Modelling of Nicotiana Tabacum L. Oil Biodiesel Production: Comparison of ANN and ANFIS

Among the modern computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are preferred because of their ability to deal with non-linear modelling complex stochastic dataset. Nondeterministic models involve some complexities while solving real-life problems but would always produce better outcomes. For first time, this study utilized ANN ANFIS for tobacco seed oil methyl ester (TSOME) production from underutilized seeds in tropics. The dataset was obtained an earlier which focused on design experiment TSOME production. This is exposition influence transesterification parameters such as reaction duration (T), methanol/oil molar ratio (M:O), catalyst dosage TSOME/biodiesel yield. A multi-layer model ten hidden layers trained simulate methanolysis process. approach further implemented comparison formulated completed by statistical criteria coefficient determination (R 2 ), mean average error (MAE), absolute deviation (AAD). R 0.8979, MAE 4.34468, AAD 6.0529 compared those 0.9786, 1.5311, 1.9124 model. appears be more reliable than predicting