Higher heating value prediction of torrefaction char produced from non-woody biomass

doi:10.1007/s11708-015-0377-3

Front. Energy

2015, Vol. 9

Issue (4) : 461-471 https://doi.org/10.1007/s11708-015-0377-3

RESEARCH ARTICLE

Higher heating value prediction of torrefaction char produced from non-woody biomass

Nitipong SOPONPONGPIPAT(

),Dussadeeporn SITTIKUL,Unchana SAE-UENG

Department of Mechanical Engineering, Faculty of Engineering and Industrial Technology, Silpakorn University, Nakhon Pathom 73000, Thailand

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Abstract

The higher heating value of five types of non-woody biomass and their torrefaction char was predicted and compared with the experimental data obtained in this paper. The correlation proposed in this paper and the ones suggested by previous researches were used for prediction. For prediction using proximate analysis data, the mass fraction of fixed carbon and volatile matter had a strong effect on the higher heating value prediction of torrefaction char of non-woody biomass. The high ash fraction found in torrefied char resulted in a decrease in prediction accuracy. However, the prediction could be improved by taking into account the effect of ash fraction. The correlation developed in this paper gave a better prediction than the ones suggested by previous researches, and had an absolute average error (AAE) of 2.74% and an absolute bias error (ABE) of 0.52%. For prediction using elemental analysis data, the mass fraction of carbon, hydrogen, and oxygen had a strong effect on the higher heating value, while no relationship between the higher heating value and mass fractions of nitrogen and sulfur was discovered. The best correlation gave an AAE of 2.28% and an ABE of 1.36%.

Keywords higher heating value correlation biomass proximate analysis ultimate analysis

Corresponding Author(s): Nitipong SOPONPONGPIPAT

Online First Date: 12 October 2015 Issue Date: 04 November 2015

Cite this article:

Nitipong SOPONPONGPIPAT,Dussadeeporn SITTIKUL,Unchana SAE-UENG. Higher heating value prediction of torrefaction char produced from non-woody biomass[J]. Front. Energy, 2015, 9(4): 461-471.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-015-0377-3
https://academic.hep.com.cn/fie/EN/Y2015/V9/I4/461

Tab.1 Biomass types and their species

Biomass	Temperature /°C	Torrefaction time/min	Weight percentages (dry basis)								HHV_exp/(MJ·kg⁻¹)
Ash	VM	FC	C	H	N	S	O
Sugarcane leaves	Raw	—	8.3±0.3	73.8±1.0	17.9±1.2	47.4	5.9	0.55	0.15	37.7	17.94±0.60
	220	15	7.6±0.3	73.8±1.2	18.6±1.3	47.9	5.6	0.49	0.18	38.2	18.76±0.82
	260	15	9.5±0.3	66.7±1.3	23.8±1.2	50.9	5.4	0.30	0.17	33.7	20.01±0.36
	260	60	9.6±0.2	67.4±1.3	23.0±1.5	51.7	5.2	0.34	0.21	33	20.01±0.73
	280	60	9.9±0.1	58.4±1.0	31.7±1.3	56.5	5.1	0.46	0.23	27.8	21.65±0.43
Oil palm frond	Raw	—	4.0±0.2	79.1±1.5	16.9±1.4	44.8	5.5	0.31	0.12	45.3	18.21±0.79
	220	60	4.5±0.3	70.8±1.1	24.7±1.5	52.7	5.6	0.26	0.07	36.9	20.20±0.29
	260	15	5.0±0.4	66.4±1.4	28.6±1.3	55.2	5.2	0.27	0.08	34.2	21.21±0.47
	260	35	5.4±0.1	63.5±1.3	31.1±1.5	56.4	4.9	0.32	0.07	32.9	21.95±0.32
	260	60	5.6±0.3	61.0±1.1	33.4±1.4	58.4	5	0.36	0.07	30.6	22.48±0.45
	280	60	6.4±0.1	53.0±1.4	40.6±1.6	63.2	4.7	0.35	0.08	25.3	24.41±0.50
Rice straw	Raw	—	11.2±0.7	71.8±0.9	17.0±1.8	45.3	5.9	0.88	0.15	36.6	17.20±0.28
	220	60	14.9±0.8	68.9±1.2	16.2±1.0	45.8	5.5	0.9	0.09	32.8	17.38±0.60
	260	20	17.9±0.5	62.5±1.0	19.6±1.7	47.4	4.9	0.97	0.08	28.8	18.45±0.41
	260	40	18.5±0.5	59.8±1.2	21.7±1.3	49.1	5.1	0.98	0.07	26.2	18.78±0.57
	260	60	18.9±0.7	58.9±2.2	22.2±2.1	46.9	5	1.0	0.07	28.1	18.89±0.39
	280	60	24.0±0.5	46.0±2.0	30.0±0.9	51.4	4.4	1.2	0.07	18.9	20.73±0.66
Cassava rhizome	Raw	—	5.0±0.4	76.0±0.9	19.0±2.4	49.2	6.7	1.0	0.11	38.0	17.22±0.81
	220	60	10.7±0.5	70.4±1.8	18.9±1.7	46.3	5.6	0.88	0.09	36.4	18.50±0.59
	260	20	10.5±0.4	68.7±1.4	20.8±1.0	49.3	5.1	0.96	0.09	34.0	19.18±0.61
	260	40	12.2±0.5	64.7±1.2	23.1±1.4	49.1	5.1	0.94	0.09	32.6	19.65±0.73
	260	60	11.6±0.6	65.8±1.4	22.6±1.4	50.6	5.2	0.97	0.09	31.5	19.79±0.76
	280	60	11.8±0.4	58.7±1.9	29.5±1.0	52.5	4.5	1.1	0.09	30.0	21.12±0.34
Corncob	Raw	—	2.0±0.5	80.5±2.9	17.5±1.4	49.7	5.9	0.38	0.03	42.0	19.06±0.24
	220	60	6.0±0.3	74.7±2.2	19.3±1.9	48.2	5.4	0.94	0.08	39.4	19.01±0.65
	260	10	7.5±0.3	64.2±1.2	28.3±2.1	54.5	5.5	1.2	0.1	31.2	21.28±0.84
	260	60	8.2±0.4	61.9±1.0	29.9±1.7	53.4	4.9	1.2	0.11	32.2	21.45±0.89
	280	60	10.6±0.5	49.4±1.5	40.0±1.2	62.3	4.6	1.5	0.1	20.9	23.83±0.47

Tab.2 Data of various biomass and their torrefaction char

Correlation	No.	Equation	Researchers	R²reported	HHV unit	Types of material
Correlations based on proximate analysis	Eq. (1)	HHV=354.3FC+170.08VM	Cordero et al., 2001 [6]	0.999	kJ/kg	Forest/agricultural wastes /char
	Eq. (2)	HHV=0.196FC+14.119	Demirbaş, 1997 [15]	−0.647	MJ/kg	Biomass
	Eq. (3)	HHV=0.3536FC+0.1559VM−0.0078Ash	Parikh et al., 2007 [14]	0.942	MJ/kg	Solid carbonaceous materials (Coals,lignite,manufactured fuel, biomass,MSW, biomass char)
	Eq. (4)	HHV=0.1905VM+0.2521FC	Yin, 2011 [17]	0.995	MJ/kg	Biomass
	Eq. (5)	HHV=19.288−0.2135VM/FC−1.9584Ash+0.0234FC/Ash	Nhuchhen and Abdul Salam, 2012 [4]	–	MJ/kg	Biomass (by products of fruits, Agri-wastes,wood, grasses,leaves, fibrous materials)
	Eq. (6)	HHV=20.7999−0.3214VM/FC+0.0051(VM/FC)²−11.2277Ash/VM+4.4953(Ash/VM)²−0.7223(Ash/VM)³+0.0383(Ash/VM)⁴+0.0076FC/Ash	Nhuchhen and Abdul Salam, 2012 [4]	–	MJ/kg
	Eq. (7)	HHV=259.3(VM+FC)−2454.76	Thipkhunthod et al., 2005 [12]	0.899	kJ/kg	Sewage sludge
	Eq. (8)	HHV=−3.0368+0.2218VM+0.2601FC	Sheng and Azevedo, 2005 [1]	0.617	MJ/kg	Biomass
	Eq. (9)	HHV=0.312FC+0.1534VM	Demirbaş, 1997 [15]	−0.306	MJ/kg	Biomass
	Eq. (10)	HHV=19.914−0.2324Ash	Sheng and Azevedo, 2005 [1]	0.625	MJ/kg	Biomass
	Eq. (11)	HHV=−10.8141+0.3133(VM+FC)	Jiménez and González, 1991 [13]	0.533	MJ/kg	Lignocellulosic residues
Correlations based on ultimate analysis	Eq. (12)	HHV=0.3259C+3.4597	Sheng and Azevedo, 2005 [1]	0.758	MJ/kg	Biomass
	Eq. (13)	HHV=3.55C²−232C−2230H+51.2C×H+131N+20600	Friedl et al., 2005 [2]	0.943	kJ/kg	Biomass
	Eq. (14)	HHV=0.3491C+1.178H+0.1005S−0.1034O−0.015N−0.0211Ash	Channiwala and Parikh, 2002 [18]	0.733	MJ/kg	Gaseous and liquid fuels/coal/coke/biomass/refuse,MSW/animal waste
	Eq. (15)	HHV=0.4373C−1.6707	Tillman, 1978 [19]	0.666	MJ/kg	Biomass/refuse
	Eq. (16)	HHV=0.2949C+0.825H	Yin, 2011 [17]	0.997	MJ/kg	Biomass
	Eq. (17)	HHV=−1.3675+0.3137C+0.7009H+0.0318O	Sheng and Azevedo, 2005 [1]	0.834	MJ/kg	Biomass
	Eq. (18)	HHV=−0.763+0.301C+0.525H+0.064O	Jenkins and Ebeling, 1985 [8]	0.792	MJ/kg	Biomass
	Eq. (19)	HHV=430.2C−186.7H−127.4N+178.6S+184.2O−2379.9	Thipkhunthod et al., 2005 [12]	0.905	kJ/kg	Biomass

Tab.3 Details of correlations used for prediction

Fig.1 Comparison of experimental data and predicted result of HHV (The dashed lines show the range of AAE of±3.61%,±5.26%, and±6.08%, respectively)

(a) Using Eq. (1); (b) using Eq. (2); (c) using Eq. (3)

Fig.2 Comparison of experimental data and predicted result of HHV (The dashed lines show the range of AAE of±7.97% and±11.14%, respectively)

(a) Using Eq. (4); (b) using Eq. (9)

Tab.4 Error evaluation of HHV prediction of five non-woody biomass and their torrefaction char

Fig.3 Relationship between HHV_exp and mass fraction of (a) FC; (b) VM; (c) ash

Fig.4 Comparison of predicted result using correlation developed in this paper and experimental data (The dashed lines show the range of AAE of±2.74% and±2.43%, respectively)

(a) Data conducted in this paper; (b) both data conducted in this paper and Cordero et al., 2001 [6]

Tab.5 Prediction result of correlation developed in this paper (Correlation based on proximate analysis)

Fig.5 Relationship between the HHV and the mass fraction

(a) Mass fraction of carbon; (b) mass fraction of hydrogen; (c) mass fraction of oxygen; (d) mass fraction of nitrogen; (e) mass fraction of sulfur

1	Sheng C, Azevedo J L T. Estimating the higher heating value of biomass fuels from basic analysis data. Biomass and Bioenergy, 2005, 28(5): 499–507 https://doi.org/10.1016/j.biombioe.2004.11.008
2	Friedl A, Padouvas E, Rotter H, Varmuza K. Prediction of heating values of biomass fuel from elemental composition. Analytica Chimica Acta, 2005, 544(1–2): 191–198 https://doi.org/10.1016/j.aca.2005.01.041
3	Shen J, Zhu S, Liu X, Zhang H, Tan J. The prediction of elemental composition of biomass based on proximate analysis. Energy Conversion and Management, 2010, 51(5): 983–987 https://doi.org/10.1016/j.enconman.2009.11.039
4	Nhuchhen D R, Abdul Salam P. Estimation of higher heating value of biomass from proximate analysis: a new approach. Fuel, 2012, 99: 55–63 https://doi.org/10.1016/j.fuel.2012.04.015
5	Akkaya A V. Proximate analysis based multiple regression models for higher heating value estimation of low rank coals. Fuel Processing Technology, 2009, 90(2): 165–170 https://doi.org/10.1016/j.fuproc.2008.08.016
6	Cordero T, Marquez F, Rodriguez-Mirasol J, Rodriguez J J. Predicting heating values of lignocellulosics and carbonaceous materials from proximate analysis. Fuel, 2001, 80(11): 1567–1571 https://doi.org/10.1016/S0016-2361(01)00034-5
7	Erol M, Haykiri-Acma H, Küçükbayrak S. Calorific value estimation of biomass from their proximate analyses data. Renewable Energy, 2010, 35(1): 170–173 https://doi.org/10.1016/j.renene.2009.05.008
8	Jenkins B M, Ebeling J M. Correlation of physical and chemical properties of terrestrial biomass with conversion. In: Proceedings of Energy from Biomass and Wastes IX, Chicago, 1985, 371–400
9	Kathiravale S, Muhd Yunus M N, Sopian K, Samsuddin A H, Rahman R A. Modeling the heating value of Municipal Solid Waste. Fuel, 2003, 82(9): 1119–1125 https://doi.org/10.1016/S0016-2361(03)00009-7
10	Callejón-Ferre A J, Velázquez-Marti B, López-Martínez J A, Manzano-Agugliaro F. Greenhouse crop residues: energy potential and models for the prediction of their higher heating value. Renewable & Sustainable Energy Reviews, 2011, 15(2): 948–955 https://doi.org/10.1016/j.rser.2010.11.012
11	Graboski M, Bain R. Properties of biomass relevant to gasification [Fuel gas production]. Biomass gasification—principles and technology, 1981, 41–69
12	Thipkhunthod P, Meeyoo V, Rangsunvigit P, Kitiyanan B, Siemanond K, Rirksomboon T. Predicting the heating value of sewage sludges in Thailand from proximate and ultimate analyses. Fuel, 2005, 84(7–8): 849–857 https://doi.org/10.1016/j.fuel.2005.01.003
13	Jiménez L, González F. Study of the physical and chemical properties of lignocellulosic residues with a view to the production of fuels. Fuel, 1991, 70(8): 947–950 https://doi.org/10.1016/0016-2361(91)90049-G
14	Parikh J, Channiwala S A, Ghosal G K. A correlation for calculating elemental composition from proximate analysis of biomass materials. Fuel, 2007, 86(12–13): 1710–1719 https://doi.org/10.1016/j.fuel.2006.12.029
15	Demirbaş A. Calculation of higher heating values of biomass fuels. Fuel, 1997, 76(5): 431–434 https://doi.org/10.1016/S0016-2361(97)85520-2
16	Küçükbayrak S, Dürüs B, Meríçboyu A E, Kadioġlu E. Estimation of calorific values of Turkish lignites. Fuel, 1991, 70(8): 979–981 https://doi.org/10.1016/0016-2361(91)90054-E
17	Yin C Y. Prediction of higher heating values of biomass from proximate and ultimate analyses. Fuel, 2011, 90(3): 1128–1132 https://doi.org/10.1016/j.fuel.2010.11.031
18	Channiwala S A, Parikh P P. A unified correlation for estimating HHV of solid, liquid and gaseous fuels. Fuel, 2002, 81(8): 1051–1063 https://doi.org/10.1016/S0016-2361(01)00131-4
19	Tillman D. Wood as an energy resource. New York: Academic, 1978
20	Francis H E, Lloyd W G. Predicting heating value from elemental composition. Journal of Coal Quality, 1983, 2: 2
21	Bridgeman T G, Jones J M, Williams A, Waldron D J. An investigation of the grindability of two torrefied energy crops. Fuel, 2010, 89(12): 3911–3918 https://doi.org/10.1016/j.fuel.2010.06.043
22	Chen W H, Cheng W Y, Lu K M, Huang Y P. An evaluation on improvement of pulverized biomass property for solid fuel through torrefaction. Applied Energy, 2011, 88(11): 3636–3644 https://doi.org/10.1016/j.apenergy.2011.03.040
23	Medic D, Darr M, Shah A, Potter B, Zimmerman J. Effects of torrefaction process parameters on biomass feedstock upgrading. Fuel, 2012, 91(1): 147–154 https://doi.org/10.1016/j.fuel.2011.07.019

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