Prediction of selected biodiesel fuel properties using artificial neural network

doi:10.1007/s11708-015-0383-5

Frontiers in Energy

2015, Vol. 9

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

本期目录

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.

Key words： biodiesel fuel properties artificial neural network fatty acid prediction

收稿日期: 2014-12-24 出版日期: 2015-11-04

Corresponding Author(s): Solomon O. GIWA

引用本文:

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

链接本文:

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

Fig.1

Fig.2

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

Fig.3

Fig.4

Tab.2

Fig.5

Tab.3

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