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

Olusegun D Samuel,Mohsen Sharifpur,Modestus O Okwu,Solomon O Giwa,Zaid O O Jagun,Lagouge K Tartibu

doi:10.3389/fenrg.2020.612165

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.

Highlights

Notable properties associated with methyl and ethyl esters of oils such as renewability, environmental friendliness, and others, have propelled researchers and scientific communities to substitute such renewable fuels for diesel fuel in diesel and automotive engines (Enweremadu and Mbarawa, 2009; Huang et al, 2012; Huang et al, 2019; Jayaprabakar et al, 2019; Shrivastava and Verma, 2019)
The study established the application of the Artificial Neural Network (ANN) model in comparison with the hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) in the modelling of transesterification parameters of biodiesel production from tobacco seed oil (TSO)
The efficacy of the ANN and ANFIS models was assessed based on the statistical indices such as R2, mean average error (MAE), and AD

Summary

Introduction

Notable properties associated with methyl and ethyl esters of oils such as renewability, environmental friendliness, and others, have propelled researchers and scientific communities to substitute such renewable fuels for diesel fuel in diesel and automotive engines (cars, trucks, farm machinery, marine vessels, and even aircraft) (Enweremadu and Mbarawa, 2009; Huang et al, 2012; Huang et al, 2019; Jayaprabakar et al, 2019; Shrivastava and Verma, 2019). Clean and sparkling oily feedstock has been a preferred option for investigation by researchers in terms of biodiesel production since the oils possess the shortest reaction time and do not need pre-treatment prior to the base transesterification (Giwa et al, 2010). Non-edible seed oils from castor, rubber, jatropha, tobacco seed, and others have been considered for biodiesel production globally. This consideration is associated with non-edible oils as they can lessen the cost of biodiesel production. Among non-edible oils, tobacco seed oil (TSO) seemed appealing for biofuel production which is often observed to possess close basic properties with diesel fuel (Andrianov et al, 2010). The literature (Giannelos et al, 2002; Moser, 2009) remarked on the viability of biodiesel production from TSO

Methods

Results

Conclusion