A recommendation for harnessing adaptive neuro-fuzzy inference systems for biodiesel production: Advances and perspectives

By lowering reliance on fossil fuels and lessening environmental effects, biodiesel has a substantial potential to support the long-term sustainability of transportation fuels. However, the manufacture of biodiesel entails extremely complicated, multivariable, and nonlinear processes, especially during transesterification, when process parameters interact in complex ways. These nonlinear interactions are frequently difficult for traditional modelling and optimisation approaches to correctly represent, underscoring the need for reliable, flexible, and intelligent modelling tools. A potent method for simulating such intricate biodiesel production systems is Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which combines the reasoning structure of fuzzy logic with the learning capacity of artificial neural networks. With a focus on process modelling, prediction, and optimisation of key performance indicators like biodiesel yield, conversion efficiency, viscosity, and fuel properties, this review offers a thorough assessment and critical discussion of ANFIS applications in biodiesel research. ANFIS-based models applied to diverse feedstocks, such as edible, non-edible, waste oils, and microalgal sources, under various transesterification pathways are comprehensively analysed in the research. The enhanced prediction accuracy and flexibility of ANFIS models are further shown through comparisons with independent machine learning approaches and conventional statistical methods. The paper also addresses existing constraints, methodological shortcomings, and difficulties related to real-time implementation, model generalisation, and data quality. The integration of ANFIS with optimisation frameworks, hybrid modelling techniques, and real-time tracking systems for industrial-scale biodiesel production is highlighted as a future research objective. The methodical synthesis of ANFIS-based modelling strategies with a concentrated emphasis on their integration with optimisation frameworks and hybrid intelligent techniques in biodiesel production is what makes this review novel. In contrast to previous reviews, this study offers a critical and comparative assessment of ANFIS performance across various feedstocks and modelling approaches, providing deeper insights into its limitations and practical applicability. This review's conclusions provide a solid basis for developing data-driven, intelligent optimisation techniques in sustainable biofuel production systems. The results validate the excellent predictive performance and optimisation capability of ANFIS and its hybrid variations, making them effective instruments for promoting sustainable and efficient biodiesel production. Overall, this analysis highlights the increasing importance of ANFIS as a trustworthy decision-support tool and offers tactical suggestions for maximising its potential to improve the viability, sustainability, and efficiency of biodiesel manufacturing operations.