu et al., 2006a), which includes size, porosity, shape, pore size and its distribution and average char-wall thickness. The main aims of the char characterisation process were: • to measure the morphology characteristics for each char particle, • to assign each char a char morphotype, • to identify the differences between coal-derived chars and biomass-derived

char, and chars derived from different types of biomasses, and, • to collect char morphology data that could be used in the char burnout

model. A modified version of the char burnout model [ChB] (Cloke et al., 2003, Wu, 2004, Wu et al., 2006b) developed at the University of Nottingham was used to predict the burnout of coal-biomass blends. Char morphological data, both for coal- and biomass-derived chars were then fed into the modified burnout model to predict the outcome of co-firing coal and biomass blends under typical pulverised coal combustion conditions, and to assess the potential impacts of biomass addition on overall carbon conversion. Previously, this model had only been used for modelling coal combustion, (Wu, 2004, Wu et al., 2006b).

2.6.1 Sample characterisation Table 10 lists the proximate data for CCP, olive cake and PKE in the three small and three larger sizes chosen for these experiments. Data is also presented for the coal used in the small size ranges. Comparing the data, it is clear that the differences in the proximate data between large and small biomass and coal samples are relatively small.

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A series of blends were made using the coal and biomasses using 5%, 15% and 30% by weight for each of the three smaller size ranges. The data is shown in Table 11.

2.6.2 Char preparation

2.6.2.1 DTF chars

The drop-tube furnace [DTF] is a suitable facility for the preparation of chars for the assessment of their reactivity (Card and Jones, 1995, Lester and Cloke, 1999, Cloke et al., 2002a, Cloke et al., 2002b, Ulloa et al., 2005). A short study was carried out using the DTF located at E.ON’s Power Technology to produce chars from biomass. Details concerning the DTF configuration can be found elsewhere (Lester et al., 1993, Cloke et al., 2002b, Cloke et al., 2003). The three biomass samples, CCP, olive cake and PKE, were ground and sieved into different size fractions: -106+53 microns, -150+106 microns and -212+150 microns. Blends were made with Daw Mill coal in the same size fractions in a controlled ratio. The percentages of biomass in blends were in three levels: 5, 15 and 30%. The blends were then fed into the DTF at a rate of around 0.1g/min, subjecting them to an average temperature of 1,300ºC and an atmosphere of 1% oxygen in nitrogen for around 200 milliseconds. Char samples were collected by a cyclone attached to the collector probe exhaust line. DTF experiments were also carried out using only biomass particles. These were of a larger than normal size fractions i.e. 0.5-1mm, 1-2mm and 2-2.8 mm, which were fed at a rate of ~0.1g/min with the same furnace conditions as described for the blends.

2.6.2.2 Slow pyrolysis chars Since the larger sized biomass particles required a significantly longer residence time than realistically possible in the DTF, a second set of similarly sized chars was produced by slow pyrolysis in a laboratory muffle furnace heated to 1,000ºC. The samples were placed in a crucible fitted with a lid, to prevent oxidation, for 5 minutes. The crucibles were weighed before and after heating to determine the loss in weight and thereby the weight of char produced. Each sample of biomass was then blended with the Daw Mill coal sized between 75 and 106 microns to 5%, 15% and 30% by weight and pyrolysed in the muffle furnace.

2.6.3 Thermogravimetric analysis of chars A thermogravimetric method for the proximate analysis of chars produced both from DTF and by slow pyrolysis was carried out.

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2.6.3.1 DTF chars The proximate data of the DTF chars from the biomass samples is shown in Tables 12 and 13. The volatile content of the chars from the large biomass particles were significantly greater than those of the finer samples, indicating the need for a much longer residence time to completely devolatilise the large size biomass particles.

2.6.3.2 Slow pyrolysis chars

Table 14 shows proximate analysis results of three biomasses and one coal sample in larger size fractions. It is clear that the devolatilisation of Daw Mill coal was almost complete whilst that of the three biomass samples was far from complete. Table 15 shows the proximate data of biomass/coal blends in larger sizes. It is very interesting to see that when co-pyrolysed with coal, the quantity of volatiles retained in the particles are much lower than when pyrolysed alone. 2.6.4 Scanning Electron Microscopy [SEM] of chars All chars derived from the three biomasses in the three size fractions were characterised using SEM/EDAX in order to obtain data on their surface and macrostructural features including elemental compositions.

2.6.4.1 DTF chars

Figure 2 to Figure 4 show a close-up view of some biomass DTF chars in different size fractions. It is clear from these pictures that the chars from biomass are highly porous. It is also evident that some molten ash/ash-rich particles have formed on the surface of the chars. Elemental analysis of the CCP chars show the light particles [left side of Figure 2] which are rich in Si, Al, P, Ca, and K, whereas the dark particles [right side of Figure 2] are dominated by carbon. In Figure 4, it can be seen that PKE chars are mainly composed of carbon particles, few silicon-rich particles [light particles shown in Figure 3] were found in the large [-212+150 microns] size. Olive cake chars, however, contain significantly numbers of particles rich in Si, Ca and K [light particles shown in Figure 4]. Figure 5 shows the mixture of particles obtained when CCP, PKE and olive cake were pyrolysed in the presence of silica particles. The inorganic material is characterised by its smooth surface and angular appearance.

2.6.4.2 Slow pyrolysis chars Figure 6 shows CCP char particles in three size fractions. The overall shape of the CCP char particles is shown in the image on the left hand side whilst on the right hand side there are close-up images for char particles with specific surface features. It is clear that a large proportion of CCP char particles are flakes, similar to their original shape prior to devolatilisation. The close-up

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images show high levels of spongy porosity, 5-10 microns. Some minerals, such as silicates, can be found as small grains dotted inside the particle. Figure 7 shows the surface nature of olive cake chars. These chars have a similar shape to the particle before devolatilisation. Generally, these chars have very few large surface pores. There is no evidence of fragmentation during pyrolysis, which is a common phenomenon during pf coal combustion (Baxter, 1992, Dakic et al., 1989, Feng and Bhatia, 2000, Liu et al., 2000). This is very important finding and is useful when modelling biomass combustion. Grain size prior to combustion can be adapted [linking into actual swelling features] in order to predict char size. Some surface features of PKE chars are shown in Figure 8. It is clear that, similar to CCP and olive cake chars, PKE chars do not change much, in terms of size or aspect ratio from their original particles. Limited fragmentation occurs during devolatilisation. There is some porosity, but to a much less visible level than with CCP and olive cake. It is also clear that PKE chars did undergo a stage of softening, as normally the case for the devolatilisation of coal particles. 3. RESULTS 3.1 Determination of biomass content of coal/biomass blends

3.1.1 Slow heating rate testing

The results showed that it appeared possible to discriminate between the volatile matter that evolved from the biomass and that from the coal, see Table 16 and Figure 9. From the data obtained it appeared that a heating rate of 5OC/m was the optimum as this allowed the test to be completed in a reasonable amount of time and it allowed full discrimination of the volatile from both blend components. Heating higher rates did not allow the full separation of each volatile fraction. As a result further work at 5OC/m was carried out. 3.1.2 Source apportionment using TGA data 3.1.2.1 Heating rate experiments A range of different approaches have been reported in the literature with regard to TGA methods especially with regard to heating rate. Few workers discuss testing the effect of heating rate on the devolatilisation behaviour of biomass (Modhtaderi et al., 2004). The most common ramp rate reported in the literature when characterising biomass appears to be 10oC/m, (Vamvuka et al. 2003a; Vamvuka et al. 2003b; Kastanaki et al. 2003; Cui et al. 2005), although some are at 20-30oC/m, (Vamvuka et al. 2003a; Peralta et al. 2002; Jones et al. 2005; Vuthlaru, 2004 and Biagini et al. 2002), 50oC/m, (Moghtaderi et al., 2004), 100oC/min (Pan et al. 1996), but relatively few are lower (Gronli et al., 2002). It is therefore useful to investigate how heating profiles change with different ramp rates, since these profiles are characteristic of each component. It is reported in the literature that coal and biomass behave independently during devolatilisation (Kastanaki et al., 2003; Jones et

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al., 2005; Annamalai et al., 2003; and Meesri et al. 2002) hence an optimised heating rate might allow these blends to be characterised more accurately. Figure 10 shows the effect of heating rate on the olive cake [-125+106 micron] at ramp rates of 1oC, 2.5oC, 5oC, 10oC, 25oC and 50oC. Three temperatures were recorded: Tini which is the temperature where weight loss first reaches 0.2%/minute; Tpeak where the weight loss reaches its maximum and Tend where the weight loss falls to below 0.2%/min. The limit of 0.2%/min was established using actual profiles as an arbitrary limit for the start and end of peaks. Clearly the peaks widen as a result of higher heating rates. Figure 11 shows the relationship between peak width, peak temperature and heating rate. Peak temperature increases with heating rate, most noticeably over the 1-10oC/min range. More important is the dramatic broadening of the peak as the heating rate increases. The initial width of the peak at 10oC/min is only 100oC rising to >800oC at 50oC/min. Broad peaks are not desirable when seeking to attribute blend proportions since weight loss profiles will overlap and devolution of peaks becomes more difficult. Biagini and co-workers predicted that heating rates are important for avoiding peak overlaps with biomass coal blends, (Biagini et al., 2002). High heating rates can cause devolatilisation peaks for the coal and biomass to converge although Pan and co-workers appeared to find good resolution at 100oC/min with blends of biomass and a high ranked coal (Pan et al., 1996). Figure 12 shows the effect of heating rate on the coal [-125+106 micron size fraction] and Figure 13 shows the profiles for a 10% olive cake/coal blend using the -125+106 micron fraction. The peak temperatures and the minima between the two peaks all increase with increasing heating rate. Across all the heating rates, the two components appear to devolatilise independently of each other. It was initially expected that the two peak positions might converge leading to a loss of definition, but this was not the case. Increasing the ramp rate from 1oC/min to 50oC/min appears to increase the peak positions for the components and blended components by approximately 75 degrees. However, it is clear that, as with the profiles from the single coal and biomass runs, higher heating rates produce broader profiles which cause an increase in the degree of overlap between the two components. Clearly the optimum ramp rate will give the most defined peaks with the smallest degree of overlap. The other factor is time of analysis. For the 1oC/min run, an analysis time of over 15 hours is required whilst at 2.5oC/min and 5oC/min the times are more than six and three hours respectively. The latter was chosen as a standard condition for blend work, since it gave good peak definition in a more reasonable time.

3.1.2.2 Individual biomass and coal heating profiles

Figure 14 shows the profiles for the -125+106 micron sizes of the coal and four biomass samples. There are two key conclusions from this figure. Firstly, coal volatile loss occurs, not surprisingly, at a significantly higher temperature than the biomass samples. Secondly each profile, including the coal profile, is unique in shape and peak position. The peaks are all different, although the cereal co-product and PKE are close, but the actual weight loss profiles all have characteristic profiles, either above or below the main peak. Some peaks are

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more Gaussian than others. The coal profile is almost Gaussian with a longer shoulder between 500oC and 800oC. The olive cake and sawdust both have a ridged profile between 200oC and 300oC whereas the cereal co-product and PKE both appear more Gaussian. Table 17 shows the change in Tini, Tpeak and Tend for the three size fractions of each fuel type. There is also minimal variation in the characteristic profiles for each size fraction. The proximate analysis of these sizes was not particularly different, as shown in Table 17, hence size effects may have been negated by the choice of a very slow heating rate, allowing the particles to swell and devolatilise controlled only by furnace temperature.

3.1.2.3 Blend profiles

Table 18 shows the proximate analysis for the -125+106 micron coal, biomass and coal/biomass blends of 5%, 10%, 15% and 20% wt/wt. A comparison of measured against predicted proximate analysis composition was found to be accurate to ±0.5%. Figures 15 to 18 show the profiles for the various blends against the pure coal sample. Clearly, in each case, as the biomass proportion increases, so does the first peak at around 300oC. Peak positions do not appear to change more than 1-3o C, hence it is clear that blend proportions do not impact on peak heights or peak width. The characteristic nature of the pure biomass peak also appears in the coal/biomass blends. 3.2 Ash sinter strength studies The crushing strength [CS] data obtained from the tensometer testing relates to the surface area of the pellet, which was 0.7854mm2. The data is converted into KN/cm2 and the mean of the three determinations was calculated. Data for Daw Mill coal ash alone and nine coal ash/ biomass blends were obtained and tabulated for four different temperatures. The results are shown in Table 19. It was noted that under certain conditions for certain samples, the triplicate CS values were, in some cases, poorer than others. To try to quantify the extent of this variation the Standard Deviation of the CS for each series was calculated. This information is shown in Table 20. To illustrate the effect of biomass additions to Daw Mill coal ash, a series of plots were produced showing the CS of the coal ash alone with that of the various blends for the four test temperatures, [see Figures 19 to 27]. Best-fit linear plots were constructed for each set of data. In addition, temperature data was extracted to identify the temperature at which the ash developed a CS of 5MPa, see Table 21. The significance of this data will be discussed in section 4.4. A plot of crushing strength recorded by the data logger is shown in Figure 28. This is typical of most of the data gathered from the crushing strength testing. 3.3 Electrostatic precipitation testing The ashes were subjected to a standardised procedure in the ESPTF using ~1.5g/m3 ash loading, 30kV and 130OC operating conditions. Table 22 shows

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the mass balance of ash in the various sections of the ESPTF and the overall efficiency of the ESP. Table 23 shows the mass balance of LOI in the same sections. For comparison, two PFA’s from coal-firing alone are included. 3.4 Burnout modelling studies 3.4.1 Characterisation of char morphology

3.4.1.1 Char geometric and morphological features It is known that chars from different origins vary significantly in morphology (Bailey et al., 1990, Lester et al., 1996, Wu et al., 2006a). Major geometric characteristics of char that should be measured in order to describe char morphology in a way that can be used in char/coal combustion models are described as follows:- Porosity: A sectioned char can be examined to provide information on the area of void space. There are two types of voids in char, primary pore, which counts the central void[s], and secondary pore, which takes into account voids located on char boundaries. Wall-thickness: It is difficult to define wall-thickness for a specific char since it is difficult to find the exact boundaries for chars. In the automated char image analysis technique developed by Wu and co-workers [Wu et al., 2006a], a series of chords are drawn starting from the centre of gravity of the particle. The thickness of char wall was defined as the chord length through char walls after secondary voids are not taken into account. Fused/unfused material: The fused/unfused materials within a carbonaceous matter are of interest since it may provide information on the reactivity of the char material. A special polar filter is needed together with some expertise in order to distinguish between isotropic and anisotropic. Char voids and their size distribution: It is essential to know the size of voids within the char particle in order to distinguish spherical particles [tenui-/crassi-spheres] from network particles [tenui-/crassi-networks], which also matters for the modelling point of view. Char sphericity: Char sphericity is an indication of char shape, which plays some roles in affecting the burnout of char. The ratio of the particle surface area of an equivalent volume sphere to the actual surface area of the shape is defined in this study as a measure of sphericity.

3.4.1.2 Automated char image analysis In this study, the technique developed by Wu and co-workers (Wu et al., 2006a) was used to analyse char samples that were prepared with a scratch-free surfaces. For each char block, at least 900 images [1300×1030 pixels each] were captured using a microscope with a magnification of 200 times. All the images were stored on a hard-disk. In order to collect char morphology data from the saved image, a series of colour image manipulation functions were firstly applied on individual images to produce a sharpened edge for individual char particles/fragments, which was then converted into a binary image before any measurements were taken.

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Figure 29 shows the general procedure of the AChIA program. Detailed validation of AChIA can be found else where (Wu, 2004, Wu et al., 2006a). In this study, chars produced by slow pyrolysis were mounted using carnauba wax and polished before images were taken of the scratch-free surface. 3.4.2 Char morphology studies on biomass/coal chars

3.4.2.1 A representation of sectioned biomass/coal derived chars

Figure 30 includes typical char images of Daw Mill coal and the three biomass samples. It is clear that for chars derived from Daw Mill coal, the size of voids is large, whilst that for biomass-derived chars is very small as seen in the SEM photos shown above. CCP chars show different morphological features from chars from olive cake and PKE. Table 24 lists the average porosities of chars found in Figure 30. It can be seen that CCP produces the most porous chars whilst olive cake forms the least porous chars.

3.4.2.2 Char morphology results

The AChIA technique was used to characterise the morphology of chars derived from the three biomass and one coal samples and their blends. The time taken to complete the analysis of one char sample was around 3 hours. Figure 31 shows the morphology distribution of chars for the three biomass and Daw Mill coal samples in three size fractions. Daw Mill coal derived chars are dominated by crassi-network particles. Since the original coal particles are large and the overall heating rate in the muffle furnace is not very high, it is likely that volatiles were released at a relatively slow rate escaping through existing pores/cracks. Such slow release of volatiles would avoid the violent evolution of very rapid heating rates that might fragment and rupture the char particles thereby creating more spheres and less networks. For biomass-derived chars, the majority of the particles were networks. Biomass particles do not experience the same degree of softening and swelling compared with heated coal particles. The biomass particles were already ‘soft’ and more porous than the coal. It is clear that PKE-derived chars are dominated by thin-walled network particles with approximately 20% thick-walled; CCP is mainly thick-walled networks with around 20% thin-walled; whilst olive cake-derived chars are predominately thick-walled networks. The average porosities of pure biomass and coal samples are shown in Table 25. Generally, PKE-derived chars are more porous than chars derived from olive cake and CCP. It is interesting to see that the biomass-derived chars in 2.0-2.8 mm size fraction are the least porous compared to chars in 0.5-1.0 mm and 1.0-2.0mm size fractions. However, porosities of Daw Mill char in three difference size fractions do not change much. Figure 32 to Figure 34 compare the differences in char morphology between three sized fractions for chars derived from CCP, olive cake and PKE. In these tests, biomasses in large size fractions were co-pyrolysed with Daw Mill coal of -75+53 micron size. It is obvious from the graphs shown in Figure 32 to Figure

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34 that there is not much change in terms of overall char morphology for Daw Mill coal-derived chars when mixed with biomass. However, it is possible that synergic effects might have lead to morphology change on biomass-derived chars. 3.4.3 Burnout modelling of biomass/coal blends

3.4.3.1 Basics of co-firing

During the pulverised coal-biomass combustion, coal-biomass particles undergo several processes, as shown in Figure 35. Moisture vaporisation and devolatilisation normally take 20-30 ms to complete under typical pf combustion conditions. The devolatilisation, combustion of volatiles, and char burnout may overlap (Smoot, 1993, Williams et al., 2002). However, it is the burnout of char that takes the longest time to complete and therefore determines the fate of carbon in fly ash (Hurt et al., 1998, Hurt and Calo, 2001, Wu et al., 2006b). Since char combustion is the dominant step in pf coal combustion, it is obvious that char structure has a significant impact on the burnout of char under pf combustion conditions (Hurt, 1998, Backreedy et al., 1999, Backreedy et al., 2003, Cloke et al., 2003, Wu et al., 2006b). Generally, the char burnout period is controlled by three rate-limiting steps: gas phase diffusion, heterogeneous reaction, and pore diffusion. In the CBK model (Hurt et al., 1998, Hurt and Calo, 2001), chars are assigned with an average porosity [divided by tortuosity], which is a common way to consider char structure in other char burnout models. It was found by Wu and co-workers (Wu et al., 2006b) that the accuracy of char burnout modelling can be improved by the inclusion of a sub-model to consider the impacts of char morphology on the diffusion of gases. Normally, char morphology data can be obtained by collecting directly from real chars produced in DTF or other laboratory facilities operated under conditions similar to that of pf coal combustion or by predicting based on coal image analysis data (Wu et al., 2006b).

3.4.3.2 Modifications on ChB model As mentioned previously, in this study the ChB model developed at the University of Nottingham was used to assess the overall efficiency of the co-firing for biomass and coal in pf power plants. As shown in section 3.4.2, the vast majority of biomass-derived chars are thick-walled network particles. It is the case that biomass is more difficult than coal to grind into a fine powder in the mills used in power plant. Biomass fed to boilers is normally larger than coal, particularly in co-firing boilers, where the biomass and coal are ground together prior to injection. Therefore, biomass-derived chars are generally larger than coal-derived chars. Due to limited availability of suitable facilities it proved difficult to obtain sufficient quantities of large-sized biomass-derived chars for image analysis purposes. Only sieved biomasses in large size fractions were devolatilised and analysed using the char image analysis technique developed at the University

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of Nottingham. Since the difference in morphology among those size fractions investigated in this study is not significant, it has had to be assumed that biomass-derived chars are all thick-walled network particles with an overall porosity determined by char image analysis. Since the fixed carbon in biomass is low, the yield of char after devolatilisation of small biomass particles is minimal and therefore not easy to collect from the DTF. In practical terms, since it is difficult to feed biomass over its full size range using the DTF facility, biomass-derived chars in a full size range could not be produced. Because biomass-derived chars are more reactive than coal (Janse et al., 1998, Williams, 2001, Backreedy et al., 2003), complete burnout of small biomass-derived chars is likely and the impact of such chars on the overall efficiency of the burnout process is negligible. In this study, only large sized biomass-derived chars were counted and were assumed to have an average size of 150 microns. This is to reflect the known poorer size reduction of biomass particles in the mill. The ChB model assigns chars into eight size groups with particles in each group being further classified into thin-walled, thick-walled and solids depending on their porosity and the thickness of the char walls. In this study, biomass-derived chars were considered as the ninth char group. Because carbon in biomass-derived char has a more disordered structure than that of coal-derived char, the former normally has a slightly higher reactivity than that of bituminous coal-derived char. In the modified ChB model, an intrinsic char combustion sub-model was established for biomass-derived char, which followed a similar approach to that of coal-derived chars under pf combustion conditions. Based on some relevant data (Janse et al., 1998, Jones et al., 2000, Williams, 2001, Arenillas et al., 2002, Backreedy et al., 2003), the activation energy for biomass-derived chars was assumed to be the same as that of coal-derived chars whereas the pre-exponential factor was assumed to be 1.5 times of that of coal-derived chars. A hierarchical structure of the modified ChB model is illustrated in Figure 36.

3.4.3.3 Structure of the ChB model for coal-biomass blends

The general structure of the modified ChB model for coal-biomass-derived chars is shown in Figure 37. Two sets of data, one for coal and another one from biomass have to be fed to the model as inputs, which include proximate data, calorific values, density, etc. Biomass-derived chars are treated in a similar manner as coal-derived chars. The differences between biomass-derived chars and coal-derived chars lie in their porosity, wall-thickness, overall size and morpho-type.

3.4.3.4 Model validation

One of the main motivations to develop mathematical models is that they could be used as useful tools in process design and optimisation. A good and reliable mathematical model can produce results which are consistent with the actual burnout process, identify possible problems in the process and lead to a better overall understanding of the mechanisms involved.

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Validation, through comparisons with experimental data, is an essential part of development and refinement of the model predictions. A real chemical process is undoubtedly much more complex than mathematical models, which involves complicated transfer of heat, mass and momentum and also involves chemical reactions with exact reaction mechanisms that are not fully understood. For pf coal combustion, it is possible to assess the burnout potential of a coal by testing it in a DTF (Cloke et al., 1997, Cloke et al., 2002b), burning in small scale boilers (Barranco et al., 2006), or even firing in a commercial boiler (Belosevic et al., 2006). The ChB model was initially validated against a set of char re-firing experimental data (Wu et al., 2006b). One of the objectives of this study was to test the modified ChB model against a set of re-firing data. However, due to the limitation of availability of DTF facility, such work was outside the scope of this investigation.

3.4.3.5 Properties of biomass/coal blends and their chars

A widely used UK power station coal, Daw Mill, was used in this study to assess the overall combustion efficiency after biomass addition. Biomasses used in this study were provided by power utilities in the UK who are members of BCURA. Four types of biomasses, CCP, PKE, olive cake and sawdust were used in this study. The properties of Daw Mill coal and the four biomasses are shown in Table 26. In the modified ChB model, Daw Mill coal used was in the range of 75-106 microns, whilst biomass was selected with an average size of 150 microns. The morphological data on Daw Mill coal-derived chars were obtained directly from actual chars via image analysis techniques described earlier. The size for biomass-derived chars was assumed to be 150 microns, not only because biomass is difficult to grind but also because it normally retains its original shape after devolatilisation. The porosity data of biomass-derived chars shown in Table 25 were fed into the model. Since sawdust was not pyrolysed to produce char for morphology studies, there are no average porosity data for sawdust chars. An average porosity of 85% was assumed for sawdust chars due to its high volatile content and low ash content. 4. DISCUSSION OF RESULTS 4.1 Thermal analysis & testing of coal, biomass and blends 4.1.1 Proximate analysis Proximate analysis of the biomass, Table 1, showed that CCP and PKE were quite similar in composition, whereas the wood was higher in volatile matter. In terms of their size fractions, PKE and wood showed the most marked changes in ash content as their size became smaller. The CCP did not show a consistent trend. Changes in volatile matter, [better illustrated by changes in fuel ratio], were most marked for the two CCP samples although both PKE and wood also showed analogous but small similarities.

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Sieve analysis of the coal / biomass mixtures showed [Table 2] that they were coarser than normal PF although the milling process had resulted in significant comminution of the biomass. The quantities of –75 micron materials were ~46% for the coal / PKE mixture [SSE], ~36% for the coal / wood mixture [RWE] and ~69% for the coal / CCP mixture [E.ON UK]. These variations appear to reflect the different grindabilities of the biomasses and also the milling capabilities and the different equipment used to prepare the blends. Table 3 shows the proximate analysis data for the three coal / biomass mixtures and their sieved fractions. All samples showed marked differences in their ash contents with the coarser fractions being lowest. The volatile matter contents of the coarse PKE samples were much higher than in the fine fractions. This signifies that the biomass is concentrated in the coarser size fractions. This trend was less pronounced and less consistent for the other two blends. 4.1.2 Thermogravimetric analysis Thermogravimetric data on the biomass [Table 4] showed that the peak loss temperatures [PLT] did not vary very much between the whole sample and the sized fractions. The burnout temperature [BOT], however did vary significantly. In all cases, the whole sample gave a higher BOT than any of the fractions. The BOT was highest for the PKE [625OC], with CCP some 20OC lower. The wood sample had a BOT of 537OC. The BOT of the fractions showed no systematic trend that could be related to particle size. Table 5 contains the thermogravimetric data on the coal / biomass blends. PLT data of the whole sample and the coarsest fraction for the coal / PKE mixture showed a very large difference. This was caused by the presence of mostly biomass in the coarsest fraction. This difference fell with the finer fractions showing that this mixture is effectively fine coal mixed with coarse biomass. A similar but less pronounced effect is seen in the case of the other two coal / biomass mixtures. 4.2 Determination of biomass content of coal/biomass blends 4.2.1 Source apportionment using TGA data

4.2.1.1 Using peaks to identify components Whilst there is some overlap of peaks around 350-400oC, the peaks are still clearly distinguishable and therefore it should be possible to predict blend proportions using the areas under each profile. Table 27 shows the proximate analysis for the blends and pure components by dividing the profiles at <150oC for moisture content, 150-370oC for Volatiles Region 1, 370 to 900oC for Volatile Region 2 and +900oC [where gas changes to air] for fixed carbon – minus final value for ash %. If Region 1 is attributed to biomass, then the blend proportions can be estimated. These values are given in Table 28 as Region 1/Biomass volatiles. The agreement appears to be close, but the biomass samples with the poorest

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agreement are those with the widest peak profiles, resulting in an overlap into Region 2, attributed as Coal Volatiles. From Table 28 the overlap of coal in Region 1 is low [1.7%] whereas the overlap of biomass volatiles into Region 2 is 18-30%. A second approach is the use of Region 1 and Region 2 data for both pure coal and pure biomass, to correct for the overlap. Biomass proportion = [Region1 – Region1Coal]/[Region1biomass-Region1coal] This data is given in Table 29, and shows that the blend predictions are much improved particularly for the olive cake blends. All of the predictions are now within experimental error.

4.2.1.2 Using profile mapping

The limitation of the techniques described in the previous section is the need to know what biomass is present in the blend. Based on Table 18, size appears to be less of an issue. Prior knowledge of the blends might not always be available, and therefore any technique that does not need to know what biomass is present, as well as the proportion, would be really useful to power generators. Figure 38 shows a set of artificial profiles using a pure biomass sample in the blend at different proportions. The black line is the actual profile generated for the 15% sawdust/coal blend. This process can be achieved for any blend profile using any biomass or coal component profile. The differences between the actual and predicted profiles can be subtracted for any value across the profile. The summed value of the differences across the range of 150-800oC was chosen as it represented the section of the profile generated by the volatile release of biomass and coal components. This represents the differences calculated from more than 2,500 measurements over each samples time/temperature history. Since simulated profiles can be generated for all biomass types at all blend proportions, the system should be able to identify the most likely biomass in the blend as well as the blend proportion based on the profile that has the smallest net difference to the actual profile. Figure 39 shows a plot of absolute difference against blend proportion for each of the four biomass types. Clearly sawdust appears to have the closest fit with 15% having the smallest net difference. This same process can be performed for any of the profiles. Using this method, the predicted proportions are shown in Table 28. As with the Region 1 calculation method used in the previous section, predictions are accurate within experimental error. The added benefit of this mapping method is ability to predict blend proportion without prior knowledge of what biomass is in the blend. 4.3 Drop-tube furnace testing 4.3.1 Using coal / biomass samples from power plant

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Two samples of coal / biomass, obtained from power plant were subjected to DTF analysis. The sample from SSE contained PKE and that from RWEnpower contained sawdust. They were separated into two size fractions, -75+53 microns and -150+106microns. The fractions, together with a similar sized coal from Daw Mill, were passed through the DTF. The DTF residence time was set to 200 milliseconds and the furnace temperature was 1,300OC. The furnace atmosphere was 1% oxygen in nitrogen. The coal feed rate was approximately 0.1g/m and the subsequent chars were collected and weighed. Following the determination of proximate analysis of both coals and chars, it was possible to calculate the collection efficiency of the char Details of the data from the chars and the parent coals / blends are shown in Tables 29 and 30.

4.3.2 Using coal / biomass samples prepared in the laboratory

A series of coal / biomass samples were prepared in the laboratory using sized fractions of the biomass and Daw Mill coal. The selected biomasses were PKE, CCP and olive cake, the size distributions were 53 to 106, 106 to 150 and 150 to 212 microns and the percentage biomass in the blends were 5%, 15% and 30% by weight. Details of the data from the chars and the parent coal / blends are shown in Tables 31, 32 and 33. Intrinsic reactivities of the CCP series of coal biomass blends for 5%, 15% and 30% at a 5OC/minute heating rate are shown in Figure 40.

4.3.3 Using biomass samples

It was necessary to prepare chars from pure biomass, however, feeding them through the DTF proved to be difficult if not impossible. To overcome the problem the biomass was mixed with equal weights of similarly sized silica sand. This proved successful in addressing the feeding problem but difficulties arose in trying to separate the biomass char from the sand particles. Details of the data from the pure coal and biomasses and the sand-containing chars are shown in Tables 34 and 35. Collection efficiency of biomass char samples is shown in Table 36. 4.4 Ash sinter strength studies Boiler fouling of the superheater sections occurs at temperatures of around 1,000OC to 1,400OC and is initiated by the preferential deposition on such surfaces of low-melting compounds, especially alkali metal sulphates formed during combustion in the furnace zone. To be able to identify coals which are likely to cause deposition problems during their combustion, Gibb [2] developed a procedure which used the crushing strength of low temperature ash pellets which had been sintered to a range of appropriate temperatures. The resulting work produced a correlation which was shown to be an acceptably accurate reflection of observed plant data. The chosen crushing

29

strength value was 5MPa and the temperature at which this occurred was known as the Fouling Index, Ts[5]. It is known that biomasses frequently contain elements such as alkalies and alkaline earths which could exacerbate boiler fouling. For this reason it was decided to prepare a series of pellets and test them under similar conditions to those adopted in Gibb’s earlier work. Inspection of the data points did not reveal any consistent plot shape and it was decided that straight-line fits were probably most appropriate. This was different to the findings of Gibb where most coal ashes showed a non-linear or exponential-type curved plot. The plots showed an increase in strength with temperature for most of the blends relative to the ‘pure’ coal ash with the exception of one sample, olive waste. Some of the biomass ashes appear to have a more pronounced increase than others. If one considers Figures 19, 20 and 26, it can be seen that the strength of the coal/biomass ashes builds up strongly. Cereal co-product, Swedish wood and RWE sawdust produced strong pellets at temperatures above about 900OC. A similar but slightly less marked effect is seen in Figures 21, 23, 24 and 25 where the two miscanthus samples, wood pellets and palm kernel expeller produced moderately strong pellets at greater than 900OC. A different behaviour was noted with the data from olive cake and olive waste samples, Figures 22 and 27. In the case of the olive cake there was a rather small increase in pellet strength with temperature which could almost be attributed to experimental variation. The olive waste however, showed a completely different behaviour in that it appeared to become weaker with increasing temperature relative to coal ash alone. It was noticed when the coal / olive waste pellets were prepared they were bulkier than most others and that this would produce a lower density and potentially weaker pellet. After sintering it was also noticed that the appearance of these pellets was more heterogeneous than others and they had begun to deform slightly from their initial cylindrical shape. The ash contribution from olive waste was also higher than for others, but was not the highest. The ash content of the biomasses studied was very variable from low [in the wood-based samples] to high [PKE]. As a result it was decided to calculate the percentage contribution of the biomass ash to the total ash in the blends, [see Table 37]. It is clear from the data that any effect is not simply related to the amount of biomass ash in the blend but also, as might be expected, to the composition of the biomass ash. There are significant differences in the sinter strengths measured for different blend components, however, the ash data in Table 9 does not show any noteworthy variations in composition. The calculation of a Fouling Factor (Skorupska and Crouch, 1993), also shown in Table 9, shows no correlation whatsoever with the sinter strength data, see Table 21. This suggests that the

30

use of ash data alone may be a poor indicator of fouling and sintering in the case of coal / biomass ashes. It was intended to include in the project a brief examination of the specifications currently in use for fly ash use for constructional applications, (BS EN450:1994). However, the status of this specification still seems to be in a state of flux at this time. Furthermore it is stated that a new standard will introduce an extension to the scope to "include fly ashes produced by the co-combustion of coal with other materials" (BS EN206-1/BS8500). In view of this further work at this stage seemed inappropriate as it is quite possible that the current suite of ashes would be suitable for use under one of the newer categories. 4.5. Electrostatic precipitation testing Points to note in Table 22 are that the overall collection efficiencies for fly ashes from the burning of coal-only and coal / biomass are similar. However, the distribution of ash in the three sections of the ESPTF is different for the two types of ash. In the case of the coal / biomass ashes, more material is collected downstream, i.e. in sections 2 and 3, than is the case for coal-only ash. This is probably due to differences in the resistivities of the ashes. The distribution of carbon was also investigated by measuring the LOI of ash taken from different sections of the ESPTF, [see Table 23]. From this it was possible to determine what proportion of the carbon was deposited in the different parts of the ESPTF. As before, two coal ashes are included for comparison. It should be noted that the LOI values of the ashes show considerable differences. The mass balance figures shown for the different sections are calculated from the weight and LOI of the material found in each location. Data on the size distribution of the ash samples is shown in Table 38 and Figures 41 to 43. The data showed that the coal / CPP ash was the finest with more than 99% less than 75 microns. The coal / wood ash contained ~91% and the coal / PKE ash ~58% -75 microns. The coarseness of some of the ash samples indicated the presence of unburnt biomass residues and this is confirmed by the LOI data. The finest ash had ~5% LOI, the intermediate grade contained ~13% and the coarsest contained ~21% LOI. 4.6 Burnout modelling studies In recent years, the co-firing of biomass and coal has been widely adopted by power plants to address their emissions issues (Hughes and Tillman, 1998). Currently, nearly two-thirds of the energy from renewable sources used in Europe comes from biomass and by 2010 biomass is expected to meet as much as 8% of the total EU energy supply (EUR, 2005). In the UK, it is the practice that some 2-4% [thermal basis] of coal is replaced by biomass in large-scale power plants (Backreedy et al., 2005). To meet the increasingly stringent standards on pollutant emissions, it is clear that the rate of co-firing must be increased significantly.

31

Due to the fact that biomasses are from a variety of sources with low energy density and relatively unstable chemical and physical properties, the detailed effects of biomass addition to the process of pulverised coal combustion are yet to be fully understood. Practical operation may affect flame stability, overall combustion efficiency, emissions, slagging and fouling, corrosion and erosion, the quality of ashes, etc. (Baxter and Koppejan, 2005). Under pulverised fuel combustion conditions, coal and biomass particles are rapidly pyrolysed to yield char particles. The physical structure of char has a wide distribution of pore sizes from micro-porous to macro-porous and chars from the same coal can exhibit a range of morphologies and porosities (Bailey et al., 1990, Bend et al., 1992, Rosenberg et al., 1996, Wu et al., 2006a). As discussed in previous sections, char is a very important intermediate combustion product of coal/biomass. It dictates the final stage of coal/biomass combustion and determines the fate of carbon in fly ash (Cloke et al., 2002b), as well as the performance of the ESP’s to remove ash from the flue gas stream. 4.6.1 Discussion of results The burnout profiles of different biomasses blended with Daw Mill coal [sized to -106+75 microns] are shown in Figure 44 to Figure 47. Each of these figures comprises two plots. The left hand plot shows carbon burnout against blend proportion for five different residence times. The left hand plot is combustion temperature against blend proportion for the same residence times. the points on the curves correspond to biomass mass fractions of 0%, 5.0%, 10.0%, 20.0% and 30.0% by weight. Figure 44 shows the co-firing profiles of CCP and Daw Mill coal. It is clear that if there is only 5% CCP being added to coal, there will be a slight drop in the overall carbon conversion, which is associated with a small decrease in the overall combustion temperature. The reduction in combustion efficiency will increase with the increase in biomass proportion. According to the modelling results, if the mass fraction of CCP is 30%, after two seconds in a typical pf-fired boiler, the overall carbon conversion is around 90.5%. Assuming complete devolatilisation, the average carbon content in the remaining fly ash will be:

CarbonUnburntbiomassinAshcoalinAshCarbonUnburntashinCarbon

⋅+⋅⋅+⋅⋅⋅

=⋅⋅

)905.00.1()166.03.0534.07.0(041.03.0093.07.0

)905.00.1()166.03.0534.07.0(−××+×+×+×

−××+×=

%30= Such a high content of carbon in fly ash may be attributed to the low carbon conversion resulting from the drop in char reactivity. Some 9.5% of the carbon in biomass-coal-derived chars was left unburnt, which will make the carbon in fly ash very high especially when the ash content in the original blend is low. Obviously, such a high unburnt carbon in ash will limit the utilisation of such ash as Type I additives since according to British Standards on the specification of fly ash to be used as a Type I addition, the loss on ignition [LOI] should not exceed 12.0 % [wt/wt basis] (BS, 1996). To meet this

32

requirement, the overall carbon conversion efficiency should be around 97.0%, which seems unlikely based on a 30% CCP/Daw Mill blend according to the modified ChB model outputs. The reason is that CCP has a very low calorific value, which will lead to a relatively low combustion flame temperature. Lower combustion temperature normally means lower char reactivity and a slower heating-up rate. For conventional coal-fired utility boilers, the residence time is in the range of one to two seconds (Smoot, 1993); therefore, a lower char reactivity will undoubtedly result in a poorer char burnout. Predicted burnout profiles for Daw Mill and PKE-derived chars are shown in Figure 45. There is not much difference between Daw Mill/PKE blend and Daw Mill/CCP blend compared with Figure 44. From what was obtained in Table 24, the average porosity of PKE [79%] derived chars is similar to that of CCP derived chars [70%]. However, the calorific value of PKE is slightly higher than CCP, so the overall carbon conversion efficiencies for PKE are all slightly higher than their CCP/Daw Mill counterparts. Figure 46 plots predicted burnout profiles for Daw Mill and sawdust-derived chars. Morphologically, sawdust-derived chars are normally more porous than the other biomass-derived chars in this study. Sawdust has a very high volatile matter content and a very low ash content, which might explain why it can form thinner walled vesiculated chars during pyrolysis. Highly porous chars would contribute to the fast conversion of carbon in char and therefore the impact of sawdust blending would be expected to be less. However, since the fixed carbon content of sawdust is relatively low [13.2%], its contribution to higher carbon conversion efficiency is limited. In the meantime, the moisture content of sawdust is the highest among all the biomasses investigated in this study. Such a high moisture content makes the calorific value of sawdust relatively low [19,334kJ/kg] due to the evaporation of moisture reducing the amount of useful heat. Sawdust, with its higher calorific value [than CCP and PKE] and higher char porosity appears to only produce a slight drop in temperature before char combustion starts. The overall carbon conversion [94.2%] has not dropped too much even with 30.0% blends. Figure 47 shows the predicted burnout profiles of Daw Mill/Olive Cake against pure Daw Mill. As seen from Table 25, Olive Cake has the highest CV among the four biomasses investigated, and the lowest moisture content. The calorific value of Olive Cake [23,004kJ/kg] is still much lower than that of Daw Mill coal [32,820kJ/kg]. The modified ChB model still predicts a slight drop in combustion temperature before char combustion starts, although the change is almost negligible and does not impact much on char reactivity. For the highest biomass additions of 30.0% olive cake, as seen from Figure 47, a drop of around 20ºC in flame temperature was predicted, which will produce a slightly lower char reactivity. However, this deficiency was compensated for by the heat released at the first 60ms from the fast combustion of the highly porous char material.

Burnout data is also presented in Figures 48 to 51. In this case carbon burnout is plotted against elapsed time. Each plot contains five curves corresponding to

33

0%, 5%, 10%, 20% and 30% biomass combined with Daw Mill coal. The plots are for CCP, olive cake, PKE and sawdust. The deviation of the curves from that of 0% biomass shows the extent to which the biomass is having an effect on burnout with time. The closest similarity to coal-only is found with olive cake whilst the plots from CCP show the biggest deviation. This indicates that olive cake seems to have the least effect on burnout (up to 30% by weight) and that CCP has the most effect. The other two biomasses that were modelled were intermediate in behaviour.

4.6.2 Observations In this section, chars from three biomass and one coal sample were made using both DTF and a slow pyrolysis method. Char morphology studies were carried out using SEM and optical microscopy. The automated char image analysis technique developed at Nottingham (Wu, 2004, Wu et al., 2006a) were used to characterise char morphology. The important observations which can be drawn from this work are:- The small size biomass DTF chars were highly porous and some molten ash/ash-rich particles formed on the surface of the chars. The biomass chars contains a large proportion of light-coloured particles [compared to carbon char] rich in Si, P, Ca, and K. The size or aspect ratio of large sized slow pyrolysed chars was not significantly different from the original particles and limited fragmentation occurred during devolatilisation, which is useful when modelling biomass combustion. Grain size prior to combustion can be adapted [linking into actual swelling feature] in order to predict char size. In terms of morphology, Daw Mill coal in large sized fractions normally forms thick-walled networks [crassi-network], whilst its particles in small size range [-106+75 microns] thick-walled spheres dominate char formation. The morphology of coal-derived chars does not change much whatever the nature or proportion of the biomass. CCP and olive cake normally form crassi-network chars, whilst PKE yields a large percentage of thin-walled chars. When co-pyrolysed with Daw Mill coal, it seems that chars formed are slightly more porous than chars from pure biomasses. In addition, the char burnout kinetic model [ChB] has been adopted and modified to assess the potential for co-firing biomass-coal blends. Such a modified model distinguishes itself from others by introducing a sub-model to consider the impacts of biomass-derived char morphology on the burnout process. The addition of biomass to the coal creates a drop in combustion flame temperature as char combustion begins. The reason for such a drop is that biomass normally has a much lower calorific value compared to the typical coal fired in power plants. Generally, char morphology plays a significant role in determining the fate of carbon (Hurt et al., 1998, Wu et al., 2006b). However, due to the difference in

34

morphology among biomass-derived chars is not significant and the amount of chars yielded from biomass is small, the impact caused by the difference in morphology of biomass-derived char can be negligible, compare to the impact caused by biomass addition on the flame temperature drop. The proportion of biomass in blends needs to be carefully controlled in order to avoid high unburnt carbon in ash. Even though the intrinsic reactivity of chars derived from coal-biomass blends are higher than those derived from pure coal (Sjostrom et al., 1999), the improvement in reactivity may not compensate for the loss in reactivity due to a decreased combustion temperature. From the predicted burnout performance olive cake has the lowest impact on burnout performance due to its high calorific value. It is clear that though the modified ChB model needs further validation using re-firing data collected from DTF or data collected from small scale/full scale boilers. Once validated, the model may become a useful tool for the selection and blending of different biomass types. 5. CONCLUSIONS Past experience has shown that complete burnout of biomass-derived chars in blends with coal up to 10% by weight is generally not a concern under normal PF combustion conditions. This project has applied more precision to what actually happens by developing and using an existing burnout model [BCURA B58] for such blends. In brief, the findings for this and other aspects of the work were:- i The ChB model predicted that when burnt as a blend with coal, the lower heat content of biomass compared with coal would result in a reduction in operating temperature in the furnace. ii The ChB model also predicted that the higher the biomass heat content, the smaller would be the likelihood of the biomass affecting the overall burnout efficiency. iii Large biomass particles, i.e. greater than 1mm, could remain as unburnt carbon in both fly ash and bottom ash, although biomass type and particle shape would also have an effect. iv Because of the variability in burner arrangement, boiler size and operating regime, it is not possible at this time to state what the maximum size of biomass particle should be in order to give an acceptable degree of burnout. v Care should be taken when firing high biomass content blends that the boiler can accommodate the increased fuel volume. vi Ash sinter strength measurements for most of the biomass / coal ashes showed them to be more prone to fouling than for coal alone.

35

vii The increase in fouling tendency was not dependent upon the ash content of the biomass but seemed to be more related to ash composition. viii The sinter strength method described in this project appears to be better able to discriminate between the effect of different biomass / coal ash composition and fouling tendency than another compositionally based method reported in the technical press. ix Analysis of the compositional data suggests that in many cases, and at the levels tested, the ashes would still be saleable and would comply to the latest specifications. It is recognised that these specifications are under review and a category for co-fired ashes is under consideration. x Electrostatic precipitation testing of fly ash from coal and coal / biomass blend firing found that the overall collection efficiencies were largely similar. However, the distribution of collected ash in the sections of the ESPTF was different. More ash from burning coal / biomass blends was found downstream than was the case for the coal-only ashes. A similar 'delayed' collection of the unburnt carbon was also noted. The differences in behaviour are attributable to differences in the resistivities of the different ashes. xi In addition to the completion of the original work programme there has been the development of an innovative method which can determine the biomass content of any coal / biomass blend. This data is unobtainable by any other means and the methodology has already been published in the scientific press. It will provide the generators with a new tool and has already been successfully 'blind-tested' by one of the industrial partners. 6. PROPOSED WORK FOR A SUBSEQUENT PROGRAMME It is clear from the findings that the modelling work shows promise and that its further development, including the validation of the ChB model, would be very worthwhile. The opportunity for power utilities to use direct injection of biomass into their furnaces is available now. The higher amounts of biomass which can be injected by this route, however, open up a series of questions which need to be answered. These include, for example, more significant changes to the composition, and hence quality, of the fly ash; the extent and severity of furnace deposition and possibly enhanced high temperature corrosion. Furthermore, as some of the proposed energy crops are known to be high in nitrogen content, a study on the evolution of volatile matter, including nitrogen, from these fuels would be useful in terms of understanding their NOx potential. Additional work is also needed to establish the maximum particle size for biomass in specific boiler applications with the higher utilisation scenario as

36

well as combustion performance in terms of ignition, flame stability and carbon burnout. 7. PUBLICATIONS ARISING FROM PROJECT LESTER, E., GONG, M., & THOMPSON, A.W., [2007] "A method for source apportionment in coal/biomass blends using thermogravimetric analysis", Journal of Analytical and Applied Pyrolysis, doi:10.1016/j.jaap.2007.01.10 WU, T., GONG, M., & LESTER, E., "Inclusion of char morphological information in biomass-coal gasification modelling", accepted for oral presentation at International Coal Chemistry & Science Conference 2007, Nottingham, (journal submission in preparation). GONG, M., & LESTER, E., "The impact of fly ash from biomass-coal co-firing on the performance of an electrostatic precipitator", (journal submission in preparation). WU, T., GONG, M., & LESTER, E., "A new classification system for biomass-derived chars", (journal submission in preparation). 8. REFERENCES ALVAREZ, D., BORREGO, A. G. & MENENDEZ, R. [1997] Unbiased methods for the

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WU, T. [2004] Automated char image analysis and the inclusion of char morphology in char burnout modelling. School of Chemical, Environmental and Mining Engineering. Nottingham, The University of Nottingham.

WU, T., LESTER, E. & CLOKE, M. [2006a] Advanced Automated Char Image Analysis Techniques. Energy Fuels, 20, 1211-1219.

WU, T., LESTER, E. & CLOKE, M. [2006b] A Burnout Prediction Model Based around Char Morphology. Energy & Fuels.

40

9. LIST OF TABLES 1. Proximate analyses of biomass samples [air-dried] 2. Sieve analysis of coal / biomass mixtures 3. Proximate analyses of coal / biomass mixtures 4. TGA data on biomass samples 5. TGA data on coal / biomass blends 6. Proximate analyses of coal and biomass samples in different size fractions 7. Theoretical proximate analyses of coal/biomass mixtures with different biomass

contents 8. Experimental proximate analyses of coal/biomass mixtures with different

biomass contents 9. Comparative ash analysis for Daw Mill coal and various blends with biomass 10. Proximate analysis of biomass and coal used in modelling activity 11. Proximate analyses of small biomass/coal blends in three size fractions 12. Proximate analysis of biomass and coal DTF chars used in modelling activity 13. Proximate analyses of DTF chars 14. Proximate analysis of biomass and coal pyrolysis chars used in modelling activity 15. Proximate analysis of large biomass in blends with coal by slow pyrolysis 16. Proximate analyses data for biomass samples and coal sample in three different

sizes 17. Peak Profile Data for individual components in three sizes 18. Proximate analysis data for blends of individual components <125>106 microns 19. Crushing strength data 20. Standard Deviation [%] between crushing strengths of individual pellets 21. Differences between datum coal ash and Ts[5] for coal / biomass blends 22. Mass balance of ash in ESPTF Coal / biomass and coal only ashes 23. Mass balance of LOI in ESPTF 24. Average porosity of the slow pyrolysed chars shown in Figure 30 25. Average porosity of pure biomass and coal samples in large size fractions 26. Properties of coal and biomasses 27. Proximate analysis data for individual blends divided by peak/regions 28. Blend Proportions [wt%] based on different prediction methods 29. Proximate analysis data on DTF samples 30. DTF Collection efficiency of char samples in different size fractions 31. Proximate analyses data for coal/biomass mixtures. 32. Proximate analyses data of DTF chars from coal/biomass blends 33. DTF Collection efficiency of coal/biomass blend char samples 34. Proximate analyses of biomass and coal samples in different size fractions 35. Proximate analyses of sand-containing chars and coal char 36. DTF Collection efficiency of biomass char samples

37. Contribution to overall ash content of blends and TS[5] values 38. Size distribution and LOI of fly ashes

41

10. FIGURES & APPENDIX

1. Schematic of electrostatic precipitation test facility 2. Close-up of DTF CCP chars using SEM 3. Close-up of DTF PKE chars using SEM 4. Close-up of DTF olive cake chars using SEM 5. Surface features of biomass DTF chars using SEM (106-150µm) 6. Surface features of slow pyrolysed CCP chars using SEM 7. Surface features of slow pyrolysed olive cake chars using SEM 8. Surface features of slow pyrolysed PKE chars using SEM 9. TGA plot of coal/cereal blends, 5% to 20%, 5OC/m 10. TGA profiles for olive cake [106-125µ] at different heating rates 11. The change in Peak Temperatures with different heating rates [olive cake at

106-125µ] 12. TGA profiles for Daw Mill coal [106-125µ] at different heating rates 13. TGA profiles for 10% olive cake: 90% Daw Mill coal [wt:wt%] at 106-125µ at

different heating rates 14. TGA profiles for different components [106-125µ] at 5OC/minute 15. PKE / Daw Mill coal blends at 0,5,10,15,20% PKE: coal [wt%] 16. Sawdust / Daw Mill coal blends at 0,5,10,15,20% sawdust: coal [wt%] 17. Cereal /Daw Mill coal blends at 0,5,10,15,20% cereal: coal [wt%] 18. Olive cake / Daw Mill coal blends at 0,5,10,15,20% olive cake: coal [wt%] 19. Comparative crushing strength - Daw Mill coal ash / sawdust ash 20. Comparative crushing strength - Daw Mill coal ash / Swedish wood ash 21. Comparative crushing strength - Daw Mill coal ash / miscanthus pellet ash 22. Comparative crushing strength - Daw Mill coal ash / olive cake ash 23. Comparative crushing strength - Daw Mill coal ash / wood pellet ash 24. Comparative crushing strength - Daw Mill coal ash / PKE ash 25. Comparative crushing strength - Daw Mill coal ash / chopped miscanthus ash 26. Comparative crushing strength - Daw Mill coal ash / cereal co-product ash 27. Comparative crushing strength - Daw Mill coal ash / olive waste ash 28. Typical datalogger plot of crushing strength of sintered ash pellet 29. Schematic of char image analysis procedure

30. A representation of biomass/coal chars 31. Char morphology distributions of pure biomass/coal-derived chars

32. Morphology distributions of CCP/coal chars 33. Morphology distributions of PKE/coal chars 34. Morphology distributions of olive cake/coal chars 35. General procedure of pulverised coal combustion process 36. Illustration of modified ChB model

37. Main program structure of ChB (after (Wu, 2004)) 38. Theoretical Blend Profiles generated from mapping individual component

profiles [coal:sawdust 85%:15% wt%] 39. Absolute Variance of Predicted Blend Profile and Actual Blend Profile 40. Intrinsic analysis of CCP char samples [150-212µ] 41. Size distribution of ESPTF fly ash-coal / PKE ash sample 42. Size distribution of ESPTF fly ash-coal / wood ash sample 43. Size distribution of ESPTF fly ash-coal / CCP ash sample 44. Burnout of CCP/Daw Mill Blends (BO and comb.temp. vs blend proportion) 45. Burnout of PKE/Daw Mill blends (BO and comb.temp. vs blend proportion) 46. Burnout of sawdust/Daw Mill blends (BO and comb.temp. vs blend proportion) 47. Burnout of olive cake/Daw Mill blends (BO and comb.temp. vs blend proportion) 48. Burnout of CCP/Daw Mill Blends (burnout vs time) 49. Burnout of olive cake/Daw Mill blends (burnout vs time) 50. Burnout of PKE/Daw Mill blends (burnout vs time) 51. Burnout of sawdust/Daw Mill blends (burnout vs time) Appendix 1. Database of samples received

42

TABLES

Size Fraction Moisture wt (%)

Ash wt (%)

Volatile matter wt (%)

Fixed carbon wt (%)

Fuel Ratio

PKE (SSE) Whole sample 4.87 5.51 71.71 17.91 0.25 >212 microns 4.11 3.73 72.47 19.69 0.27 <212>150 microns 4.94 5.43 71.68 17.95 0.25 <150>106 microns 5.13 5.61 72.29 16.98 0.23 <106 microns 5.03 8.46 69.86 16.65 0.24

CCP (E.ON - Kingsnorth) Whole sample 5.62 5.00 71.97 17.41 0.24 >212 microns 5.44 5.16 71.66 17.75 0.25 <212>150 microns 5.67 4.65 72.60 17.09 0.24 <150>106 microns 5.86 4.14 73.45 16.55 0.23 <106>75 microns 6.05 4.16 73.39 16.40 0.22 <75>53 microns 6.23 3.35 75.39 15.04 0.20 <53>38 microns 6.57 4.29 75.11 14.04 0.19

CCP (E.ON - PT) Whole sample 6.44 6.18 70.28 17.10 0.24 >212 microns 5.85 6.45 70.84 16.85 0.24 <212>150 microns 6.04 4.53 72.34 17.10 0.24 <150>106 microns 6.32 4.14 73.62 15.92 0.22 <106>75 microns 6.42 4.10 73.26 16.22 0.22 <75>53 microns 6.66 4.14 74.65 14.56 0.20 <53 microns 7.04 4.67 75.81 12.48 0.16

Wood (RWE) Whole sample 5.95 1.25 78.54 14.26 0.18 >212 microns 6.33 0.37 78.94 14.37 0.18 <212>150 microns 6.35 0.53 79.01 14.12 0.18 <150>106 microns 6.69 0.58 79.53 13.20 0.17 <106>75 microns 6.65 0.88 78.96 13.53 0.17 <75 microns 6.39 1.51 79.56 12.54 0.16

Table 1. Proximate analyses of biomass samples (air-dried)

PKE ex-SSE (Ferrybridge)

wt (%)

Wood ex-RWE wt (%)

CCP ex-E.ON UK (PT)

wt (%) >500 microns 0.00 0.00 0.00 >212 microns 2.61 5.35 3.85 <212>150 microns 9.60 16.15 6.44 <150>106 microns 18.45 21.26 9.19 <106>75 microns 23.16 20.86 11.77 <75>53 microns 17.76 15.90 13.80 <53>38 microns 12.66 10.58 16.43 <38 microns 15.77 9.90 38.52

Table 2. Sieve analysis of coal / biomass mixtures

43

Size Fraction Moisture Ash Volatile matter Fixed carbon Fuel Ratio

wt (%) wt (%) wt (%) wt (%)

ex-SSE Whole sample 2.58 13.07 33.27 51.08 1.54 >212 microns 3.81 7.12 54.24 34.83 0.64 <212>150 microns 3.14 8.46 41.05 47.35 1.15 <150>106 microns 3.10 11.43 32.59 52.88 1.62 <106>75 microns 3.16 11.55 31.47 53.82 1.71 <75>53 microns 2.99 13.43 30.52 53.06 1.74 <53>38 microns 2.77 13.51 30.02 53.70 1.79 <38 microns 2.54 16.32 28.95 52.19 1.80

ex-RWE

Whole sample 2.50 11.52 33.21 52.77 1.59 >212 microns 2.49 7.04 35.37 55.10 1.56 <212>150 microns 2.67 7.27 34.89 55.17 1.58 <150>106 microns 2.75 7.13 34.60 55.52 1.60 <106>75 microns 3.60 8.10 33.80 54.50 1.61 <75>53 microns 3.51 10.49 33.37 52.64 1.58 <53>38 microns 3.45 12.14 32.37 52.04 1.61 <38 microns 3.32 17.74 30.21 48.73 1.61

ex-E.ON UK (PT)

Whole sample 2.85 9.18 35.37 52.61 1.49 >212 microns 2.46 7.82 37.15 54.16 1.46 <212>150 microns 2.75 5.06 37.47 54.71 1.46 <150>106 microns 4.49 5.41 37.11 52.99 1.43 <106>75 microns 3.30 7.45 35.86 53.38 1.49 <75>53 microns 3.22 8.38 36.10 52.31 1.45 <53>38 microns 2.67 8.74 34.57 54.02 1.56 <38 microns 2.88 11.11 33.91 52.11 1.54

Table 3. Proximate analyses of coal / biomass mixtures

44

Size Fraction Peak loss temperature

(OC) Burnout temperature

(OC)

PKE – (SSE) Whole sample 303 625 >212 microns 303 567 <212>150 microns 301 553 <150>106 microns 301 511 <106>75 microns 299 540 CCP - (E.ON Kingsnorth) Whole sample 303 602 >212 microns 301 565 <212>150 microns 301 562 <150>106 microns 303 562 <106>75 microns 302 570 <75>53 microns 301 576 <53>38 microns 303 555 CCP - (E.ON PT) Whole sample 300 604 >212 microns 298 584 <212>150 microns 302 552 <150>106 microns 303 569 <106>75 microns 302 568 <75>53 microns 303 586 <53>38 microns 303 577 Wood - (RWE) Whole sample 360 537 >212 microns 346 501 <212>150 microns 348 510 <150>106 microns 346 504 <106>75 microns 344 501 <75>53 microns 343 492

Table 4. TGA data on biomass samples

45

Size Fraction Peak Loss

Temperature (PLT)

Difference,

Burnout Temperature

(BOT)

Difference,

(OC) whole:fraction (OC) whole:fraction Coal/biomass (ex-SSE) (OC) (OC) Whole sample 605 ~ 697 ~ >212 microns 303 302 631 66 <212>150 microns 303 302 662 35 <150>106 microns 575 30 672 25 <106>75 microns 573 32 660 37 <75>53 microns 567 38 648 49 <53>38 microns 559 46 642 55 <38 microns 558 47 629 68 Coal/biomass (ex-RWE) Whole sample 620 ~ 723 ~ >212 microns 505 115 667 56 <212>150 microns 487 133 657 66 <150>106 microns 489 131 646 77 <106>75 microns 564 56 649 74 <75>53 microns 540 80 641 82 <53>38 microns 532 88 643 80 <38 microns 513 107 614 109 Coal/biomass (ex-E.ON) Whole sample 566 ~ 709 ~ >212 microns 493 73 680 29 <212>150 microns 479 87 659 50 <150>106 microns 595 -29 649 60 <106>75 microns 550 16 644 65 <75>53 microns 536 30 626 83 <53>38 microns 527 39 610 99 <38 microns 516 50 594 115

Table 5. TGA data on coal / biomass blends

46

Material Size range (microns)

Moisture, (%)

Volatile matter,

(%)

Fixed carbon,

(%)

Ash, (%)

PKE <75>53 5.91 71.61 15.53 6.95 PKE <150>106 5.98 74.08 14.83 5.10 PKE <212>150 5.93 74.17 14.86 5.04 Sawdust <75>53 4.98 80.11 13.33 1.57 Sawdust <150>106 4.27 81.10 13.41 1.23 Sawdust <212>150 3.83 80.90 13.80 1.47 Olive cake <75>53 4.59 64.12 20.84 10.45 Olive cake <150>106 4.57 64.54 21.09 9.80 Olive cake <212>150 4.58 63.50 22.52 9.40 Cereal <75>53 7.80 72.92 15.36 3.92 Cereal <150>106 6.53 71.15 18.36 3.96 Cereal <212>150 6.48 71.70 17.60 4.22 Daw Mill coal <75>53 3.32 33.25 55.57 7.86 Daw Mill coal <150>106 3.41 32.63 54.13 9.83 Daw Mill coal <212>150 5.10 35.14 55.17 4.59

Table 6. Proximate analyses of coal and biomass samples

in different size fractions

Sample (%) Moisture,

(%) Volatile

matter, (%) Fixed carbon,

(%) Ash, (%)

PKE 5 3.54 34.70 52.17 9.59 PKE 10 3.67 36.78 50.19 9.36 PKE 15 3.80 38.85 48.23 9.12 PKE 20 3.92 40.92 46.27 8.89

Sawdust 5 3.46 35.05 52.09 9.40 Sawdust 10 3.50 37.48 50.05 8.97 Sawdust 15 3.54 39.90 48.02 8.54

Sawdust 20 3.58 42.32 45.99 8.11

Olive cake 5 3.47 34.23 52.48 9.83 Olive cake 10 3.53 35.82 50.82 9.83 Olive cake 15 3.58 37.42 49.17 9.83

Olive cake 20 3.65 39.01 47.51 9.83

Cereal 5 3.57 34.56 52.33 9.54 Cereal 10 3.73 36.48 50.54 9.25 Cereal 15 3.88 38.41 48.76 8.95 Cereal 20 4.03 40.33 46.98 8.66

Table 7. Theoretical proximate analyses of coal/biomass mixtures

with different biomass contents

47

Sample (%) Moisture,

(%) Volatile

matter, (%) Fixed carbon,

(%) Ash, (%)

PKE 5 4.74 33.69 52.86 8.71 PKE 10 4.18 36.55 51.12 8.15 PKE 15 4.30 38.74 48.90 8.06

PKE 20 4.52 40.39 47.84 7.25

Sawdust 5 3.70 33.54 52.13 10.63 Sawdust 10 3.72 36.84 50.01 9.42 Sawdust 15 4.28 39.23 47.75 8.74 Sawdust 20 4.58 41.37 45.13 8.92

Olive cake 5 4.07 33.76 52.35 9.82 Olive cake 10 4.12 34.53 50.93 10.42 Olive cake 15 4.10 36.24 49.32 10.34

Olive cake 20 4.00 38.07 47.18 10.75

Cereal 5 3.78 35.04 53.20 7.98 Cereal 10 3.86 37.08 51.18 7.88 Cereal 15 4.13 38.76 49.31 7.80 Cereal 20 4.53 40.37 47.17 7.93

Table 8. Experimental proximate analyses of coal/biomass mixtures

with different biomass contents by TGA

48

Element, (%, as oxides)

Daw Mill coal ash

Daw Mill, (90%) + RWE sawdust

(10%)

Daw Mill, (90%) + RWE Swedish

wood (10%)

Daw Mill, (90%) + wood pellets (10%)

Daw Mill, (90%) + olive waste (10%)

Daw Mill, (90%) + olive cake (10%)

Daw Mill, (90%) + cereal co-

product

Daw Mill, (90%) + palm kernel expeller (10%)

Daw Mill, (90%) + chopped miscanthus

(10%)

Daw Mill, (90%) + miscanthus pellets

(10%)

Silicon, as SiO2 43.17 42.59 42.38 42.61 39.09 41.5 43.00 42.66 44.56 43.19Aluminium, as Al2O3 28.02 26.28 26.36 26.77 24.26 25.39 26.58 26.68 25.49 26.39Titanium , as TiO2 1.20 0.98 1.00 1.03 1.00 0.95 1.08 0.97 0.98 0.97

Iron, as Fe203 6.12 7.42 6.85 6.89 5.99 6.75 6.58 6.66 6.63 6.32Calcium, as CaO 3.62 3.58 3.88 3.41 5.40 3.83 3.23 3.48 3.61 3.50Magnesium, as MgO 3.00 3.22 3.28 3.45 3.65 3.33 3.17 3.37 3.22 3.38Potassium, as K2O 2.36 2.47 2.67 2.51 4.70 4.04 2.99 3.12 2.64 2.65Sodium, as Na2O 2.68 2.48 2.48 2.59 2.51 2.40 2.45 2.44 2.44 2.55

B/A 0.25 0.27 0.27 0.27 0.35 0.30 0.26 0.27 0.26 0.26

B/A x Na2O 0.66 0.68 0.68 0.69 0.87 0.72 0.64 0.66 0.64 0.66

Table 9. Comparative ash analysis for Daw Mill and various blends with biomass (data on Fouling Index included)

49

Sample Size Moisture, (%)

Ash, (%) Volatile matter, (%)

Fixed carbon, (%)

CCP 53–106um 5.5 4.3 71.2 18.9

106-150um 4.5 5.0 71.0 19.5

150-212um 4.5 4.9 70.8 19.8

0.5-1.0mm 5.8 7.5 73.2 13.5

1.0-2.0mm 6.2 7.0 72.3 14.5

2.0-2.8mm 5.9 6.8 71.8 15.5

Olive cake 53–106um 2.6 10.8 64.2 22.4

106-150um 2.4 9.5 64.9 23.2

150-212um 2.1 8.8 65.5 23.6

0.5-1.0mm 2.9 14.1 64.6 18.5

1.0-2.0mm 2.7 10.7 67.4 19.3

2.0-2.8mm 1.6 7.7 69.3 21.3

PKE 53–106um 3.7 5.9 72.3 18.1

106-150um 3.5 5.9 71.6 19.0

150-212um 3.1 5.8 71.9 19.1

0.5-1.0mm 3.4 7.2 72.0 17.4

1.0-2.0mm 2.8 6.5 72.5 18.2

2.0-2.8mm 1.8 5.1 73.1 19.9

Daw Mill coal

53–106um 2.1 13.8 31.8 52.3

106-150um 2.0 10.1 32.6 55.3

150-212um 1.8 13.0 30.1 55.1

0.5-1.0mm no data no data no data no data

1.0-2.0mm no data no data no data no data

2.0-2.8mm no data no data no data no data

Table 10. Proximate analysis of biomass and coal used in modelling activity

50

Type Size (µm)

Biomass proportion

(%)

Moisture (%)

Volatiles (%)

Fixed carbon (%)

Ash (%)

5 2.3 33.8 50.7 13.3

15 2.6 37.7 47.3 12.4 -106 +53

30 3.1 43.6 42.3 11.0

5 2.1 34.5 53.5 9.8

15 2.3 38.4 50.0 9.3 -150 +106

30 2.7 44.1 44.6 8.6

5 2.0 32.1 53.4 12.6

15 2.2 36.2 49.8 11.7

CCP

-212 +150

30 2.6 42.3 44.5 10.5

5 2.1 33.4 50.8 13.6

15 2.2 36.6 47.9 13.3 -106 +53

30 2.2 41.5 43.4 12.9

5 2.0 34.2 53.7 10.0

15 2.0 37.5 50.5 10.0 -150 +106

30 2.1 42.3 45.7 9.9

5 1.8 31.9 53.5 12.8

15 1.9 35.4 50.4 12.3

Olive cake

-212 +150

30 1.9 40.7 45.6 11.7

5 2.2 33.8 50.6 13.4

15 2.3 37.9 47.2 12.6 -106 +53

30 2.6 43.9 42.1 11.4

5 2.0 34.6 53.5 9.9

15 2.2 38.5 49.9 9.5 -150 +106

30 2.4 44.3 44.4 8.8

5 1.9 32.2 53.3 12.6

15 2.0 36.4 49.7 11.9

PKE

-212 +150

30 2.2 42.6 44.3 10.8

Table 11. Proximate analyses of small biomass/coal blends in three size

fractions

51

Sample Size Moisture,

(%) Ash, (%) Volatile

matter, (%)

Fixed carbon, (%)

CCP 53–106um 1.8 7.6 7.3 83.3

106-150um 1.5 8.2 6.2 84.1

150-212um 1.7 7.9 7.8 82.6

0.5-1.0mm 2.2 18.3 37.3 42.2

1.0-2.0mm 3.1 14.7 33.9 48.3

2.0-2.8mm 2.8 13.4 32.8 51.0

Olive cake 53–106um 1.9 17.5 6.9 73.7

106-150um 2.1 16.3 7.5 74.1

150-212um 1.6 16.9 8.1 73.4

0.5-1.0mm 4.7 10.2 61.5 23.6

1.0-2.0mm 4.4 5.9 67.3 22.5

2.0-2.8mm 1.4 9.8 66.8 22.0

PKE 53–106um 1.2 8.5 8.0 82.3

106-150um 0.9 8.9 7.1 83.1

150-212um 1.3 10.2 8.2 80.3

0.5-1.0mm 3.5 28.0 50.4 18.1

1.0-2.0mm 2.5 10.0 69.6 18.0

2.0-2.8mm 1.4 7.4 69.8 21.4

Daw Mill coal

53–106um 2.1 29.0 6.0 63.0

106-150um 1.3 22.9 5.2 70.6

150-212um 1.2 21.7 6.0 71.1

0.5-1.0mm no data no data no data no data

1.0-2.0mm no data no data no data no data

2.0-2.8mm no data no data no data no data

Table 12. Proximate analysis of biomass and coal DTF chars used in

modelling activity

52

Type Size (µm)

Biomass proportion

(%)

Moisture (%)

Volatiles (%)

Fixed carbon

(%)

Ash (%)

5 1.3 6.0 68.7 24.0 15 0.8 7.1 65.5 26.6

-106 +53

30 1.0 7.1 68.5 23.5 5 1.0 6.4 64.1 28.5 15 0.8 5.9 74.8 18.5

-150 +106

30 1.0 7.8 75.1 16.0 5 1.6 6.7 63.5 28.2 15 1.8 6.4 70.9 20.9

CCP

-212 +150

30 1.4 8.2 68.5 21.8 5 0.1 5.0 73.6 21.4 15 1.9 6.5 68.3 23.3

-106 +53

30 2.5 10.8 65.6 21.0 5 2.1 4.1 65.0 28.7 15 1.3 6.3 67.8 24.6

-150 +106

30 1.7 6.1 66.7 25.4 5 2.5 4.9 60.5 32.1 15 1.9 6.9 63.8 27.4

Olive cake

-212 +150

30 2.0 5.8 67.6 24.6 5 0.6 4.2 73.0 22.2 15 0.9 5.0 78.2 15.9

-106 +53

30 1.6 6.4 76.0 16.0 5 1.4 6.9 54.4 17.3 15 0.8 5.7 71.3 22.2

-150 +106

30 0.6 6.6 67.7 25.1 5 0.7 4.7 67.4 27.3 15 1.1 5.8 72.4 20.7

PKE

-212 +150

30 0.9 7.2 68.1 23.8

Table 13. Proximate analyses of DTF chars

53

Moisture,

(%) Ash, (%) Volatile

matter, (%)

Fixed carbon, (%)

CCP 0.5-1.0mm

2.2 28.3 27.3 42.2

1.0-2.0mm

2.9 25.2 24.6 47.3

2.0-2.8mm

1.5 23.4 24.1 51.0

Olive cake 0.5-

1.0mm 3.5

26.3 23.3 46.9

1.0-2.0mm

2.9 22.6 18.3 56.2

2.0-2.8mm

2.5 25.2 16.7 55.7

PKE 0.5-

1.0mm 2.8 19.4 14.2 63.7

1.0-2.0mm

2.2 14.7 8.0 75.1

2.0-2.8mm

1.2 18.4 13.8 66.6

Daw Mill coal

0.5-1.0mm

0.5 21.1 4.6 73.8

1.0-2.0mm

0.6 20.1 3.9 75.5

2.0-2.8mm

0.4 19.5

3.9 76.1

Table 14. Proximate analysis of biomass and coal pyrolysis chars used in

modelling activity

54

Type Size

(mm)

Biomass proportion

(%)

Moisture (%)

Volatiles (%)

Fixed carbon

(%)

Ash (%)

10 1.2 6.3 75.9 16.6 20 1.4 5.1 80.4 13.2 0.5-1.0 30 1.4 5.5 76.9 16.2 10 1.9 5.4 79.8 13.0 20 1.9 6.0 77.2 14.9 1.0-2.0 30 1.1 4.8 78.0 16.1 10 1.7 6.7 75.8 15.8 20 1.8 7.9 72.5 17.8

CCP

2.0-2.8 30 1.2 5.1 77.9 15.8 10 1.4 4.9 77.1 16.7 20 1.1 6.3 75.8 16.8 0.5-1.0 30 1.1 5.4 75.5 18.0 10 1.9 5.5 79.8 12.8 20 1.0 4.8 79.3 14.9 1.0-2.0 30 1.0 4.2 81.5 13.3 10 1.7 9.1 73.5 15.7 20 0.9 7.3 73.7 18.2

Olive cake

2.0-2.8 30 2.0 7.0 71.0 20.0 10 1.4 5.1 78.2 15.3 20 1.8 5.5 77.2 15.6 0.5-1.0 30 0.9 3.1 80.3 15.7 10 1.4 4.5 81.5 12.7 20 1.0 4.7 79.0 15.3 1.0-2.0 30 0.9 4.6 79.9 14.6 10 1.5 4.0 83.4 11.1 20 1.1 5.8 79.7 13.4

PKE

2.0-2.8 30 0.9 3.9 80.4 14.9

Table 15. Proximate analysis of large biomass in blends with coal by slow

pyrolysis

55

Volatile matter, (%) Heating rate, (OC/m)

Moisture, (%) total biomass coal

Fixed carbon, (%)

Ash, (%)

1 3.92 35.83 7.12 28.75 52.33 7.92 5 3.86 37.08 7.54 29.54 51.18 7.88 10 3.86 37.16 7.53 29.63 51.44 7.54

50 3.32 38.13 7.48 30.65 50.94 7.61

Table 16. Proximate analyses of <150>106 micron coal/cereal mixture at different heating rates

Table 17. Peak Profile Data for individual components in three sizes

Sample

Size (µm)

Tini (oC)

Tpeak (oC)

Tend (oC)

<75>53 178 296 476 <125>106 151 296 494

PKE

<212>150 151 294 496

<75>53 224 355 451 <125>106 227 358 443

Sawdust

<212>150 227 357 443

<75>53 160 310 512 <125>106 162 312 515

Olive cake

<212>150 160 311 512

<75>53 172 300 477 <125>106 171 301 484

Cereal

<212>150 172 307 492

<75>53 382 452 631 <125>106 386 453 616

Daw Mill coal

<212>150 380 452 612

56

Table 18. Proximate analysis data for blends of individual components

<125>106microns

Daw Mill Biomass Blends PKE

[0%] [5%] [10%] [15%] [20%] [100%] Moisture, (%) 3.5 5.9 4.3 4.4 4.7 6.8 Volatile matter, (%) 30.4 30.8 35.4 37.6 39.2 68.1 Fixed carbon, (%) 55.8 54.5 52.2 49.9 48.8 19.8 Ash, (%) 10.3 8.7 8.1 8.0 7.2 5.3

Daw Mill Biomass Blends Sawdust

[0%] [5%] [10%] [15%] [20%] [100%] Moisture, (%) 3.5 4.0 3.8 4.4 4.6 4.3 Volatile matter, (%) 30.4 34.0 35.4 37.9 39.8 80.0 Fixed carbon, (%) 55.8 50.7 51.4 49.0 46.7 14.4 Ash, (%) 10.3 11.2 9.4 8.7 8.9 1.3

Daw Mill Biomass Blends Cereal

[0%] [5%] [10%] [15%] [20%] [100%] Moisture, (%) 3.5 5.3 3.4 5.2 4.7 7.4 Volatile matter, (%) 30.4 32.3 34.8 35.8 39.2 69.0 Fixed carbon, (%) 55.8 56.1 54.3 51.5 48.4 19.3 Ash, (%) 10.3 6.3 7.4 7.5 7.8 4.2

Daw Mill Biomass Blends Olive Cake

[0%] [5%] [10%] [15%] [20%] [100%] Moisture, (%) 3.5 4.2 4.2 4.2 4.1 5.0 Volatile matter, (%) 30.4 32.6 33.3 34.9 36.9 61.3 Fixed carbon, (%) 55.8 53.4 52.1 50.5 48.3 23.4 Ash, (%) 10.3 9.8 10.4 10.3 10.7 10.2

57

800OC CCS from tensometer

(kN) CCS (corr) (kN/cm2)

CCS (mean) (kN/cm2)

CCS (mean) (MPa)

A1 0.105 0.134 ) A2 0.092 0.117 > 0.127 1.27 A3 0.102 0.130 ) B1 0.217 0.276 ) B2 0.230 0.293 > 0.268 2.68 B3 0.184 0.234 ) C1 0.199 0.253 ) C2 0.171 0.218 > 0.235 2.35 C3 0.183 0.233 ) D1 0.095 0.121 ) D2 0.116 0.148 > 0.149 1.49 D3 0.141 0.180 ) E1 0.162 0.206 ) E2 0.137 0.174 > 0.196 1.96 E3 0.163 0.208 ) F1 0.137 0.174 ) F2 0.116 0.148 > 0.165 1.65 F3 0.136 0.173 ) G1 0.113 0.144 ) G2 0.124 0.158 > 0.140 1.40 G3 0.093 0.118 ) H1 0.160 0.204 ) H2 0.155 0.197 > 0.190 1.90 H3 0.133 0.169 ) I1 0.196 0.250 ) I2 0.172 0.219 > 0.237 2.37 I3 0.191 0.243 ) J1 0.089 0.113 ) J2 0.095 0.121 > 0.120 1.20 J3 0.098 0.125 )

Table 19. Crushing strength data

58

900OC

CCS from tensometer

(kN) CCS (corr) (kN/cm2)

CCS (mean) (kN/cm2)

CCS (mean) (MPa)

A1 0.443 0.564 ) A2 0.415 0.528 > 0.539 5.39 A3 0.411 0.523 ) B1 0.579 0.737 ) B2 0.641 0.816 > 0.801 8.01 B3 0.667 0.849 ) C1 0.525 0.668 ) C2 0.629 0.801 > 0.792 7.92 C3 0.713 0.908 ) D1 0.423 0.539 ) D2 0.530 0.675 > 0.524 5.24 D3 0.282 0.359 ) E1 0.417 0.531 ) E2 0.584 0.744 > 0.677 6.77 E3 0.593 0.755 ) F1 0.533 0.679 ) F2 0.553 0.704 > 0.635 6.35 F3 0.411 0.523 ) G1 0.486 0.619 ) G2 0.349 0.444 > 0.585 5.85 G3 0.543 0.691 ) H1 0.567 0.722 ) H2 0.587 0.747 > 0.721 7.21 H3 0.545 0.694 ) I1 1.213 1.544 ) I2 1.045 1.331 > 1.305 13.05 I3 0.816 1.039 ) J1 0.334 0.425 ) J2 0.276 0.351 > 0.390 3.90 J3 0.308 0.392 )

Table 19. (continuation 1). Crushing strength data

59

1,000OC

CCS from tensometer

(kN) CCS (corr) (kN/cm2)

CCS (mean) (kN/cm2)

CCS (mean) (MPa)

A1 0.976 1.243 ) A2 0.866 1.103 > 1.152 11.52 A3 0.873 1.112 ) B1 1.555 1.980 ) B2 1.443 1.837 > 1.971 19.71 B3 1.645 2.094 ) C1 1.341 1.707 ) C2 1.320 1.681 > 1.623 16.23 C3 1.162 1.480 ) D1 0.888 1.131 ) D2 1.012 1.289 > 1.217 12.17 D3 0.968 1.232 ) E1 0.965 1.229 ) E2 1.000 1.273 > 1.200 12.00 E3 0.862 1.098 ) F1 1.217 1.550 ) F2 0.783 0.997 > 1.422 14.22 F3 1.351 1.720 ) G1 0.992 1.263 ) G2 0.875 1.114 > 1.157 11.57 G3 0.860 1.095 ) H1 0.873 1.112 ) H2 1.069 1.361 > 1.284 12.84 H3 1.083 1.379 ) I1 1.938 2.468 ) I2 1.663 2.117 > 2.258 22.58 I3 1.719 2.189 ) J1 0.502 0.639 ) J2 0.630 0.802 > 0.701 7.01 J3 0.519 0.661 )

Table 19. (continuation 2). Crushing strength data

60

1,100OC

CCS from tensometer

(kN) CCS (corr) (kN/cm2)

CCS (mean) (kN/cm2)

CCS (mean) (MPa)

A1 1.293 1.646 ) A2 1.098 1.398 > 1.49 14.90 A3 1.120 1.426 ) B1 1.723 2.194 ) B2 2.112 2.689 > 2.442 24.42 B3 1.920 2.445 ) C1 1.960 2.496 ) C2 1.693 2.156 > 2.178 21.78 C3 1.479 1.883 ) D1 1.573 2.003 ) D2 1.263 1.608 > 1.851 18.51 D3 1.525 1.942 ) E1 1.267 1.613 ) E2 1.173 1.494 > 1.486 14.86 E3 1.062 1.352 ) F1 1.390 1.770 ) F2 1.786 2.274 > 1.657 16.57 F3 0.729 0.928 ) G1 1.875 2.387 ) G2 1.588 2.022 > 2.120 21.20 G3 1.532 1.951 ) H1 1.715 2.184 ) H2 1.562 1.989 > 1.968 19.68 H3 1.361 1.733 ) I1 2.363 3.009 ) I2 2.306 2.936 > 2.873 28.73 I3 2.101 2.675 ) J1 0.870 1.108 ) J2 0.781 0.994 > 1.022 10.22 J3 0.756 0.963 )

Table 19. (continuation 3). Crushing strength data

61

Code 800OC 900OC 1,000OC 1,100OC Mean SD for each biomass

A Daw Mill coal ash 0.007 0.017 0.062 0.107 0.048 B RWE sawdust 0.024 0.045 0.101 0.195 0.091 C Swedish wood 0.014 0.094 0.098 0.241 0.112 D Miscanthus pellets 0.023 0.124 0.063 0.167 0.094 E Olive cake 0.015 0.099 0.072 0.103 0.072 F Wood pellets 0.012 0.077 0.297 0.534 0.230 G Palm kernel expeller 0.016 0.100 0.072 0.184 0.093 H Miscanthus, chopped 0.014 0.021 0.117 0.178 0.083 I Cereal co-product 0.013 0.199 0.145 0.138 0.124 J Olive waste 0.005 0.029 0.070 0.060 0.041

SD % @ 800OC

SD % @ 900OC

SD % @ 1,000OC

SD % @ 1,100OC

0.014 0.081 0.11 0.191

Table 20. Standard Deviation (%) between crushing strengths of individual pellets

Sample identity Code Ts(5)

temperature (OC)

Difference between Ts(5) for coal and

blend

Cereal co-product - E.ON I 818 -63 Sawdust – RWE B 837 -44 Swedish wood – RWE C 844 -37 Miscanthus (chopped) – RWE H 858 -23 Wood pellets – Alstom F 861 -20 Olive cake – Alstom E 862 -19 PKE (SSE) G 873 -8 Miscanthus (pellets) - RWE D 875 -6 Olive waste- Alstom J 930 49 Daw Mill coal - E.ON A 881 0

Table 21. Differences between datum coal ash and Ts(5) for coal /

biomass blends

62

Sample identity Ash load

(g/m3) ESP

Section 1 (%)

ESP Section 2

(%)

ESP Section 3

(%)

Cyclone (%)

Lost (%)

ESP Efficiency

(%) Coal / PKE (1) 1.59 54.5 30.4 13.0 2.1 2.1 97.9 Coal / PKE (2) 1.54 54.3 30.5 12.8 2.2 2.5 97.5 Coal / sawdust 1.46 54.2 30.4 12.7 2.2 2.8 97.2 Coal / CCP 1.51 68.4 17.4 7.7 2.5 6.5 93.5 Kuzbass PFA 1.52 74.2 17.9 5.3 2.0 0.7 97.7 Blair Athol PFA 1.46 67.9 19.2 6.5 2.1 4.3 94.2

Table 22. Mass balance of ash in ESPTF Coal / biomass and coal only ashes

Sample identity

LOI, (%)

ESP

Section 1

(%)

ESP

Section 2

(%)

ESP

Section 3

(%)

ESP total (%)

Cyclone

(%)

ESP +

Cyclone (%)

Coal / PKE (1) 19.7 22.3 47.7 26.6 96.5 4.2 100.7 Coal / PKE (2) 22.7 22.2 47.9 26.2 96.2 4.3 100.6 Coal / sawdust 12.9 30.6 47.9 19.6 98.0 4.1 102.2 Coal / CCP 4.8 56.7 30.8 11.2 98.7 2.9 101.6

Kuzbass PFA 8.5 55.3 33.3 7.8 96.5 2.5 101.8 Blair Athol PFA 12.0 54.0 37.3 17.5 103.8 0.7 104.5

Table 23. Mass balance of LOI in ESPTF

Biomass Type CCP Olive cake PKE Daw Mill Average Porosity

(%) 79 52 70 66

Table 24. Average porosity of the slow pyrolysed chars shown in

Figure 30.

Average porosity (%) Size fractions (mm) CCP OC PKE Daw Mill

0.5-1.0 74 60 85 62 1.0-2.0 72 53 86 59 2.0-3.0 79 52 70 66

Table 25. Average porosity of pure biomass and coal samples in large size fractions

63

Daw Mill CCP PKE OC Sawdust Moisture 2.8 5.9 4.9 3.5 6.7

Volatile Matter, wt% 37.3 73.5 71.7 68.0 79.5 Fixed Carbon, wt% 53.4 16.6 18.0 20.1 13.2

Ash, wt% 9.3 4.1 5.4 8.4 0.6 Vitrinite 66.0 n/a n/a n/a n/a Liptinite 13.0 n/a n/a n/a n/a

Maceral, (%,vol)

Inertinite 21.0 n/a n/a n/a n/a Calorific Value, kJ/kg (daf) 32,820 17,039 18,502 23,007 19,334

Table 26. Properties of coal and biomasses

Table 27. Proximate analysis data for individual blends divided by

peak/regions

[0%] [5%] [10%] [15%] [20%] [100%] Moisture, (%) 3.5 5.9 4.3 4.4 4.7 6.8 Volatiles Region 1, (%) 1.7 4.2 7.1 9.9 12.1 53.7 Volatiles Region 2, (%) 28.7 26.6 28.3 27.7 27.1 14.5 Fixed carbon, (%) 55.8 54.5 52.2 49.9 48.8 19.8

PKE Ash, (%) 10.3 8.7 8.1 8.0 7.2 5.3 Moisture, (%) 3.5 4.0 3.8 4.4 4.6 4.3 Volatiles Region 1, (%) 1.7 4.6 7.6 11.2 14.6 65.4 Volatiles Region 2, (%) 28.7 29.4 27.8 26.8 25.2 14.7 Fixed carbon, (%) 55.8 50.7 51.4 49.0 46.7 14.4

Sawdust Ash, (%) 10.3 11.2 9.4 8.7 8.9 1.3 Moisture, (%) 3.5 5.3 3.4 5.2 4.7 7.4 Volatiles Region 1, (%) 1.7 4.5 7.3 9.7 12.5 56.0 Volatiles Region 2, (%) 28.7 27.8 27.5 26.1 26.7 13.1 Fixed carbon, (%) 55.8 56.1 54.3 51.5 48.4 19.3

Cereal Ash, (%) 10.3 6.3 7.4 7.5 7.8 4.2 Moisture, (%) 3.5 4.2 4.2 4.2 4.1 5.0 Volatiles Region 1, (%) 1.7 4.1 5.4 7.8 10.3 44.2 Volatiles Region 2, (%) 28.7 28.5 27.9 27.1 26.5 17.2 Fixed carbon, (%) 55.8 53.4 52.1 50.5 48.3 23.4 Olive

cake Ash, (%) 10.3 9.8 10.4 10.3 10.7 10.2

64

Table 28. Blend Proportions (wt%) based on different prediction methods

Sample Size,

(microns) Moisture,

(%)

Volatile matter,

(%)

Fixed carbon,

(%)

Ash, (%)

Coal/biomass RWE <75>53 3.76 31.53 54.91 9.80

Char RWE <75>53 4.52 3.62 69.60 22.26

Coal/biomass RWE <150>106 3.76 32.12 57.25 6.87

Char RWE <150>106 4.75 3.75 75.34 16.16

Coal/biomass SSE <75>53 3.04 28.70 54.66 13.60

Char SSE <75>53 3.66 3.84 70.82 21.68

Coal/biomass SSE <150>106 3.24 32.22 53.34 11.20

Char SSE <150>106 5.04 4.10 72.25 18.61

Coal DM <75>53 3.55 33.49 55.14 7.82

Char DM <75>53 3.22 3.79 75.71 17.28

Coal DM <150>106 3.79 32.32 57.91 5.98

Char DM <150>106 3.37 3.82 82.50 10.31

Table 29. Proximate analysis data on DTF samples

Region 1 / Biomass Volatiles

[5%] [10%] [15%] [20%] PKE 6.2% 10.4% 14.6% 17.8% Sawdust 5.8% 9.5% 14.0% 18.2% Cereal 6.6% 10.6% 14.1% 18.1% Olive cake 6.7% 8.8% 12.7% 16.8%

(Region 1-Coal Region 1)/Biomass Region 1-Coal Region 1

[5%] [10%] [15%] [20%] PKE 4.8% 10.4% 15.8% 20.0% Sawdust 4.6% 9.3% 14.9% 20.2% Cereal 5.2% 10.3% 14.8% 19.9% Olive cake 5.6% 8.7% 14.3% 20.3%

Best Fit Mapping

[5%] [10%] [15%] [20%] PKE 4.8 10.4 15.4 20.0 Sawdust 4.6 9.4 15.4 20.6 Olive cake 5.1 9.0 14.5 19.3 Cereal 5.3 9.7 14.4 19.9

65

Sample ID

Sample size (microns)

Weight of coal (g)

Weight of char

collected (g)

Collection Efficiency

(%)

<75>53 1.0022 0.4548 103.08 <75>53 1.0355 0.4921 107.94

<150>106 1.0470 0.4452 100.02 SSE PF

(Coal/PKE)

<150>106 1.0223 0.4614 106.17 <75>53 1.0062 0.4373 96.04 <75>53 1.0180 0.4308 93.51

<150>106 1.0472 0.4490 67.59

RWE PF (coal/sawdust)

<150>106 1.0080 0.4409 68.95 <75>53 1.0103 0.3773 59.53 <75>53 1.0359 0.4189 64.46

<150>106 1.0047 0.4047 66.93 Daw Mill PF

<150>106 1.0195 0.4052 66.04

Table 30. DTF Collection efficiency of char samples in different size fractions

66

Size

fraction

Moisture Volatile matter

Fixed carbon

Ash Sample identity * (balance = coal)

(microns) (%) (%) (%) (%)

<106>53 2.52 35.11 51.00 11.37

<150>106 2.10 35.44 45.75 16.70 PKE (5%)*

<212>150 1.58 33.05 48.68 16.70

<106>53 2.41 38.27 47.68 11.64

<150>106 2.36 38.18 47.80 11.67 PKE (15%)*

<212>150 1.68 36.66 49.05 12.61

<106>53 2.29 43.37 44.60 9.74

<150>106 2.60 44.06 42.71 10.63 PKE (30%)*

<212>150 1.94 41.54 44.56 11.95

<106>53 1.78 34.15 52.72 11.35

<150>106 1.79 36.54 52.04 9.63 CCP (5%)*

<212>150 1.68 32.82 52.69 12.80

<106>53 2.09 38.12 47.45 12.34

<150>106 2.10 40.52 46.07 11.32 CCP (15%)*

<212>150 2.61 37.92 49.45 10.02

<106>53 2.89 43.38 43.96 9.78

<150>106 2.83 43.81 44.93 8.44 CCP (30%)*

<212>150 2.63 43.20 43.50 10.67

<106>53 2.43 30.44 51.34 15.79

<150>106 2.32 34.20 47.86 15.62 Olive Cake (5%)*

<212>150 2.24 31.69 53.01 13.07

<106>53 2.42 34.11 46.17 17.31

<150>106 2.42 36.67 50.63 10.27 Olive Cake (15%)*

<212>150 2.18 35.34 50.56 11.92

<106>53 2.66 42.02 42.77 12.55

<150>106 2.47 42.40 45.03 10.10 Olive Cake (30%)*

<212>150 2.33 40.76 43.44 13.46

Table 31. Proximate analyses data for coal/biomass mixtures.

67

Size

fraction Moisture Volatile Fixed carbon Ash Original material

* (balance = coal) (μm) (%) (%) (%) (%)

<106>53 0.07 4.98 73.58 21.37

<150>106 1.89 6.46 68.31 23.35 PKE (5%)*

<212>150 2.53 10.85 65.61 21.01

<106>53 2.11 4.15 65.03 28.71

<150>106 1.31 6.26 67.78 24.64 PKE (15%)*

<212>150 1.72 6.14 66.69 25.44

<106>53 2.47 4.93 60.51 32.09

<150>106 1.93 6.90 63.76 27.40 PKE (30%)*

<212>150 2.02 5.76 67.59 24.63

<106>53 0.63 4.22 72.95 22.20

<150>106 0.89 5.05 78.16 15.91 CCP (5%)*

<212>150 1.57 6.38 76.01 16.04

<106>53 1.37 (26.93)? 54.41 17.28

<150>106 0.82 5.67 71.29 22.21 CCP (15%)*

<212>150 0.63 6.58 67.73 25.07

<106>53 0.67 4.70 67.37 27.26

<150>106 1.07 5.81 72.43 20.69 CCP (30%)*

<212>150 0.95 7.22 68.05 23.77

<106>53 1.27 6.04 68.66 24.04

<150>106 0.82 7.06 65.54 26.58 Olive Cake

(5%)* <212>150 0.96 7.09 68.48 23.47

<106>53 1.02 6.38 64.11 28.49

<150>106 0.85 5.87 74.80 18.48 Olive Cake

(15%)* <212>150 1.04 7.84 75.10 16.02

<106>53 1.65 6.69 63.50 28.16

<150>106 1.82 6.39 70.86 20.93 Olive Cake

(30%)* <212>150 1.39 8.23 68.53 21.85

Table 32. Proximate analyses data of DTF chars from coal/biomass blends

68

Size

fraction

Weight of

sample fired

Weight of

char collected

Collection

Efficiency Sample Name

(μm) (g) (g) (%)

<106>53 1.1365 0.3921 64.87

<150>106 1.1624 0.3658 43.99 PKE (5%)

<212>150 2.4101 0.3953 20.64

<106>53 1.2580 0.4548 89.16

<150>106 1.2829 0.3980 65.53 PKE (15%)

<212>150 1.4725 0.3064 41.98

<106>53 1.4818 0.2693 59.89

<150>106 1.2543 0.4003 82.25 PKE (30%)

<212>150 1.4837 0.4203 58.38

<106>53 1.1492 0.3328 56.65

<150>106 1.1116 0.3314 49.25 CCP (5%)

<212>150 1.1951 0.4068 42.66

<106>53 1.2425 0.6068 68.40

<150>106 1.6359 0.3514 42.17 CCP (15%)

<212>150 1.2980 0.4098 79.01

<106>53 1.4875 0.3270 61.30

<150>106 1.4839 0.3962 65.45 CCP (30%)

<212>150 1.5259 0.4022 58.74

<106>53 1.1337 0.3499 46.97

<150>106 1.1459 0.3622 53.78 Olive Cake (5%)

<212>150 1.1959 0.3983 59.82

<106>53 1.7928 0.4727 43.40

<150>106 1.2508 0.3701 53.26 Olive Cake (15%)

<212>150 1.2791 0.4175 43.86

<106>53 1.5000 0.5264 78.72

<150>106 1.4793 0.4204 58.88 Olive Cake (30%)

<212>150 1.4853 0.4120 45.02

Table 33. DTF Collection efficiency of coal/biomass blend char samples

69

Size fraction

Moisture

Volatile matter

Fixed carbon

Ash

Sample

identity

(μm) (%) (%) (%) (%)

<106>53 3.73 72.27 18.12 5.88

<150>106 3.46 71.59 19.01 5.94 PKE

<212>150 3.14 71.93 19.13 5.79

<106>53 5.51 71.25 18.91 4.33

<150>106 4.48 70.96 19.55 5.01 CCP

<212>150 4.50 70.81 19.84 4.85

<106>53 2.55 64.24 22.42 10.78

<150>106 2.44 64.89 23.19 9.49 Olive cake

<212>150 2.10 65.54 23.55 8.80

<106>53 2.09 31.78 52.34 13.79

<150>106 1.97 32.62 55.34 10.07 Daw Mill

coal <212>150 1.83 30.08 55.12 12.97

Table 34. Proximate analyses of biomass and coal samples in different size fractions

Size fraction

Moisture

Volatile matter

Fixed carbon

Ash

Original

sample

(μm) (%) (%) (%) (%)

<106>53 0.20 4.29 1.78 93.73

<150>106 Nil 0.81 0.63 98.61 PKE / sand

(1:1) <212>150 Nil 0.56 0.69 98.76

<106>53 0.10 1.01 0.61 98.28

<150>106 0.29 2.47 1.51 95.72 CCP / sand

(1:1) <212>150 0.37 2.34 2.10 95.19

<106>53 0.17 2.17 1.19 96.48

<150>106 0.12 1.33 0.76 97.79 Olive Cake

(1:1) <212>150 1.97 12.37 11.06 74.60

<106>53 2.07 5.97 62.95 29.02

<150>106 1.26 5.22 70.61 22.92 Daw Mill

coal <212>150 1.23 6.02 71.06 21.70

Table 35. Proximate analyses of sand-containing chars and coal char

70

Size fraction

Weight of sample fired

Weight of char collected

Collection Efficiency Original sample

(μm) (g0 (g) (%)

PKE / sand (1:1)

<106>53

1.7255

0.8223

84.38 PKE / sand

(1:1) <150>106

1.4731 0.7699 97.29 PKE / sand

(1:1) <212>150

1.5803

0.6892

81.43

CCP / sand (1:1)

<106>53 2.1408 1.0529 92.66

CCP / sand (1:1)

<150>106 2.1609 0.5025 42.40

CCP / sand (1:1)

<212>150 1.9697 0.8463 78.01

Olive cake / sand (1:1)

<106>53 2.1608 1.0243 82.57

Olive cake / sand (1:1)

<150>106 2.1404 0.9519 79.44

Olive cake / sand (1:1)

<212>150 2.1608 0.9392 59.60

Daw Mill coal <106>53 1.0038 0.4111 86.17 Daw Mill coal <150>106 1.0448 0.3370 73.38 Daw Mill coal <212>150 1.0823 0.3328 51.46

Table 36. DTF Collection efficiency of biomass char samples

Sample name

Contribution to overall

ash content

of blends,

(%)

Differences in Ts(5)

between coal ash and blend ash

(OC)

Sawdust – ex-RWE 1.38

-44

Wood pellet- ex-Alstom 2.27

-20

Swedish wood- ex-RWE 2.52

-37

Miscanthus, pellets – ex-RWE 3.93 -6

CCP, pellets – ex-E.ON 6.27

-63

Olive pellet-Alstom 9.38 -19

Olive cake- Alstom 14.56

+49

Miscanthus, chopped - ex-RWE 18.11

-23

PKE-Alstom 34.90

-8

Table 37. Contribution to overall ash content of blends and TS(5) values

71

PKE (ex-SSE) CCP (ex-E.ON PTC) Wood (ex-RWE) Size Fraction (microns) wt (%) wt (%) wt (%)

>212 11.31 0.02 0.96

<212>150 5.83 0.02 1.62 <150>106 11.64 0.15 2.56 <106>75 13.41 0.77 3.47 <75>53 16.09 1.19 5.69 <53>38 15.15 25.84 21.55

<38 26.58 72.00 64.16 Moisture, (%) 0.03 0.14 0.02 Loss on ignition, (%) 21.22 4.84 12.91

Table 38. Size distribution and LOI of fly ashes

72

FIGURES

Figure 1. Schematic of electrostatic precipitation test facility (ESPTF)

73

Original size range: 53-106 microns

Original size range: 106-150 microns

Original size range: 150-212 microns

Figure 2. Close-up of DTF CCP chars using SEM

74

Original size range: 53-106 microns

Original size range: 106-150 microns

Original size range: 150-212 microns

Figure 3. Close-up of DTF PKE chars using SEM

75

Original size range: 53-106 microns

Original size range: 106-150 microns

Original size range: 150-212 microns

Figure 4. Close-up of DTF olive cake chars using SEM

76

CCP chars mixed with silica powder (angular, smooth particles)

PKE chars mixed with silica powder (angular, smooth particles)

Olive cake chars mixed with silica powder (angular, smooth particles)

Figure 5. Surface features of biomass DTF chars using SEM (106-150µm)

77

Original size range: 0.5-1.0 mm

Original size range: 1.0-2.0 mm

Original size range: 2.0-3.0 mm

Figure 6. Surface features of slow pyrolysed CCP chars using SEM

78

Original size range: 0.5-1.0 mm

Original size range: 1.0-2.0 mm

Original size range: 2.0-3.0 mm

Figure 7. Surface features of slow pyrolysed olive cake chars using SEM

79

Original size range: 0.5-1.0 mm

Original size range: 1.0-2.0 mm

Original size range: 2.0-3.0 mm

Figure 8. Surface features of slow pyrolysed PKE chars using SEM

80

-6

-5

-4

-3

-2

-1

0

1

0 50 100 150 200 250

Time (min)

De

riv

ati

ve

we

igh

t %

(%

/m

in)

5% 10%15% 20%

-30

-25

-20

-15

-10

-5

00 100 200 300 400 500 600 700 800 900 1000

Temperature (oC)

Rat

e of

Wei

ght L

oss

(dW

/dt)

1oC/min 2.5oC/min 5oC/min 10oC/min 25oC/min 50oC/min

Figure 9. TGA plot of coal/cereal mixtures, 5% to 20%, 5OC/m

Figure 10. TGA profiles for olive cake (106-125 micron) at different heating rates

81

-13

-11

-9

-7

-5

-3

-1

1

0 100 200 300 400 500 600 700 800 900

Temperature (oC)

Rat

e of

Wei

ght L

oss

(dW

/dt)

1oC/min 2.5oC/min 5oC/min 10oC/min 25oC/min 50oC/min

0

100

200

300

400

500

600

700

800

900

0 10 20 30 40 50 60

Heating Rate (oC/min)

Pea

k W

idth

Tin

i-Ten

d (o C

)

200

220

240

260

280

300

320

340

360

380

Peak

Tem

pera

ture

(Tpe

ak)

Tini-TendTpeak

Figure 11. The change in Peak Temperatures with different heating rates

(olive cake at 106-125 micron)

Figure 12. TGA profiles for Daw Mill coal (106-125 micron) at different heating rates

82

-12.0

-10.0

-8.0

-6.0

-4.0

-2.0

0.00 100 200 300 400 500 600 700 800 900

Temperature (oC)

Rat

e of

Wei

ght L

oss

(dW

/dt)

1oC/min 2.5oC/min 5oC/min 10oC/min 25oC/min 50oC/min

Figure 13. TGA profiles for 10% olive cake: 90% Daw Mill (wt:wt%) at 106-125 micron at different heating rates

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.00 100 200 300 400 500 600 700 800 900 1000

Temperature

Rat

e of

Wei

ght L

oss

(dW

/dt)

PKE

Sawdust

OLIVE CAKE

Cereal

Daw Mill

Figure 14. TGA profiles for different components (106-125 micron) at 5OC/minute

83

PKE/Coal Blends

-1.2

-1

-0.8

-0.6

-0.4

-0.2

00 100 200 300 400 500 600 700 800 900 1000

Temperature (oC)

dW/d

t

0%5%10%15%20%

Figure 15. PKE Daw Mill coal blends at 0,5,10,15,20% PKE: coal (wt%)

Coal/Sawdust Blends

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

00 100 200 300 400 500 600 700 800 900 1000

Temperature (oC)

Rate

of W

eigh

t Los

s (d

W/d

t)

0%5%10%15%20%

Figure 16. Coal / sawdust blends at 0,5,10,15,20% Sawdust: Coal (wt%)

84

Coal/Cereal Blends

-1.2

-1

-0.8

-0.6

-0.4

-0.2

00 100 200 300 400 500 600 700 800 900 1000

Temperature (oC)

Rate

of W

eigh

t Los

s (d

W/d

t)

0%5%10%15%20%

Figure 17. Coal / Cereal Blends at 0,5,10,15,20% Cereal: Coal (wt%)

Coal/Olive Cake Blends

-1.2

-1

-0.8

-0.6

-0.4

-0.2

00 100 200 300 400 500 600 700 800 900 1000

Temperature (oC)

Rate

of W

eigh

t Los

s (d

W/d

t)

0%5%10%15%20%

Figure 18. Olive cake / Coal Blends at 0,5,10,15,20% olive cake: coal (wt%)

85

Plot of Pellet Strength vs Temperature

Daw Mill coal ash

0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

750 800 850 900 950 1000 1050 1100 1150

Temperature (OC)

Cru

shin

g St

reng

th (M

Pa)

RWE Saw dust

Figure 19. Comparative crushing strength

Daw Mill coal ash / RWE sawdust

Plot of Pellet Strength vs Temperature

Daw Mill coal ash

Sw edish Wood

0

2

4

6

8

10

12

14

16

18

20

22

24

750 800 850 900 950 1000 1050 1100 1150

Temperature (OC)

Cru

shin

g St

reng

th (M

Pa)

Figure 20. Comparative crushing strength Daw Mill coal ash / RWE Swedish wood ash

86

Plot of Pellet Strength vs Temperature

Daw Mill coal ash

Miscanthus pellets

0

2

4

6

8

10

12

14

16

18

20

750 800 850 900 950 1000 1050 1100 1150

Temperature (OC)

Cru

shin

g St

reng

th (M

Pa)

Figure 21. Comparative crushing strength Daw Mill coal ash / miscanthus pellet ash

Plot of Pellet Strength vs Temperature

Olive Cake

Daw Mill coal ash

0

2

4

6

8

10

12

14

16

18

750 800 850 900 950 1000 1050 1100 1150

Temperature (OC)

Cru

shin

g St

reng

th (M

Pa)

Figure 22. Comparative crushing strength Daw Mill coal ash / olive cake pellet ash

87

Plot of Pellet Strength vs Temperature

Wood Pellets

Daw Mill coal ash

0

2

4

6

8

10

12

14

16

18

20

750 800 850 900 950 1000 1050 1100 1150

Temperature (OC)

Cru

shin

g St

reng

th, (

MPa

)

Figure 23. Comparative crushing strength Daw Mill coal ash / wood pellet ash

Plot of Pellet Strength vs Temperature

Daw Mill coal ash

Palm Kernel Expeller

0

2

4

6

8

10

12

14

16

18

20

22

24

750 800 850 900 950 1000 1050 1100 1150

Temperature (OC)

Cru

shin

g St

reng

th (M

Pa)

Figure 24. Comparative crushing strength Daw Mill coal ash / palm kernel expeller ash

88

Plot of Pellet Strength vs Temperature

Daw Mill coal ash

Miscanthus, chopped

0

2

4

6

8

10

12

14

16

18

20

22

750 800 850 900 950 1000 1050 1100 1150

Temperature (OC)

Cru

shin

g St

reng

th (M

Pa)

Figure 25. Comparative crushing strength Daw Mill coal ash / chopped miscanthus ash

Plot of Pellet Strength vs Temperature

Daw Mill coal

Cereal co-product

02468

101214161820222426283032

750 800 850 900 950 1000 1050 1100 1150

Temperature (OC)

Cru

shin

g St

reng

th (M

Pa)

Figure 26. Comparative crushing strength Daw Mill coal ash / cereal co-product ash

89

Plot of Pellet Strength vs Temperature

Olive w aste

Daw Mill coal ash

0

2

4

6

8

10

12

14

16

18

750 800 850 900 950 1000 1050 1100 1150

Temperature (OC)

Cru

shin

g St

reng

th (M

Pa)

Figure 27. Comparative crushing strength Daw Mill coal ash / olive waste ash

Plot of Load vs ExtensionCereal co-product, 1,100OC

0

0.5

1

1.5

2

2.5

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Extension (mm)

Load

, (KN

)

Figure 28. Typical datalogger plot of crushing strength of sintered ash pellet

90

Char sampleLeitz Ortholus II POL-BKmicroscope with AxioCam

digital camera attached

Computer with a KS400 imageanalysis system installed

Captured charimages

Imageprocessing

Proportion ofunfused material

Wall-thickness &its distribution Char porosity Particle size Pore size & size

distribution

Porosity index Wall-thicknessindex

Unfused-materialindex

Char morphotype

Figure 29. Schematic of char image analysis procedure

(Wu, 2004, Wu et al., 2006a)

91

Daw Mill char 2-3mm

CCP 2-3mm

Olive cake char 2-3mm

PKE char 2-3 mm

Figure 30. A representation of biomass/coal chars

92

0.0

20.0

40.0

60.0

80.0

100.0

TS CS TN CN IN FS

Char Type

Freq

uenc

y, %

0.5-1.0mm 1.0-2.0mm 2.0-3.0mm

PKE

0.0

20.0

40.0

60.0

80.0

100.0

TS CS TN CN IN FS

Char Type

Freq

uenc

y, %

0.5-1.0mm 1.0-2.0mm 2.0-3.0mm

CCP

0.0

20.0

40.0

60.0

80.0

100.0

TS CS TN CN IN FS

Char Type

Freq

uenc

y, %

0.5-1.0mm 1.0-2.0mm 2.0-3.0mm

Olive cake

0.0

20.0

40.0

60.0

80.0

100.0

TS CS TN CN IN FS

Char Type

Freq

uenc

y, %

0.5-1.0mm 1.0-2.0mm 2.0-3.0mm

Daw Mill Coal

Figure 31. Char morphology distributions of pure biomass/coal-derived chars

0.0

20.0

40.0

60.0

80.0

TS CS TN CN IN FS

Char Type

Freq

uenc

y, %

0.5-1.0mm 1.0-2.0mm 2.0-3.0mm

CCP 10%+Daw Mill Coal

0.0

20.0

40.0

60.0

80.0

TS CS TN CN IN FS

Char Type

Freq

uenc

y, %

0.5-1.0mm 1.0-2.0mm 2.0-3.0mm

CCP 20%+Daw Mill Coal

0.0

20.0

40.0

60.0

80.0

TS CS TN CN IN FS

Char Type

Freq

uenc

y, %

0.5-1.0mm 1.0-2.0mm 2.0-3.0mm

CCP 30%+Daw Mill Coal

Figure 32. Morphology distributions of CCP/coal chars

0.0

20.0

40.0

60.0

80.0

TS CS TN CN IN FS

Char Type

Freq

uenc

y, %

0.5-1.0mm 1.0-2.0mm 2.0-3.0mm

PKE 10%+Daw Mill Coal

0.0

20.0

40.0

60.0

80.0

TS CS TN CN IN FS

Char Type

Freq

uenc

y, %

0.5-1.0mm 1.0-2.0mm 2.0-3.0mm

PKE 20%+Daw Mill Coal

0.0

20.0

40.0

60.0

80.0

TS CS TN CN IN FS

Char Type

Freq

uenc

y, %

2.0-3.0mm 1.0-2.0mm 0.5-1.0mm

PKE 30%+Daw Mill Coal

Figure 33. Morphology distributions of PKE/coal chars

Key to Figures 31 to 33 =

(TS-Tenuisphere, CS-Crassisphere, TN- Tenuinetwork , CN-Crassinetwork, IN-Inertoid, FS-Fusinoid/Solid)

93

0.0

20.0

40.0

60.0

80.0

TS CS TN CN IN FS

Char Type

Freq

uenc

y, %

0.5-1.0mm 1.0-2.0mm 2.0-3.0mm

Olive cake 10%+Daw Mill Coal

0.0

20.0

40.0

60.0

80.0

TS CS TN CN IN FS

Char Type

Freq

uenc

y, %

0.5-1.0mm 1.0-2.0mm 2.0-3.0mm

Olive cake 20%+Daw Mill Coal

0.0

20.0

40.0

60.0

80.0

TS CS TN CN IN FS

Char Type

Freq

uenc

y, %

0.5-1.0mm 1.0-2.0mm 2.0-3.0mm

Olive cake 20%+Daw Mill Coal

Figure 34. Morphology distributions of olive cake/coal chars

(TS-Tenuisphere, CS-Crassisphere, TN- Tenuinetwork , CN-Crassinetwork, IN-Inertoid, FS-Fusinoid/Solid)

Figure 35. Process of pulverised coal combustion

Figure 36. Illustration of modified ChB model

94

Figure 37. Main programme structure of ChB (after (Wu, 2004))

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0 200 400 600 800 1000

Temperature (oC)

Wei

ght L

oss

(dW

/dt)

[10%][12%][18%][20%]Blend [15%]

Figure 38. Theoretical Blend Profiles generated from mapping individual component profiles

(Coal:sawdust 85%:15% wt%)

95

0

50

100

150

200

250

0 5 10 15 20 25

Prediction Percentage (%)

Abso

lute

Var

iatio

n fro

m A

ctua

l

SawdustOlive CakeCerealPKE

Figure 39. Absolute Variance of Predicted Blend Profile and Actual Blend

Profile

Figure 40. Intrinsic analysis of CCP char samples (150-212µ)

96

Figure 41. Coal / PKE ash sample

Figure 42. Coal / wood ash sample

Size distribution of SSE (PKE) fly ash

0.0

6.0

12.0

18.0

24.0

30.0

-38 +38 +53 +75 +106 +150 +212

Size Bin (µm)

Freq

uenc

y (%

)

0.0

20.0

40.0

60.0

80.0

100.0

Cum

ulat

ive

Freq

uenc

y(%

)

Size distribution of RWE (wood) fly ash

0.0

15.0

30.0

45.0

60.0

75.0

-38 +38 +53 +75 +106 +150 +212

Size Bin (µm)

Freq

uenc

y (%

)

0.0

20.0

40.0

60.0

80.0

100.0

Cum

ulat

ive

Freq

uenc

y (%

)

97

Figure 43. Coal / CCP ash sample

0.0

0.2

0.4

0.6

0.8

1.0

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Blend Proportion, %

Car

bon

Bur

nout

.

0.2s 0.4s 0.6s 0.8s 1.0s

CCP+Daw Mill

1500

1600

1700

1800

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Blend Proportion, %

Com

bust

ion

Tem

pera

ture

/ºC

.

0.2s 0.4s 0.6s 0.8s 1.0s

CCP+Daw Mill

Figure 44. Burnout of CCP/Daw Mill Blends (burnout and combustion temperature versus blend proportion)

0.0

0.2

0.4

0.6

0.8

1.0

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Blend Proportion, %

Car

bon

Bur

nout

.

0.2s 0.4s 0.6s 0.8s 1.0s

PKE+Daw Mill

1500

1600

1700

1800

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Blend Proportion, %

Com

bust

ion

Tem

pera

ture

/ºC

.

0.2s 0.4s 0.6s 0.8s 1.0s

PKE+Daw Mill

Figure 45. Burnout of PKE/Daw Mill blends (burnout and combustion temperature versus blend proportion)

Size distribution of E.ON UK (CCP-PTC) fly ash

0.0

15.0

30.0

45.0

60.0

75.0

-38 +38 +53 +75 +106 +150 +212

Size Bin (µm)

Freq

uenc

y (%

)

0.0

20.0

40.0

60.0

80.0

100.0

Cum

ulat

ive

Freq

uenc

y (%

)

98

0.0

0.2

0.4

0.6

0.8

1.0

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Blend Proportion, %

Car

bon

Bur

nout

.

0.2s 0.4s 0.6s 0.8s 1.0s

Sawdust+Daw Mill

1500

1600

1700

1800

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Blend Proportion, %

Com

bust

ion

Tem

pera

ture

/ºC

.

0.2s 0.4s 0.6s 0.8s 1.0s

Sawdust+Daw Mill

Figure 46. Burnout of sawdust/Daw Mill blends (burnout and combustion temperature versus blend proportion)

0.0

0.2

0.4

0.6

0.8

1.0

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Blend Proportion, %

Car

bon

Bur

nout

.

0.2s 0.4s 0.6s 0.8s 1.0s

Olive cake+Daw Mill

1500

1600

1700

1800

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Blend Proportion, %

Com

bust

ion

Tem

pera

ture

/ºC

.

0.2s 0.4s 0.6s 0.8s 1.0s

Olive cake+Daw Mill

Figure 47. Burnout of olive cake/Daw Mill blends (burnout and combustion temperature versus blend proportion)

99

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Time Elapsed /s

Car

bon

Bur

nout

.

0% Daw Mill 5% CCP 10% CCP 20% CCP 30% CCP

CCP + Daw Mill

Figure 48. Burnout of cereal co-product/Daw Mill blends (burnout versus time)

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Time Elapsed /s

Car

bon

Bur

nout

.

0% Daw Mill 5% Olive Cake 10% Olive Cake 20% Olive Cake 30% Olive Cake

Olive Cake + Daw Mill

Figure 49. Burnout of olive cake/Daw Mill blends (burnout versus time)

100

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Time Elapsed /s

Car

bon

Bur

nout

.

0% Daw Mill 5% PKE 10% PKE 20% PKE 30% PKE

PKE + Daw Mill

Figure 50. Burnout of PKE/Daw Mill blends (burnout versus time)

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Time Elapsed /s

Car

bon

Bur

nout

.

0% Daw Mill 5% Sawdust 10% Sawdust 20% Sawdust 30% Sawdust

Sawdust+ Daw Mill

Figure 51. Burnout of sawdust/Daw Mill blends (burnout versus time)

101

APPENDIX 1-Database of samples received

Sample No.

Date received Sample identifier Analysed Type of material Supplier Unit /originQuantity supplied

(kg)1 01 October 2004 Daw Mill PF Yes Coal E.ON UK PT 12 30 November 2004 Palm Kernel Expeller (PKE) Yes Biomass SSE Ferrybridge 'C' 503 30 November 2004 Raw coal No Coal SSE Ferrybridge 'C' 154 30 November 2004 Coal / PKE blend Yes Blend SSE Ferrybridge 'C' 0.55 30 November 2004 Economiser Dust Yes Ash SSE Ferrybridge 'C' 56 12 January 2005 Cereal Co-product Yes Biomass E.ON UK plc Kingsnorth 107 24 January 2005 Wood Yes Biomass RWEnpower Didcot 508 24 January 2005 Coal sample No Coal RWEnpower Didcot 159 24 January 2005 PF sample (coal / wood blend) Yes Blend RWEnpower Didcot 0.510 24 January 2005 PFA Yes Ash RWEnpower Didcot 5011 10 February 2005 CCP Loadport OBA Amsterdam No Ash E.ON UK PT 2.512 10 February 2005 DAW /LBB (Daw Mill coal and CCP) No Blend E.ON UK PT 0.513 10 February 2005 DAW/021204/ CYC 1 No Ash E.ON UK PT 2.514 10 February 2005 DAW/021205/ CYC 4 No Ash E.ON UK PT 2.5

15 10 February 2005 DAW/021205/ CYC 5 No Ash E.ON UK PT 2.516 10 February 2005 DAW/021205/ CYC 8 No Ash E.ON UK PT 2.517 10 February 2005 DAW/021206/ CYC 9 No Ash E.ON UK PT 2.5

18 10 February 2005 DAW/021206/ CYC 10 No Ash E.ON UK PT 2.519 10 February 2005 DAW/021206/ CYC 15 No Ash E.ON UK PT 2.5

20 10 February 2005 DAW CYC Samples 12 to 18 combined Yes Ash E.ON UK PT 2021 08 March 2005 Olive pellet Yes Biomass E.ON UK PT 0.222 08 March 2005 Olive cake Yes Biomass E.ON UK PT 0.15

23 08 March 2005 Sawdust Yes Biomass E.ON UK PT 0.03

24 09 May 2005 PKE Yes Biomass RWEnpower Tilbury 6

25 09 May 2005 C/b blend (raw coal and PKE) No Blend (Coarse) RWEnpower Tilbury 6

26 09 May 2005 pf No Blend RWEnpower Tilbury 0.1

27 09 May 2005 Furnace bottom ash No Ash RWEnpower Tilbury 6

28 09 May 2005 pfa Yes Ash RWEnpower Tilbury 2029 August 2005? Wood pellets Yes Biomass Alstom Fiddlers Ferry ~530 August 2005? Olive pellets No Biomass Alstom Fiddlers Ferry ~531 August 2005? Olive cake No Biomass Alstom Fiddlers Ferry ~532 August 2005? Miscanthus pellets Yes Biomass RWEnpower ? ~133 August 2005? Miscanthus chopped Yes Biomass RWEnpower ? ~134 August 2005? Wood pellets (willow) Yes Biomass RWEnpower Sweden ~1