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Data‐Driven Modeling of Biodiesel Production Using Artificial Neural Networks
Author(s) -
Mogilicharla Anitha,
Reddy P. Swapna
Publication year - 2021
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.202000434
Subject(s) - methanol , biodiesel , transesterification , biodiesel production , oleic acid , artificial neural network , myristic acid , catalysis , rapeseed , fatty acid methyl ester , chemistry , organic chemistry , process engineering , engineering , fatty acid , environmental science , biological system , computer science , biochemistry , artificial intelligence , food science , palmitic acid , biology
Data‐driven modeling of biodiesel production was developed by simultaneous transesterification and esterification of rapeseed oil and myristic acid with methanol, without catalyst or with different amounts of sulfated zirconia catalyst. An artificial neural network (ANN)‐based model was created with experimental literature data. The input data, i.e., reaction time, catalyst, temperature, and methanol‐to‐oil ratio, and output data, i.e., total fatty acid methyl ester and oleic acid methyl ester, were considered to develop the model. Multiple input single output (MISO) ANN architecture was taken to predict the above targeted two output parameters. The proposed ANN model is computationally efficient and works reasonably well when tested on biodiesel production for solving the MISO model.