Methodology To Simulate Properties Of Biomass ...

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Methodology To Simulate Properties Of Biomass Torrefaction Process And Products Yen-Hsiung Kiang

A simulation model and methodology has been proposed to determine the properties of torrefaction process and the properties of torrefied biomass products. Theoretical kinetic equations are used to develop the model. Least square fitting is then used to calculate the values of parameters in the model. The process parameters used for the prediction are torrefaction time and temperature. The data used are collected from past literatures (over 200 data). This simulation model and methodology can be used to simulate the properties of torrefaction process and torrefied biomass products. During system design phase, these simulation model and methodology is a useful tool to predict the torrefaction process parameters and properties of torrefied biomass products. Keywords: torrefaction, wastewater sludge, heating value, biomass, bio-waste, simulation model. 1.

INTRODUCTION

Biomass, including natural wood and processed wood residues, agricultural residues, market wastes, kitchen wastes and bio-sludge wastes etc., are numerous in kind and, as a group, is an abundant sustainable energy source. The use of biomass, especially the group of bio-wastes, not only can solve waste generation problem, but also can convert waste to energy, a plus for a sustainable society. An additional advantage to use biomass is the carbon credit generated. However, the utilization of biomass directly is a difficult task. The reasons are that their poor thermal characteristics, such as low heating value, and poor chemical and physical properties, such as high moisture content, decomposition and fermentation, smoke while burning, hygroscopic property, low density and polymorphism. These characteristics lead to high transportation, handling and storage costs as well as problems related to these operations. Torrefaction is an attractive option to improve the thermal, chemical and physical properties of biomass while retain over 80% of the heat carried in the raw biomass. For the effective use of torrefied biomass, the basic parameters of the torrefaction process and the properties of torrefied biomass must be understood. At the present time, the parameters of torrefaction process and properties of torrefied biomass are normally determined experimentally in laboratory or pilot plant. Currently, there are lots of experimental data available 1

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for the basic properties of torrefaction process and torrefied biomass. However, there are many problems to use these information. For one, these information cover very broad spectra, including types of biomass, laboratory data or pilot equipment test data. Properties of biomass are different also. Analytical measurement methods are also different. Thus, at the present time, for each application, laboratory or pilot tests are required to determine the properties of torrefaction for the specific applications. This is a time consuming process and also costly. Thus, it is necessary to have a methodology model that can quickly determine the parameters of torrefaction process and properties of torrefied biomass. It does not necessarily be very precise. But the obtained data should give order of magnitude quality. These data will then provide valuable information during feasibility study and system design phases. Thus, the objective of this paper is to consolidate as much as possible the available basic properties of torrefaction process and torrefied biomass from literature. And then, these data are used to develop simulation equation for the prediction of basic properties of torrefaction process and torrefied biomass. 2.

CHARACTERIZTION OF BASIC PROPERTIES

The basic torrefaction process and its variables and properties are shown in Figure 1. There are process and performance variables of the process and basic and extended properties of the biomass materials. 2.1 Torrefaction process: The process variables of torrefaction process are  

Torrefaction Reactor Temperature Torrefaction Reactor Biomass Residence Time

Torrefaction reactor temperature and torrefaction reactor biomass residence time are the control parameters to determine other properties. The torrefaction process performance variables are:  

% Mass Conversion (Product Yield): The weight percentage of raw biomass that is converted to torrefied biomass, daf basis (dry ash free). % Energy Conversion (Energy Yield): The percentage of energy contained in raw biomass that is retained in the torrefied biomass, daf basis. 2

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These two variables are calculated from the basic properties of biomass raw materials and torrefied biomass products. 2.2 Properties of torrefied biomass materials: The properties of torrefied biomass materials are higher heating value (HHV), the elementary analysis, and ash content, as follows 

Higher heating value, MJ/Kg

And the elementary analysis, wt%, dry basis       

wt% C wt% H wt% O wt% N wt % S wt% Other elements, such as chlorine etc. wt% Ash

C, H, and O are the carbon, hydrogen and oxygen contents in the torrefied biomass. And N, S and Other elements, such as chlorine etc.are the nitrogen, sulfur and Other elements, such as chlorine etc. contents in the torrefied biomass. 3.

BIOMASS TORREFACTION PROCESS SIMULATION MODEL

To carry out process simulation, the process data required are  

Torrefaction Reactor Temperature Torrefaction Reactor Biomass Residence Time

The process simulation model is a five (5) step process, refer to Figure 2 and described as follows.

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Figure 1 Relevant Properties of Biomass Torrefaction Process

TORREFACTION PROCESS

PROCESS VARIABLES RESIDENCE TIME, t, hr REACTION TEMPERATURE, T, oC

RAW MATERIAL

BASIC PROPERTIES Total Mass, kg, wet base % Combustibles, wet base % Ash, wet base % Water, wet base Energy Content, MJ, total

PRODUCT

BASIC PROPERTIES PERFORMANCE VARIABLES % Mass Conversion (or, Mass Yield),daf % Energy Conversion (or, Energy Yield), total

EXTENDED PROPERTIES Combustibles, daf % Carbon, daf % Hydrogen, daf % Oxygen, daf % Nitrogen, daf % Sulfur, daf % Other Elements, daf, (chlorine etc.) Higher Heating Value, MJ/kg, daf

Total Mass, kg, dry base % Combustibles, dry base % Ash, dry base Higher Heating Value, MJ/kg, dry base Energy Content, MJ, total EXTENDED PROPERTIES Dry mass % Carbon, dry base % Hydrogen, dry base % Oxygen, dry base % Nitrogen, dry base % Sulfur, dry base % Other Elements, dry base, (chlorine etc.) % Ash

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Figure 2 Biomass Torrefaction Process Simulation Model

PROCESS VARIABLES RESIDENCE TIME, t, hr REACTION TEMPERATURE, T, oC

RAW MATERIAL STEP 1: RAW DATA COLLECTION BASIC PROPERTIES Total Mass, kg, wet base % Combustibles, wet base % Ash, wet base % Water, wet base Total Energy Content, MJ

BASIC PROPERTIES Mass of Combustible, kg Mass of Ash, kg Total Energy Content, MJ EXTENDED PROPERTIES

STEP 5: DATA RECOVERY

% Carbon, daf EXTENDED PROPERTIES % Hydrogen, daf % Carbon, daf % Oxygen, daf % Hydrogen, daf Mass of Nitrogen, kg % Oxygen, daf Mass of Sulfur, kg % Nitrogen, daf Mass of Other Elements, kg, (chlorine etc.) % Sulfur, daf % Other Elements, daf, (chlorine etc.) Higher Heating Value, MJ/kg, daf STEP 2: DATA REDUCTION BASIC PROPERTIES Mass of Combustible, kg, daf Mass of Ash, kg, dry basis Total Energy Content, MJ

PRODUCT PRODUCT YIELD AND PROPERTIES BASIC PROPERTIES Total Mass, kg, dry base % Combustibles, dry base % Ash, dry base Higher Heating Value, MJ/kg, dry base Total Energy Content, MJ EXTENDED PROPERTIES % % % % % % %

Carbon, dry base Hydrogen, dry base Oxygen, dry base Nitrogen, dry base Sulfur, dry base Other Elements, dry base, (chlorine etc.) Ash, dry base

STEP 4: DATA MANIPULATION

STEP 3: SIMULATION CALCULATION

EXTENDED PROPERTIES Mass of Nitrogen, kg Mass of Sulfur, kg Mass of Other Elements, kg (chlorine etc.)

CALCULATED PRODUCT DATA ASSEMBLY PERFORMANCE PARAMETERS % Mass Conversion (or, Mass Yield),daf % Energy Conversion (or, Energy Yield), total EXTENDED PROPERTIES % Carbon, daf Carbon Hydrogen Ratio, daf Mass of Nitrogen Conversion Mass of Sulfur Conversion Mass of Other Elements conversion (chlorine etc.) Mass of Ash Conversion Higher Heating Value, MJ/kg, daf

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STEP 1: RAW DATA COLLECTION In this step, the following raw biomass data are collected and presented in the following form. BASIC PROPERTIES Total Mass, kg, wet base % Combustibles, wet base % Ash, wet base % Water, wet base Total Energy Content, MJ EXTENDED PROPERTIES % Carbon, daf % Hydrogen, daf % Oxygen, daf % Nitrogen, daf % Sulfur, daf % Other Elements, daf, (chlorine etc.) Higher Heating Value, MJ/kg, daf STEP 2: DATA REDUCTION Some of the above listed properties are not required or have to be converted to other parameters for process simulation, as follows, % Water, wet base, % Carbon, daf, % Hydrogen, daf, % Oxygen, daf, and Higher Heating Value, MJ/kg, daf will not be used in the simulation. The properties of products will be calculated by the simulation equation. As for water content, there is limited water content in the available biomass product data. Thus, the torrefied biomass properties will be on dry basis. The following properties will be used to develop the key properties for simulation: Total Mass, kg, wet base, % Combustibles, wet base, % Ash, wet base, % Nitrogen, daf, % Sulfur, daf and % Other Elements, daf, (chlorine etc.). Thus, the data required from the raw biomass for process simulation are: BASIC PROPERTIES Mass of Combustible, kg Mass of Ash, kg Total Energy Content, MJ EXTENDED PROPERTIES Mass of Nitrogen, kg 6

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Mass of Sulfur, kg Mass of Other Elements, kg (chlorine etc.) Mass of combustibles and total energy content of the raw biomass are used to calculate the mass and energy content of the torrefied mass. The reason that ash, nitrogen, sulfur and other elements are converted to mass quantity is that the mass of then in the torrefied products will be related to the mass in th raw biomass. Normally, the mass of ash should be the same before and after torrifaction. For others, nitrogen etc., the mass in the torrefied products should be less than that in the raw biomass, some may come out with the liquid or liquid products. STEP 3: SIMULATION CALCULATION The following simulation equations will be used to calculated the properties of the torrefied biomass products:         

% Mass Conversion (or, Mass Yield),daf = f(t, T) % Energy Conversion (or, Energy Yield), total = f(t, T) % Carbon, daf = f(t, T) Carbon Hydrogen Ratio, daf = f(t, T) Mass of Nitrogen Conversion Ratio = f(t, T) Mass of Sulfur Conversion Ratio = f(t, T) Mass of Other Elements conversion (chlorine etc.) Ratio = f(t, T) Mass of Ash Conversion Ratio = f(t, T) Higher Heating Value, MJ/kg, daf = f(t, T)

The conversion ratio is defined as mass in the torrefied biomass divided by the mass in the raw biomass. The reason only %Carbon and Carbon Hydrogen Ratio are used to characterize the organics in the torrefied biomass products are that in the products, %Carbon + % Hydrogen + % Oxygen = 100, and % carbon and carbon Hydrogen Ratio can be used to calculated % Hydrogen, and then solve for % Oxygen. STEP 4: DATA MANIPULATION From the information obtained in Step 3, the following data for the torrefied biomass will be developed: BASIC PROPERTIES  

Mass of Combustible is calculated from mass of combustible in the raw biomass and % mass conversion. Mass of Ash is calculated from mass of ash in the raw biomass and mass 7

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ash conversion ratio. Total Energy Content is calculated from the total energy content in the raw biomass and % energy conversion.

EXTENDED PROPERTIES       

% Carbon (daf) is the value calculated from the simulation equation. % Hydrogen (daf) is calculated fro % carbon and carbon hydrogen ratio. % Oxygen (daf) is cauculated from 100 - % carbon, daf - % hydrogen (daf). Mass of Nitrogen is calculated from mass of nitrogen in the raw biomass and mass nitrogen conversion ratio. Mass of Sulfur is calculated from mass of nitrogen in the raw biomass and mass sulfur conversion ratio. Masses of Other Elements are calculated from mass of other elements in the raw biomass and mass other elements conversion ratio. Higher Heating, MJ/kg, daf

STEP 5: DATA RECOVERY The torrefied biomass product yield and properties are reconstructed into the following format: BASIC PROPERTIES Total Mass, kg, dry base % Combustibles, dry base % Ash, dry base Higher Heating Value, MJ/kg, dry base Total Energy Content, MJ EXTENDED PROPERTIES % Carbon, dry base % Hydrogen, dry base % Oxygen, dry base % Nitrogen, dry base % Sulfur, dry base % Other Elements, dry base, (chlorine etc.) % Ash, dry base 4. METHODOLOGY FOR THE DEVELOPMENT OF SIMULATION EQUATIONS 4.1 Collect biomass torrefaction data from literature The data collected and calculated from the literatures are organized into the following categories 8

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Biomass type Torrefaction temperature, oC Torrefaction residence time, hour Product distribution, %wt daf % Solid % Condensable gas % non-condensable gas Product elementary analysis, %wt daf %C %H %O %N %S Carbon Hydrogen Ratio Higher heating value, MJ/Kg Heat recovery, % heat retained in torrefied biomass Mass recovery, % recovered mass for torrefied biomass

Literatures of the data used are listed in the literature section of this paper. And the consolidated data are listed in Appendix 1. The distribution of biomass types in the collected data is shown in Figure 3 Figure 3 The distribution of biomass types in the collected data

Most of the data are from laboratory tests. However, there are data collected from commercial torrefaction reactors. They are: Convective bed, Fluidized Bed and Rotating Drum reactors. The data collected are of a large sample that will provide meaningful simulation equation development. The data with no temperature or residence time are removed. And, some data have all the variables and some have only part of the variables. The curve 9

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fitting process only chooses these data with the required parameters. Only the key variables are used to develop the simulation formula. They are Process properties  

Mass Conversion, % raw biomass that is converted into torrefied biomass Energy Conversion, % heat retained in torrefied biomass

Product properties      

Higher heating value, MJ/Kg Product elementary analysis, %wt daf %C Carbon to hydrogen atom ratio Nitrogen mass out as a ratio against the nitrogen mass in Sulfur mass out as a ratio against the sulfur mass in Ash mass out as a ratio against the ash mass in

The data available for nitrogen and sulfur are limited. 4.2 Simulation equation development The reaction rate equation is used as the model for simulation: dM/dt = M

(1)

The integration of Equation (1) results in the following equation: M = B exp (kt)

(2)

k = A exp (-E/RT)

(3)

Where

Where M t k T

= the change in derived parameters, such as %C, % mass conversion, etc. = biomass residence time in torrefaction reactor = rate constants = temperature in oK

Substitute Equation (3) into Equation (2), results in M = B exp { A exp (C/T) t }

(4) 10

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The exponential term can be expended in accordance to the exponential series as follows: exp(X) = 1+ X + X2/2! + X3/3! + X4/4! ………….

(5)

Where X is a function of 1/T. The value T is in the range 473 oK and 573 oK， The order of magnitude of 1/T2 and 1/T3 is 10-6 and 10-9. Thus, the terms beyond 1/T2 can be neglected. Substitute Equation (5) into Equation (4) results in M = exp (BA t + BAC t/T)

(6)

Thus, the simulation equation can be assumed as M = exp ( A + B t + C/T + D t/T) Where the terms A and C/T are included to complete the simulation model. For better estimation, the second and third order equations are also used. After test runs for several cases, the following model gives the best results with the minimal parameters: M = exp (A + B t + C t2 + D t/T + E/T + F/T2 + G t2/T2)

(7)

Equation (7) is then used as the simulation model. Microsoft office EXCEL program’s LINEST is used to fit data to the equations. After the first fit, then extreme values are removed to finalize the equation. 4.3 Simulation Parameter Definition The simulation equations for each variable are developed and described in this section. %MC = Solid mass conversion: represented by the mass of the torrefied biomass as a percentage of the mass of the raw biomass, daf basis. %EC = Energy recovery: represented by the energy retained in the mass of the torrefied biomass as a percentage of the energy input with the mass of the raw biomass, daf basis. HHV = Higher heating value: the higher heating value of the torrefied biomass, in MJ/Kg. %C = Product carbon Concentration: the carbon content in weight 11

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percentage, daf, in the torrefied biomass products, calculated based on daf fraction of C, H, and O. C/H = Product carbon Hydrogen Ratio: the ratio of carbon content and hydrogen content, wt% daf, in the torrefied biomass products, calculated based on daf fraction of C, H, and O. NC = Nitrogen conversion ratio: The ratio between the nitrogen content in the terrified biomass, in kg, against the nitrogen content in the raw biomass, in kg. SC = Sulfur conversion ratio: The ratio between the sulfur content in the terrified biomass, in kg, against the sulfur content in the raw biomass, in kg. ASHR = Ash conversion ratio: The ratio between the sulfur content in the terrified biomass, in kg, against the sulfur content in the raw biomass, in kg. The simulation model is as follows, which gives the best fit, where the term t/T is of no significance. Thus, the simulation model equation is reduced to: 4.4 Simulation Equation M = exp (A + B t + C t2 + D/T + E/T2) Where M = %MC, %EC, HHV, %C, C/H, NC, SC and ASHR respectively. 5.

RESULTS, DISCUSSION AND CONCLUSION

5.1 Simulation Equation and Statistics The parameters in the simulation model for the different properties are listed in Table 1. And the statistics of the parameters estimation are listed in Table 2. Two statistical parameters are used to evaluate among these equations. They are average absolute error (AAE) and average bias error (ABE): AAE =

1 --n

n Σ n=1

{

ABS[HHVpredicted – HHVcalculated/experimental] ----------------------------------------------------------HHVcalculated/experimental

}

X 100%

HHVpredicted – HHVcalculated/experimental --------------------------------------------------------HHVcalculated/experimental

}

X 100%

and AAE =

1 --n

n Σ n=1

{

Basically, AAE is the average of the data set, quantifies how close the predicted HHVs are to the calculated or experimental values. Lower AAE indicates higher accuracy of a particulate correlation equation. As for ABE, a 12

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positive value indicates an overall over estimation of the correlation equation for the sample population. And, a negative value indicates an overall under estimation of the correlation equation for the sample population. The smaller the absolute value of the ABE, the smaller the bias of the correlation [Sheng, Azevedo 2005]. 5.2 Discussion and Conclusion The developed models are useful for most of the cases. However, more test data are required to further verify and even modify the models. During feasibility phase and system design phase, this model is a useful tool to predict the properties of torrefaction process and torrefied biomass. For sulfur, the data are not sufficient. Thus, more experimental data are required to further develop the model.

6. Literatures Agar, D. and M. Wihersarri, "Torréfaction of biomass - on the production of enhanced solid fuels for European large-scale power generation", Forest Bioenergy 2010 Conference Proceedings, pp. 315-323, Tampere, September 2010 Bergman, Patrick C.A. and Jacob H.A., Kiel "Torrefaction for biomass upgrading", 14th European Biomass Conference and Exhibition, Paris, France, October 17-21，2005 Bjorck, Charlotte, "Torrefaction of Biomass", The University of Sheffield, Mini-project report, May 18 2012 Bos, Fran, "Torrefaction gradients in biomass", University of Twente, Internship report, November 2011 Bourgeois, J.P., and J. Doat, "Torrefied wood from temperated and tropical species", Bioenergy 84 Conference, Gotenborg, Sweden, June 1984 Bridgeman, T.G., J.M. Jones, I. Shioeld, and P.T. Williams, "Torrefaction of reed canary grass, wheat straw and willow to enhance solid fuel qualities and combustion properties, Fuel, 87 (6), 844-856, 2008 Carrasco, Juan C., Gloria S. Oporto, John Zondlo and Jingxin Wang, "Torrefaction of Red Oak (Quercus rubra) in a Fluidized Reactor, BioResources 8 (4), 5067-5082 2013 Chen, Qing, JinSong Zhou, BingJun Liu, QinFeng Mei and ZhongYang Luo, "Influence of torrefaction pretreatment on biomass gasification technology", Chinese Science Bulletin, Vol. 56, No. 14, 1449-1456, May 2011 Ciolkosz, D. and R. Wallace, "A review of torrefaction for bioenergy feedstock production", Bio fuels, Bioprod. Biorefin. 5, 317-329 2011 Deng, J., G. Wang, Y. Kuang, and Y. Luo, "Pretreatment of agricultural residues for co-generation via torrefaction", Journal of Analytic and Applied Pyrolysis, 86 (2), 331-337 2009 13

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Dhungana, A., Torrefaction of Biomass", Master of Applied Science Thesis, Dalhousie University, Halifax, Nova Scotis, 2011 (Table 1) Dhungana, A., Torrefaction of Biomass", Master of Applied Science Thesis, Dalhousie University, Halifax, Nova Scotis, 2011 (Table 2) Ferro, D. Tito, V. Vigouroux, A. Grimm and R. Zani, "Torrefaction of Agricultural and Forest Residue", Cubasolar, March 2004 Girard, P. and N. Shah, "Developments on torrefied wood an alternative to charcoal for reducing deforest, FAO publication, 1985 Indian Institute of Science, "Project completion report on torrefaction of bamboo", Advanced Bioresidue Energy Technologies Society, Combustion, Gasification and Propulsion Laboratory, Depart of Aerospace Engineering, Indian Institute of Science, October 2006 Jaafar, Amin A. and Murni M. Ahmad, "Torrefaction of Malaysian Palm Kernel Shell into Value Added Solid Fuel", World Academy of Science. Engineering and Technology, 60, 2011 Linnebur, Kyle Henry, "Analysis of torrefaction of big bluestem and mixed grass from the conservation reserve program", Master of Science Thesis, Department of Biological and Agricultural Engineering, 2013Kansas State University, Lipinsky, E.S, J.R. Acrate and T.B. Reed, "Enhanced wood fuels via torrefaction", Fuel Chemistry Division Preprints, 47, (1), 408-410 2002 Mani, S., "Integrating biomass torrefaction with thermo-chemical conversion processes", Proceedings of the 2009 AIChE Annual Meeting, Nashville, Tennessee, Nov. 8-13 2009 Medic, Corde, "Investigation of torrefaction process parameters and characterization of torrefied biomass", Ph. D. Theses and Dissertations, Iowa State University, 2012 Medic, Dorde, Matthew Darr, Ajay Shah and Sarah Rahn, "The effects of particle size, different corn stover components and gas residence time on torrefaction of corn stover", Energies 5, 1199-1214, 2012 Nimlos, Mark N., Emily Brooking, Michael J. Looker and Robert J. Evans, "Biomass torrefaction studies with a molecular beam mass spectrometer", Am. Chem. Soc., Div. Fuel Chem., 48 (2), 590 2003 Park, Junyeong, Jiajia Meng, Kwang Hun Lim, Orlando J. Rojas and Sunkyu Park, "Transformation of lignocellulosic biomass during torrefaction", J. Anal, Appl. Pyrol 2013 Prins, M.J., "Thermodynamic analysis of biomass gasification and torrefaction", Technosche University, Eindhoven, The Netherlands, PhD thesis 2005 Rodrigues, Thiago Oliveira, Patrick Louis Albert Rousset, "Effects of Torrefaction on Energy Properties of Cucalyptus Grandis Wood", Cerne, Lavras, v. 15, n. 4, p 446-452, 2009 Shang, Lei, "Upgrading Fuel Properties of Biomass by Torrefaction", Ph. D. Thesis, Department of Chemical Engineering, Technical University of Denmark, December 2012 Sheng C, Azevedo JLT. Estimating the higher heating value of biomass fuels from basic analysis data. Biomass Bioenergy 2005;28:499-507. 14

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Sridhar, G., et. Al., "Torrefaction of Bamboo", 15th European Biomass Conference and Exhibition, Berlin, May 7-11 2007 Tumuluru, Jaya Shankar, Shahab Sokansanj, J. Richard Hess, Christopher T. Wright and Richard D. Boardman, "A review on biomass torrefaction process and product properties for energy applications", Industrial Biotechnology, vol. 7, No. 5, p. 400, October 2011 Wang, GuiJun, YongHao Luo, Jian Deng, JiangHong Kuang and YunLiang Zhang, "Pretreatment of biomass by torrefaction", Chinese Science Bulletin, Vol. 56, No. 14, 1442-1448, May 2011 Yan, W., J.T. Hastings, T.C. Acharjee, C.J. Coronella, and V.R. Vaasquez, "Mass and energy balances of wet torrefaction of lignocellulosic biomass", Energy Fuel (in Press) 2010 Zakri, Bahnam, Jussi Saari, Ekaterina Sermyagina and Esa Vakkilainen, "Integration of Torrefaction with steam power plant", LUT Scientific and Experitixe Publications Tutkimusraportit - Research Report 8, 2013 Zanzi, R., D.T. Ferro, P.B. Soler, P.B. and E. Bjornbom, "Biomass Torrefaction", 6th Asia-Pacific International Symposium on Combustion and Energy Utilization, Kuala Lumpur, May 20-22 2002

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A

B

C

D

%MC, % Solid Mass Conversion, daf

-19.82065919

-0.024952418

0.000409252

24384.79449 -6109570.772

%EC, % Energy Recovery

-5.823956947

0.016506014

-0.004214404

10268.65704 -2546202.334

HHV, Higher Heating Value, MJ/Kg

6.548413236

-0.04298652

0.003589101

-3162.712874 694086.2507

%C, %Carbon, daf

10.96995139

2.73431E-05

3.42293E-05

-6886.562202 1672366.559

C/H, carbon to hydrogen ratio

14.49156883

-0.015971172

0.000532359

-12062.3355

2940730.987

NR, N mass out/N mass in

-0.571740892

-0.006567274

0

252.3014574

0

SR, S mass out/S mass in ASHR, Ash mass out/Ash mass in

0.120894743 0 (Note)

0.01655062

0

-59.07644685

0

Note :the ASHR is assumed to be 0. That is, assume the mass of ash will not be changed during torrefaction. Table 1 The parameters in the simulation model

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E

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No. of Samples

R2{1}

Normal Value{2}

AAE

ABE

%MC, % Solid Mass Conversion, daf

264

0.5542

30%-90%

9.71

0.90

%EC, % Energy Recovery

228

0.502

50%-100%

5.74

0.31

HHV, Higher Heating Value, MJ/Kg

301

0.2447

15-40

7.08

0.55

%C, %Carbon, daf

193

0.5437

45%-65%

3.77

0.11

%H, %Hydrogen, daf{3}

193

NA

4%-6%

0.06

0.02

%O, %Oxygen, daf{4}

193

NA

27%-49%

0.06

0.03

C/H, carbon to hydrogen ratio

193

0.5153

7-15

6.31

-0.33

NR, N mass out/N mass in

24

0.2838

0.8-1

3.88

-0.19

SR, S mass out/S mass in

8

0.2000

0.77-0.99

13.76

13.76

Note: {1} The correlation coefficient {2} The value range of the data. {3} Calculated from %C (based on fraction of daf contains only C, H, and O) and C/H (based on fraction of daf contains only C, H, and O) {4} Calculated from 100 - %C (based on fraction of daf contains only C, H, and O) - % H (based on fraction of daf contains only C, H, and O) Table 2 The statistics of estimated parameters in the simulation model

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