Artificial Neural Network Approach to Predict Biodiesel Production using Algae-Based Heterogeneous Catalyst

The rising dependence on fossil fuels has intensified environmental issues such as greenhouse gas emissions and resource depletion. Biodiesel offers a renewable alternative with lower emissions. However, conventional biodiesel production are sensitive to free fatty acids and water, causing soap. Heterogeneous catalysts derived from biomass provide a cleaner and reusable alternative. In this study, Ulva lactuca, a green macroalga with rapid growth and no need for arable land or fertilizers, was used as a sustainable source for catalyst preparation. This research integrates an U. lactuca-based heterogeneous catalyst with an Artificial Neural Network (ANN) to predict biodiesel yield under different process conditions. The objective was to develop a robust predictive model for biodiesel production from waste cooking oil. Transesterification was performed at 50–70 °C, catalyst loadings of 2–5 wt%, and reaction times of 60–180 min, with a fixed methanol-to-oil ratio of 6:1. The ANN, trained using the Levenberg–Marquardt algorithm in MATLAB R2022a, achieved an optimal architecture of 4–18–1. The model showed excellent predictive accuracy, with R values of 0.9989, 0.9969, 0.9980, and 0.9987 for training, validation, testing, and overall datasets, and minimum MSE values of 2.81 × 10⁻⁴. The highest experimental biodiesel yield of 0.96 mol mol⁻¹ closely matched the ANN-predicted yield of 0.97 mol mol⁻¹ at 60 °C, 90 min, and 4 wt% catalyst loading. These results confirm the ANN’s strong predictive capability and demonstrate its potential for optimizing biodiesel production using sustainable algae-based catalysts.