Mathematical Modeling and Optimization Studies by Artificial Neural Network, Genetic Algorithm and Response Surface Methodology: A Case of Ferric Sulfate–Catalyzed Esterification of Neem (Azadirachta indica) Seed Oil

Kelechi E Okpalaeke,Lekan M Latinwo,Taiwo H Ibrahim,Eriola Betiku

doi:10.3389/fenrg.2020.614621

Abstract

High free fatty acids (FFA) content in oils poses challenges such as soap formation and difficulty in the separation of by-products in direct transesterification of oil to biodiesel, which is of environmental concern and also increases the cost of production. Thus, in this study, the ferric sulfate-catalyzed esterification of neem seed oil (NSO) with an FFA of 5.84% was investigated to reduce it to the recommended level of ≤1%. The esterification process for the NSO was modeled using response surface methodology (RSM) and artificial neural network (ANN). The effect of the pertinent process input variablesviz.methanol/NSO molar ratio (10:1–30:1), ferric sulfate dosage (2–6 wt%), and reaction time (30–90 min) and their interactions on the reduction of the FFA of the NSO, were examined using Box Behnken design. The optimal condition for the process for reducing the FFA content of the oil was established using RSM and ANN-genetic algorithm (ANN-GA). The results showed that the models developed described the process accurately with the coefficient of determination (R2) of 0.9656 and 0.9908 and the mean relative percent deviation (MRPD) of 6.5 and 2.9% for RSM and ANN, respectively. The ANN-GA established the optimum reduction of FFA of 0.58% with methanol/NSO molar ratio of 18.51, ferric sulfate dosage of 6 wt%, and reaction time of 62.8 min as against the corresponding values of 0.62% FFA, 23.5, 5.03, and 75 min established by the RSM. Based on the statistics considered in the study, ANN and GA outperformed RSM in modeling and optimization of the NSO esterification process.

Highlights

In the past decade, considerable attention has been focused on renewable and sustainable alternative fuel by various governments and the scientific community because of the finite nature of fossil-based fuels, insecurity of supply, and attendant environmental concerns (Rashid et al, 2011; Merlin et al, 2015)
Description of the Esterification Process Based on the experimental design, specific volume of the neem seed oil (NSO) was measured into a 500-ml three-necked glass flask which served as the reactor and was heated to a temperature of 100°C on a magnetic stirrer with a hot plate
The comparative performance of response surface methodology (RSM) and artificial neural network (ANN) to predict the extent of free fatty acids (FFA) reduction during esterification of NSO, with methanol and ferric sulfate as a solid acid heterogeneous catalyst, was evaluated in this work

Summary

Introduction

Considerable attention has been focused on renewable and sustainable alternative fuel by various governments and the scientific community because of the finite nature of fossil-based fuels, insecurity of supply, and attendant environmental concerns (Rashid et al, 2011; Merlin et al, 2015). Biodiesel is a potential alternative fuel considered as a credible replacement or supplement to fossil-based fuels in transportation and internal combustion engines (Sani et al, 2013). It is non-toxic, biodegradable, produces less combustion emission, and its usage in internal combustion engines does not require engine modifications (Niu et al, 2018; Ofoefule et al, 2019). Since the cost of feedstock accounts for 75–80% of the total operating cost of biodiesel production (Lisboa et al, 2014), lots of research efforts are focusing on the exploration of cheaper, non-edible feedstock alternatives (Rincón et al, 2014)

Methods

Results

Conclusion