Azadirachta indica seed oil epoxidation process using carbonized melon seed peel catalyst; genetic algorithm coupled artificial neural network approach

Abstract

The study used ANN-GA and RSM to predict the best process parameters for generating epoxide from Azadirachta indica seed oil (AISO). This procedure used carbonized sulphonated melon seed peel catalyst. FTIR, SEM, XRD, BET, and XRF measurements confirm the -SO3H group's attachment to the solid catalyst. The dependant variable was relative conversion to oxirane (RCO), while the independent parameters were catalyst dosage (0.6, 1.2, 1.8 wt %), time (4, 6, 8 h), and temperature (50°C, 60°C, 70°C). The ANN was evaluated using 11 backpropagation (BP) methods. Each method was examined with three input layer neurons for catalyst dosage, duration, and temperature. Ten neurons were in the hidden layer and one was in the output layer signifying RCO. The AISO epoxidation process forecast was most accurate using Bayesian regularisation. Simulated RSM and ANN models were built using experimental and algorithmic designs. The 3D plots showed that process parameters significantly affected RCO. R2 and MSE were used to evaluate model performance. For process forecasting, the ANN model (R2=0.9999, MSE=2.3404E-13) outperforms the RSM model (R2=0.9979, MSE=0.4688). Under the best RSM circumstances, RCO yield was 78.03 %. Additionally, the ANN and ANN-GA yielded 85.84 % and 92.51 %, respectively at optimal conditions of 0.6 wt % catalyst, 50°C temperature, and 6 h reaction time. However, all techniques optimized AISO and matched experimental results (RCO-77.41 %). FT-IR and GCMS characterizations of epoxy AISO corroborated the oxirane ring's attachment. The results show that ANN-GA is a reliable method for modelling and optimizing AISO epoxide production utilizing CSMSPC, encouraging sustainable development.