Artificial intelligence and machine learning models application in biodiesel optimization process and fuel properties prediction

Abstract

Inefficient transesterification, low-quality fuel properties, and high resource consumption are the bottlenecks associated with conventional biodiesel production. The current research trends include the application of artificial intelligence (AI) and machine learning (ML) to optimize the biodiesel process for improved yield and fuel quality. Previous reviews discussed the applications of ML in the optimization of transesterification parameters and fuel properties. However, there is a lack of deep discussion on feedstock selection, optimization, process monitoring, and cost analysis. The challenges during biodiesel production, ML model selection, and assessment of plant and animal lipid potential for biodiesel under different conditions using AI tools are reviewed. All the parameters that affect biodiesel yield and fuel properties through ML, the efficiency of different models, and pilot-scale techno-economic analyses are also discussed. Biodiesel production from animal and plant lipids showed high yield potential ranging from 78-99 %. ML models demonstrated higher efficacy in transesterification optimization to attain > 90 % yield. Various AI models exhibit a predictive efficiency range (R² = 0.85 to 0.99) for yield and fuel qualities. Economic analyses reveal that the choice of feedstock and catalyst significantly impacts final production costs. ML and AI approaches exhibit the potential for improving the biodiesel process.