Prediction of selected biodiesel fuel properties using artificial neural network

doi:10.1007/s11708-015-0383-5

Front. Energy

2015, Vol. 9

Issue (4) : 433-445 https://doi.org/10.1007/s11708-015-0383-5

RESEARCH ARTICLE

Prediction of selected biodiesel fuel properties using artificial neural network

Solomon O. GIWA(

),Sunday O. ADEKOMAYA,Kayode O. ADAMA,Moruf O. MUKAILA

Department of Agricultural and Mechanical Engineering, College of Engineering and Environmental Studies, Olabisi Onabanjo University, Ibogun Campus, Ifo, Ogun State, Nigeria

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Abstract

Biodiesel is an alternative fuel to replace fossil-based diesel fuel. It has fuel properties similar to diesel which are generally determined experimentally. The experimental determination of various properties of biodiesel is costly, time consuming and a tedious process. To solve these problems, artificial neural network (ANN) has been considered as a vital tool for estimating the fuel properties of biodiesel, especially from its fatty acid (FA) composition. In this study, four ANNs have been designed and trained to predict the cetane number (CN), flash point (FP), kinematic viscosity (KV) and density of biodiesel using ANN with logsig and purelin transfer functions in the hidden layer of all the networks. The five most prevalent FAs from 55 feedstocks found in the literature utilized as the input parameters for the model are palmitic, stearic, oleic, linoleic and linolenic acids except for density network with a sixth parameter (temperature). Other FAs that are present in the biodiesels have been considered based on the number of carbon atom chains and the level of saturation. From this study, the prediction accuracy and the average absolute deviation of the networks are CN (96.69%; 1.637%), KV (95.80%; 1.638%), FP (99.07%; 0.997%) and density (99.40%; 0.101%). These values are reasonably better compared to previous studies on empirical correlations and ANN predictions of these fuel properties found in literature. Hence, the present study demonstrates the ability of ANN model to predict fuel properties of biodiesel with high accuracy.

Keywords biodiesel fuel properties artificial neural network fatty acid prediction

Corresponding Author(s): Solomon O. GIWA

Just Accepted Date: 11 September 2015 Online First Date: 26 October 2015 Issue Date: 04 November 2015

Cite this article:

Solomon O. GIWA,Sunday O. ADEKOMAYA,Kayode O. ADAMA, et al. Prediction of selected biodiesel fuel properties using artificial neural network[J]. Front. Energy, 2015, 9(4): 433-445.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-015-0383-5
https://academic.hep.com.cn/fie/EN/Y2015/V9/I4/433

Fig.1 Architecture of ANN (5:2:4)

Fig.2 Regression plots for CN network

(a) Training; (b) validation; (c) testing; (d) performance

S/N	CN				KV
Actual	Predicted error	Error	%Error	Actual	Predicted error	Error	%Error
1	53.0	54.178	−1.178	−2.223	4.60	4.489	0.111	2.415
2	61.0	60.550	0.449	0.737	4.50	4.343	0.157	3.482
3	62.4	59.374	3.026	4.849	4.502	4.509	−0.008	−0.177
4	58.3	58.610	−0.310	−0.532	4.415	4.427	−0.013	−0.287
5	55.6	53.403	2.197	3.952	4.10	4.097	0.003	0.075
6	50.0	49.741	0.259	0.518	4.20	4.243	−0.043	−1.030
7	53.0	53.061	−0.065	−0.123	4.40	4.438	−0.038	−0.864
8	55.0	52.937	2.068	3.751	4.40	4.607	−0.207	−4.709
9	51.1	50.527	0.573	1.121	4.20	4.324	−0.124	−2.941
10	57.1	56.303	0.797	1.395	4.40	4.399	0.0007	0.015
11	59.0	58.968	0.032	0.055	4.867	4.738	0.129	2.659
12	57.0	56.469	0.531	0.932	4.50	4.407	0.093	2.065
13	48.0	49.395	−1.395	−2.907	4.10	4.224	−0.124	−3.031
14	57.0	56.231	0.769	1.349	4.20	4.330	−0.130	−3.105
15	53.0	53.041	−0.041	−0.078	4.40	4.319	0.081	1.833
16	52.2	52.693	−0.493	−0.945	4.01	4.111	−0.101	−2.524
17	55.1	55.261	−0.161	−0.292	4.16	4.269	−0.109	−2.629
18	57.0	55.495	1.505	2.641	4.30	4.177	0.123	2.868
19	55.1	55.393	−0.293	−0.531	4.16	4.292	−0.132	−3.176
20	54.13	53.791	0.339	0.626	4.07	4.025	0.045	1.103
21	51.3	51.268	0.032	0.063	4.30	4.243	0.036	1.326
22	60.0	60.398	−0.398	−0.663	3.75	3.667	0.083	2.207
23	63.3	59.108	4.692	7.354	4.50	4.459	0.040	0.891
24	45.0	44.651	0.349	0.775	5.00	4.974	0.026	0.526
25	58.8	61.502	−2.702	−4.596	5.00	4.971	0.029	0.589
26	55.0	53.271	1.729	3.144	4.83	4.811	0.019	0.384
27	52.3	52.506	−0.186	−0.356	4.46	4.454	0.006	0.139
28	55.1	55.393	−0.292	−0.531	4.54	4.536	0.004	0.082
29	52.8	52.747	0.052	0.100	4.70	4.686	0.014	0.298
30	57.2	57.411	−0.211	−0.368	4.00	3.921	0.079	1.981
31	51.0	52.421	−1.452	−2.847	4.40	4.499	−0.099	−2.267
32	62.6	62.150	0.449	0.718	4.79	4.684	0.106	2.211
33	55.0	57.566	−2.566	−4.666	3.99	3.938	0.052	1.313
34	55.0	54.342	0.658	1.196	4.37	4.382	−0.012	−0.273
35	62.0	61.397	0.603	0.973	4.12	4.172	−0.052	−1.254
36	74.3	71.566	2.734	3.680	4.41	4.582	−0.172	−3.897
37	75.6	73.455	2.145	2.837	4.29	4.233	0.056	1.313
38	57.2	56.913	0.287	0.502	4.16	4.292	−0.132	−3.174
39	42.0	41.859	0.140	0.334	4.15	4.291	−0.141	−3.400
40	22.9	22.850	0.050	0.218	4.42	4.396	0.024	0.549
41	−	−	−	−	4.39	4.377	0.013	0.293
42	−	−	−	−	4.30	4.222	0.078	1.820
43	−	−	−	−	2.78	2.783	−0.006	−0.201
44	−	−	−	−	2.99	3.059	−0.074	−2.467
45	−	−	−	−	4.52	4.679	−0.162	−3.579
46	−	−	−	−	3.75	3.735	0.010	0.274

Tab.1 Actual and predicted CN and KV of biodiesels

Fig.3 Regression plots for KV network

(a) Training; (b) validation; (c) testing; (d) performance

Fig.4 Regression plots of FP network

(a) Training; (b) validation; (c) testing; (d) performance

Tab.2 Actual and predicted FP and density of biodiesels

Fig.5 Regression plots of density network

(a) Training; (b) validation; (c) testing; (d) performance

Tab.3 Previous studies versus present study

1	Achten W M J, Verchot L, Franken Y J, Mathijs E, Singh V P, Aerts R, Muys B. Jatropha bio-diesel production and use. Biomass and Bioenergy, 2008, 32(12): 1063–1084 https://doi.org/10.1016/j.biombioe.2008.03.003
2	El Diwani G, Attia N K, Hawash S I. Development and evaluation of biodiesel fuel and by-products from Jatropha oil. International Journal of Environmental Science and Technology, 2009, 6(2): 219–224 https://doi.org/10.1007/BF03327625
3	Balat M, Balat H. Recent trends in global production and utilization of bio-ethanol fuel. Applied Energy, 2009, 86(11): 2273–2282 https://doi.org/10.1016/j.apenergy.2009.03.015
4	Kondamudi N, Strull J, Misra M, Mohapatra S K. A green process for producing biodiesel from feather meal. Journal of Agricultural and Food Chemistry, 2009, 57(14): 6163–6166 https://doi.org/10.1021/jf900140e
5	Mariod A, Klupsch S, Hussein I H, Ondruschka B. Synthesis of alkyl esters from three unconventional Sudanese oils for their use as biodiesel. Energy & Fuels, 2006, 20(5): 2249–2252 https://doi.org/10.1021/ef060039a
6	Lin C Y, Fan C L. Fuel properties of biodiesel produced from Camellia Oleifera Abel oil through supercritical-methanol transesterification. Fuel, 2011, 90(6): 2240–2244 https://doi.org/10.1016/j.fuel.2011.02.020
7	Alptekin E, Canakci M. Determination of the density and the viscosities of biodiesel−diesel fuel blends. Renewable Energy, 2008, 33(12): 2623–2630 https://doi.org/10.1016/j.renene.2008.02.020
8	Lin C, Li R. Fuel properties of biodiesel produced from the crude fish oil from the soapstock of marine fish. Fuel Processing Technology, 2009, 90(1): 130–136 https://doi.org/10.1016/j.fuproc.2008.08.002
9	Giwa S, Layeni A, Ogunbona C. Synthesis and characterization of biodiesel from industrial starch production byproduct. Energy and Environmental Engineering Journal, 2012, 1(1): 45–51
10	Knothe G. Dependence of biodiesel fuel properties on the structure of fatty acid alkyl esters. Fuel Processing Technology, 2005, 86(10): 1059–1070 https://doi.org/10.1016/j.fuproc.2004.11.002
11	Ramos M J, Fernández C M, Casas A, Rodríguez L, Pérez A. Influence of fatty acid composition of raw materials on biodiesel properties. Bioresource Technology, 2009, 100(1): 261–268 https://doi.org/10.1016/j.biortech.2008.06.039
12	Cheenkachorn K. Predicting properties of biodiesel using statistical models and artificial neural networks. In: Proceedings of the Joint International Conference on Sustainable Energy and Environment. Hua Hin, Thailand, 2004, 176–179
13	Allen C A W, Watts K C, Ackman R G, Pegg M J. Predicting the viscosity of biodiesel fuels from their fatty acid ester composition. Fuel, 1999, 78(11): 1319–1326 https://doi.org/10.1016/S0016-2361(99)00059-9
14	Ramírez-Verduzco L F, Rodríguez-Rodríguez J E, Jaramillo-Jacob A R. Predicting cetane number, kinematic viscosity, density and higher heating value of biodiesel from its fatty acid methyl ester composition. Fuel, 2012, 91(1): 102–111 https://doi.org/10.1016/j.fuel.2011.06.070
15	Krisnangkura K, Yimsuwan T, Pairintra R. An empirical approach in predicting biodiesel viscosity at various temperatures. Fuel, 2006, 85(1): 107–113 https://doi.org/10.1016/j.fuel.2005.05.010
16	Krisnangkura K, Sansa-ard C, Aryusuk K, Lilitchan S, Kittiratanapiboon K. An empirical approach for predicting kinematic viscosities of biodiesel blends. Fuel, 2010, 89(10): 2775–2780 https://doi.org/10.1016/j.fuel.2010.04.033
17	Piloto-Rodríguez R, Sánchez-Borroto Y, Lapuerta M, Goyos-Pérez L, Verhelst S. Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression. Energy Conversion and Management, 2013, 65: 255–261 https://doi.org/10.1016/j.enconman.2012.07.023
18	Bamgboye A I, Hansen A C. Prediction of cetane number of biodiesel fuel from the fatty acid methyl ester (FAME) composition. International Agrophysics, 2008, 22: 21–29
19	Gopinath A, Puhan S, Nagarajan G. Theoretical modeling of iodine value and saponification value of biodiesel fuels from their fatty acid composition. Renewable Energy, 2009, 34(7): 1806–1811 https://doi.org/10.1016/j.renene.2008.11.023
20	Shivakumar, Srinivas P P, Shrinivasa R B R, Samaga B S. Performance and emission characteristics of a 4 stroke C.I engine operated on honge methyl ester using artificial neural network. ARPN Journal of Engineering and Applied Sciences, 2010, 5(6): 83–94
21	Ramadhas A S, Jayaraj S, Muraleedharan C, Padmakumari K. Artificial neural networks used for the prediction of the cetane number of biodiesel. Renewable Energy, 2006, 31(15): 2524–2533 https://doi.org/10.1016/j.renene.2006.01.009
22	Baroutian S, Kheireddine Aroua M, Abdul Raman A A, Nik Sulaiman N M. Estimation of vegetable oil-based ethyl esters biodiesel densities using artificial neural networks. Journal of Applied Sciences, 2008, 8(17): 3005–3011 https://doi.org/10.3923/jas.2008.3005.3011
23	Meng X, Jia M, Wang T. Neural network prediction of biodiesel kinematic viscosity at 313 K. Fuel, 2014, 121: 133–140 https://doi.org/10.1016/j.fuel.2013.12.029
24	Balabin R M, Lomakina E I, Safieva R Z. Neural network approach to biodiesel analysis: analysis of biodiesel density, kinematic viscosity, methanol and water content using near infrared (NIR) spectroscopy. Fuel, 2011, 90(5): 2007–2015 https://doi.org/10.1016/j.fuel.2010.11.038
25	Singh S P, Singh D. Biodiesel production through the use of different sources and characterization of oils and their esters as the substitute of diesel: a review. Renewable & Sustainable Energy Reviews, 2009, 14(1): 200–216 https://doi.org/10.1016/j.rser.2009.07.017
26	Rashid U, Anwar F, Moser B R, Ashraf S. Production of sunflower oil methyl esters by optimized alkali-catalyzed methanolysis. Biomass and Bioenergy, 2008, 32(12): 1202–1205 https://doi.org/10.1016/j.biombioe.2008.03.001
27	Sarin R, Sharma M, Sinharay S, Malhotra R K. Jatropha−Palm biodiesel blends: an optimum mix for Asia. Fuel, 2007, 86(10−11): 1365–1371 https://doi.org/10.1016/j.fuel.2006.11.040
28	Canakci M, Sanli H. Biodiesel production from various feedstocks and their effects on the fuel properties. Journal of Industrial Microbiology & Biotechnology, 2008, 35(5): 431–441 https://doi.org/10.1007/s10295-008-0337-6
29	Martín R S, Cerda T D L, Uribe A, Basilio P, Jordán M, Prehn D, Gebauer M. Evaluation of guindilla oil (Guindilla trinervis Gillies ex Hook. et Arn.) for biodiesel Production. Fuel, 2010, 89(12): 3785–3790 https://doi.org/10.1016/j.fuel.2010.07.017
30	Anwar F, Rashid U, Ashraf M, Nadeem M. Okra (Hibiscus esculentus) seed oil for biodiesel production. Applied Energy, 2010, 87(3): 779–785 https://doi.org/10.1016/j.apenergy.2009.09.020
31	Nabi M N, Hogue S M N, Akhter M S. Karanja (Pongamia Pinnata) biodiesel production in Bangladesh, characterization of karanja biodiesel and its effect on diesel emissions. Fuel Processing Technology, 2009, 90(9): 1080–1086 https://doi.org/10.1016/j.fuproc.2009.04.014
32	Leung D Y C, Wu X, Leung M K H. A review on biodiesel production using catalyzed transesterification. Applied Energy, 2010, 87(4): 1083–1095 https://doi.org/10.1016/j.apenergy.2009.10.006
33	Sivakumar P, Anbarasu K, Renganathan S. Bio-diesel production by alkali catalyzed transesterification of dairy waste scum. Fuel, 2011, 90(1): 147–151 https://doi.org/10.1016/j.fuel.2010.08.024
34	Öner C, Altun S. Biodiesel production from inedible animal tallow and an experimental investigation of its use as alternative fuel in a direct injection diesel engine. Applied Energy, 2009, 86(10): 2114–2120 https://doi.org/10.1016/j.apenergy.2009.01.005
35	Keskin A, Guru M, Altıparmak D. Influence of tall oil biodiesel with Mg and Mo based fuel additives on diesel engine performance and emission. Bioresource Technology, 2008, 99(14): 6434–6438 https://doi.org/10.1016/j.biortech.2007.11.051
36	Dorado M P, Ballesteros E, Lo’pez F J, Mittelbach M. Optimization of alkali-catalyzed transesterification of Brassica Carinata oil for biodiesel production. Energy & Fuels, 2004, 18(1): 77–83 https://doi.org/10.1021/ef0340110
37	Aliyu B, Agnew B, Douglas S. Croton megalocarpus (musine) seeds used as a potential source of biodiesel. Biomass and Bioenergy, 2010, 34(10): 1495–1499 https://doi.org/10.1016/j.biombioe.2010.04.026
38	Enweremadu C C, Mbarawa M M. Technical aspects of production and analysis of biodiesel from used cooking oil—a review. Renewable & Sustainable Energy Reviews, 2009, 13(9): 2205–2224 https://doi.org/10.1016/j.rser.2009.06.007
39	Yang F X, Su Y Q, Li X H, Zhang Q, Sun R C. Studies on the preparation of biodiesel from Zanthoxylum bungeanum maxim seed oil. Journal of Agricultural and Food Chemistry, 2008, 56(17): 7891–7896 https://doi.org/10.1021/jf801364f
40	Zhang S, Zu Y G, Fu Y J, Luo M, Zhang D Y, Efferth T. Rapid microwave-assisted transesterification of yellow horn oil to biodiesel using a heteropolyacid solid catalyst. Bioresource Technology, 2010, 101(3): 931–936 https://doi.org/10.1016/j.biortech.2009.08.069
41	Sahoo P K, Das L M. Process optimization for biodiesel production from Jatropha, Karanja and Polanga oil. Fuel, 2009, 88(9): 1588–1594 https://doi.org/10.1016/j.fuel.2009.02.016
42	Yang F X, Su Y Q, Li X H, Zhang Q, Sun R C. Preparation of biodiesel from Idesia polycarpa var. Vestita fruit oil. Industrial Crops and Products, 2009, 29(2−3): 622–628 https://doi.org/10.1016/j.indcrop.2008.12.004
43	Schinas P, Karavalakis G, Davaris C, Anastopoulos G, Karonis D, Zannikos F, Stournas S, Lois E. Pumpkin (Cucurbita pepo L.) seed oil as an alternative feedstock for the production of biodiesel in Greece. Biomass and Bioenergy, 2009, 33(1): 44–49 https://doi.org/10.1016/j.biombioe.2008.04.008
44	Moser B R. Biodiesel production, properties, and feedstocks. In Vitro Cellular & Developmental Biology−Plant, 2009, 45(3): 229–266 https://doi.org/10.1007/s11627-009-9204-z
45	Moser B R, Vaughn S F. Evaluation of alkyl esters from Camelina sativa oil as biodiesel and as blend components in ultra low-sulfur diesel fuel. Bioresource Technology, 2010, 101(2): 646–653 https://doi.org/10.1016/j.biortech.2009.08.054
46	Rashid U, Anwar F. Production of biodiesel through base-catalyzed transesterification of safflower oil using an optimized protocol. Energy & Fuels, 2008, 22(2): 1306–1312 https://doi.org/10.1021/ef700548s
47	Santos I C F, Carvalho S H V, Solleti J I, Salles W F D L, Salles K T D S D L, Meneghetti S M P. Studies of Terminalia catappa L. oil: characterization and biodiesel production. Bioresource Technology, 2008, 99(14): 6545–6549 https://doi.org/10.1016/j.biortech.2007.11.048
48	Joshi H, Moser B R, Toler J, Smith W F, Walker T. Effects of blending alcohols with poultry fat methyl esters on cold flow properties. Renewable Energy, 2010, 35(10): 2207–2210 https://doi.org/10.1016/j.renene.2010.02.029
49	Candeia R A, Silva M C D, Carvalho-Filho J R, Brasilino M G A, Bicudo T C, Santos I M G, Souza A G. Influence of soybean biodiesel content on basic properties of biodiesel−diesel blends. Fuel, 2009, 88(4): 738–743 https://doi.org/10.1016/j.fuel.2008.10.015
50	Sarin R, Sharma M, Khan A A. Studies on Guizotia abyssinica L. oil: biodiesel synthesis and process optimization. Bioresource Technology, 2009, 100(18): 4187–4192 https://doi.org/10.1016/j.biortech.2009.03.072
51	Chakrabarti M H, Ahmad R. Investigating possibility of using least desirable edible oil of eruca sativa L. in biodiesel production. Pakistan Journal of Botany, 2009, 41: 481–487
52	Kafuku K, Mbarawa M. Biodiesel production from Croton megalocarpus oil and its process optimization. Fuel, 2010, 89(9): 2556–2560 https://doi.org/10.1016/j.fuel.2010.03.039
53	Sharma Y C, Singh B. An idea feedstocks, kusum (Schleichera Triguga) for preparation of biodiesel: optimization of parameters. Fuel, 2010, 89(7): 1470–1474 https://doi.org/10.1016/j.fuel.2009.10.013
54	da Silva J P V, Serra T M, Gossmann M, Wolf C R, Meneghetti M R, Meneghetti S M P. Moringa oleifera oil studies of characterization and biodiesel Production. Biomass and Bioenergy, 2010, 34(10): 1527–1530 https://doi.org/10.1016/j.biombioe.2010.04.002
55	Usta N. Use of tobacco seed oil methyl ester in a turbocharged indirect injection diesel engine. Biomass and Bioenergy, 2005, 28(1): 77–86 https://doi.org/10.1016/j.biombioe.2004.06.004
56	Santos N A, Tavares M L A, Rosenhaim R, Silva F C, Fernandes V J Jr, Santos I M G, Souza A G. Thermogravimetric and calorimetric evaluation of babassu biodiesel obtained by the methanol route. Journal of Thermal Analysis and Calorimetry, 2007, 87(3): 649–652 https://doi.org/10.1007/s10973-006-7765-1
57	Tiwari A K, Kumar A, Raheman H. Biodiesel production from Jatropha oil (Jatropha curcas) with high free fatty acids: an optimized process. Biomass and Bioenergy, 2007, 31(8): 569–575 https://doi.org/10.1016/j.biombioe.2007.03.003
58	Sinha S, Agarwal A K, Garg S. Biodiesel development from rice bran oil: transesterification process optimization and fuel characterization. Energy Conversion and Management, 2008, 49(5): 1248–1257 https://doi.org/10.1016/j.enconman.2007.08.010
59	Nakpong P, Wootthikanokkhan S. Roselle (Hibiscus sabdariffa L.) oil as an alternative feedstock for biodiesel production in Thailand. Fuel, 2010, 89(8): 1806–1811 https://doi.org/10.1016/j.fuel.2009.11.040
60	Saxena P, Jawale S, Joshipura M H. A review on prediction of properties of biodiesel and blends of biodiesel. Procedia Engineering, 2013, 51: 395–402 https://doi.org/10.1016/j.proeng.2013.01.055
61	Najafi G, Ghobadian B, Yusaf T F, Rahimi H. Combustion analysis of a CI engine performance using waste cooking biodiesel fuel with an artificial neural network aid. American Journal of Applied Sciences, 2007, 4(10): 756–764

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[13]	Cunwen WANG, Lu CHEN, Bajpai RAKESH, Yuanhang QIN, Renliang LV. Technologies for extracting lipids from oleaginous microorganisms for biodiesel production[J]. Front Energ, 2012, 6(3): 266-274.
[14]	Wu YU, Gen CHEN, Zuohua HUANG. Influence of cetane number improver on performance and emissions of a common-rail diesel engine fueled with biodiesel-methanol blend[J]. Front Energ, 2011, 5(4): 412-418.
[15]	Zhengjiao TANG, Cunwen WANG, Weiguo WANG, Jia GUO, Yuanxin WU, Jinfang CHEN, Yigang DING. Simulation analysis of methanol flash distillation circulation process in biodiesel production with supercritical method[J]. Front Energ, 2011, 5(1): 93-97.

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