Kinetic modeling in biofuel production: A critical review and roadmap for model selection

Abstract

Kinetic modeling plays a central role in interpreting experimental behaviour and supporting reactor design in biofuel production systems. Commonly applied models range from empirical curve-fitting equations to mechanistically derived growth models, each constructed upon distinct theoretical assumptions. However, inappropriate model selection can compromise interpretability and predictive reliability beyond the experimental domain. Thus, kinetic model selection should be guided by scientific suitability rather than conventional or statistical convenience. This review provides a critical and comparative evaluation of kinetic models applied in biofuel production systems by comparing their mechanistic basis, parameter identifiability, data requirements, and extrapolation capability. Empirical models such as First-order, Logistic and Gompertz-type models are assessed for their descriptive accuracy but limited mechanistic insight. Growth-based models like Monod model capable of representing substrate consumptions, evaluated for their ability to represent biological and biochemical constraints, while highlighting persistent challenges related to data availability, parameter identifiability, and validation. More structured and hybrid approaches, such as Luedeking-Pirettype model examined for their ability to couple microbial growth and product formation under data-rich conditions. Systematic comparison shows that model suitability is governed primarily by research objective, system complexity, and experimental resolution rather than by conventional usage. On this basis, a decision-oriented framework is developed to guide context-specific kinetic model selection to enhance methodological rigor in biofuel process analysis