Response Surface Methodology and Artificial Neural Networks-Based Yield Optimization of Biodiesel Sourced from Mixture of Palm and Cotton Seed Oil

Artificial Neural Network and Response Surface Methodology for Predicting and Maximizing Biodiesel Production from Waste Oil with KI/CaO/Al2O3 Catalyst in a Fixed Bed Reactor

Biodiesel from waste oil is produced using heterogeneous catalyzed transesterification in a fixed bed reactor (FBR). Potassium iodide/calcium oxide/alumina (KI/CaO/Al2O3) catalyst was prepared through the processes of calcination and impregnation. The novel catalyst was analyzed with X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive X-ray spectrometer (EDX). The design of experiment (DoE) method resulted in a total of 20 experimental runs. The significance of 3 reaction parameters, namely catalyst bed height, methanol to waste oil molar ratio, and residence time, and their combined impact on biodiesel yield is investigated. Both the artificial neural network (ANN) based on artificial intelligence (AI) and the Box-Behnken design (BBD) based on response surface methodology (RSM) were utilized in order to optimize the process conditions and maximize the biodiesel production. A quadratic regression model was developed to predict biodiesel yield, with a correlation coefficient (R) value of 0.9994 for ANN model and a coefficient of determination (R2) value of 0.9986 for BBD model. The maximum amount of biodiesel that can be produced is 98.88 % when catalyst bed height is 7.87 cm, molar ratio of methanol to waste oil is 17.47 : 1, and residence time is 3.12 h. The results of this study indicate that ANN and BBD models can effectively be used to optimize and synthesize the highest %yield of biodiesel in a FBR.

Maximizing biodiesel yield of a non-edible chinaberry seed oil via microwave assisted transesterification process using response surface methodology and artificial neural network techniques

In this study, the non-edible Chinaberry Seed Oil (CBO) is converted into biodiesel using microwave assisted transesterification. The objective of this effort is to maximize the biodiesel yield by optimizing the operating parameters, such as catalyst concentration, methanol-oil ratio, reaction speed, and reaction time. The designed setup provides a controlled and effective approach for turning CBO into biodiesel, resulting in encouraging yields and reduced reaction times. The experimental findings reveal the optimal parameters for the highest biodiesel yield (95 %) are a catalyst concentration of 1.5 w/w, a methanol-oil ratio of 6:1 v/v, a reaction speed of 400 RPM, and a reaction period of 3 min. The interaction of the several operating parameters on biodiesel yield has been investigated using two methodologies: Response Surface Methodology (RSM) and Artificial Neural Network (ANN). RSM provides better modeling of parameter interaction, while ANN exhibits lower comparative error when predicting biodiesel yield based on the reaction parameters. The percentage improvement in prediction of biodiesel yield by ANN is found to be 12 % as compared to RSM. This study emphasizes the merits of both the approaches for biodiesel yield optimization. Furthermore, the scaling up this microwave-assisted transesterification system for industrial biodiesel production has been proposes with focus on its economic viability and environmental effects.

MgO Nano-Catalyzed Biodiesel Production from Waste Coconut Oil and Fish Oil Using Response Surface Methodology and Grasshopper Optimization

In India, a densely populated country, fossil fuel depletion affects the energy sector that fulfils the industrial and human needs. Concerning greenhouse gas emissions and pollutants, and sustainability, there is a great demand to search for alternate feedstocks to produce alternate fuels at a low cost. The present work focuses on waste coconut and fish oil as potential inexpensive feedstock for biodiesel production. Two-stage transesterification processes for biodiesel production from hybrid oils mixed in a 1:1 volume ratio by employing solid nano-catalyst Magnesium Oxide (MgO). Response surface methodology (RSM) was used to analyze the effects of the physics of transesterification variables, such as methanol-to-oil molar ratio (M:O), MgO catalyst concentration (MgO CC), and reaction temperature (RT), on biodiesel yield, based on experimental data gathered in accordance with the matrices of central composite design (CCD). MgO CC showed the highest contribution, followed by M:O and RT, to maximize biodiesel yield. All interaction factors showed a significant effect except the M:O with RT. Grasshopper optimization algorithm (GOA) determined optimal conditions (M:O: 10.65; MgO CC: 1.977 wt.%; RT: 80 °C) based on empirical equations, resulting in maximum biodiesel yield conversion experimentally equal to 96.8%. The physical stability of the MgO nano-catalyst and reactivity up to 5 successive cycles can yield 91.5% biodiesel yield, demonstrating its reusability for sustainable biodiesel production at low cost. The optimized biodiesel yield showed better physicochemical properties (tested according to ASTM D6751-15C) to use practically in diesel engines.