Application of machine learning technologies in biodiesel production process—A review - Awogbemi, Omojola; Kallon, Daramy Vandi Von (Frontiers in Energy Research, 2023)
The search for renewable, affordable, sustainable, and ecologically benign fuels to substitute fossil-based diesel fuels has led to increased traction in the search for biodiesel production and utilization in recent times. Biodiesel, a form of liquid biofuel, has been found to alleviate environmental degradation, enhance engine performance, and reduce emissions of toxic gases in transportation and other internal combustion engines. However, biodiesel production processes have been dogged with various challenges and complexities which have limited its expected progression. The introduction of data-based technologies is one of the remedies aimed at deescalating the challenges associated with biodiesel synthesis. In this study, the application of ML–based technologies including artificial neural network (ANN), response surface methodology (RSM), adaptive network-based fuzzy inference system (ANFIS), etc. as tools for the prediction, modeling, and optimization of the biodiesel production process was interrogated based on the outcomes of previous studies in the research domain. Specifically, we review the influence of input variables like alcohol : oil molar ratio, catalyst concentration, reaction temperature, residence time, and agitation speed on the biodiesel yield (output variable). The outcome of this investigation shows that the usage of ANN, RSM, ANFIS, and other ML technologies contribute to making of reliable and accurate decisions on the process parameters leading to improved biodiesel yield, reduction in material wastage, and lower production cost. Going forward, more targeted and collaborative researches are needed to escalate the use of innovative technologies for the entire biodiesel value chain to enhance production efficiency, ensure economic feasibility, and promote ecofriendly sustainability.