Premium
Evaluation of engine performance and emission of African pear seed oil ( APO ) biodiesel and its prediction via multi‐input‐multi‐output artificial neural network ( ANN ) and sensitivity analysis
Author(s) -
Ude Callistus so,
Onukwuli O Dominic,
Uchegbu N Nneka,
Umeuzuegbu Jonah C,
Amulu Ndidi F
Publication year - 2021
Publication title -
biofuels, bioproducts and biorefining
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.931
H-Index - 83
eISSN - 1932-1031
pISSN - 1932-104X
DOI - 10.1002/bbb.2200
Subject(s) - biodiesel , artificial neural network , diesel fuel , pear , diesel engine , transesterification , engineering , gasoline , mathematics , chemistry , computer science , automotive engineering , horticulture , artificial intelligence , waste management , biology , biochemistry , catalysis
The evaluation of engine performance and emission of biodiesel produced from African pear seed oil (APO) and its prediction using multi‐input‐multi‐output (MIMO) artificial neural network (ANN) technique was studied. A standard diesel test bed was used to carry out the evaluation using petrol‐diesel, biodiesel, and its blends, operating at various engine revolutions per minute. Sensitivity analysis used the connection weight method. The performance results showed that petro‐diesel blended with a small amount of APO methyl ester (B20) improved the engine efficiency and emissions better than only petrol‐diesel. The engine performance was predicted well, with the MIMO model having better correlation coefficients of 0.99779, 0.99993, and 0.99774 on training, validation, and testing, respectively, than the Multiple input Single Output (MISO) model. The desired outputs compared between their measured and simulated values exhibited a very low mean square error of 0.14488. The blend had the highest relative impact (62.08%) on the output variables. The MIMO model has therefore shown the potential to adopt two input variables equally in predicting the engine performance of biodiesel rather than only complex variables. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd