Abstract:
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 and complex stochastic dataset. Nondeterministic
models involve some computational complexities while solving real-life problems but
would always produce better outcomes. For the first time, this study utilized the ANN
and ANFIS models for modelling tobacco seed oil methyl ester (TSOME) production from
underutilized tobacco seeds in the tropics. The dataset for the models was obtained from
an earlier study which focused on design of the experiment on TSOME production. This
study is an an exposition of the influence of transesterification parameters such as reaction
duration (T), methanol/oil molar ratio (M:O), and catalyst dosage on the TSOME/biodiesel
yield. A multi-layer ANN model with ten hidden layers was trained to simulate the
methanolysis process. The ANFIS approach was further implemented to model
TSOME production. A comparison of the formulated models was completed by
statistical criteria such as coefficient of determination (R2), mean average error (MAE),
and average absolute deviation (AAD). The R2 of 0.8979, MAE of 4.34468, and AAD of
6.0529 for the ANN model compared to those of the R2 of 0.9786, MAE of 1.5311, and
AAD of 1.9124 for the ANFIS model. The ANFIS model appears to be more reliable than the
ANN model in predicting TSOME production in the tropics.
