Abstract

This paper proposes a conceptual model for regenerating biodiesel from agricultural feedstock and waste materials, integrating circular-economy design with process intensification and digital optimization. The model comprises three layers: (i) feedstock valorization, (ii) catalytic conversion and purification, and (iii) data-driven control and sustainability assessment. In layer one, lipid-rich crops, used cooking oils, animal fats, and lignocellulosic residues are screened via multi-criteria decision analysis to balance cost, carbon intensity, free fatty acid content, and regional availability. Pre-treatment steps degumming, drying, deacidification, and particle-size reduction standardize variable inputs and reduce downstream fouling. Layer two specifies flexible transesterification and esterification trains employing bifunctional solid acid–base catalysts, reactive distillation, and ultrasound or microwave assistance to accelerate kinetics, limit soap formation, and shift equilibria. Membrane-assisted phase separation and deep eutectic solvent polishing minimize methanol loss and capture trace metals, water, and particulates. Closed-loop solvent recovery and glycerol upcycling into epoxides, biopolymers, or green hydrogen feed expand revenue and reduce waste liabilities. Layer three overlays a cyber-physical control architecture. Soft sensors estimate FFA, moisture, viscosity, and catalyst activity, hybrid models combine first-principles kinetics with machine-learning surrogates to optimize residence time, alcohol-to-oil ratio, temperature, and regeneration cycles. A dynamic life-cycle assessment quantifies carbon intensity, water stress, and eutrophication in near-real time, while techno-economic modules stress-test margins under feedstock price swings, carbon policies, and renewable-fuel credit markets. Governance and finance elements align with ISO 14040/44 and ISO 20400, enabling sustainability-linked procurement, traceable chains-of-custody, and community co-ownership. A resilience submodule introduces scenario planning for feedstock shocks, catalyst fouling, and policy shifts, using stochastic optimization to sustain ≥95% uptime. Deployment favors hub-and-spoke micro-refineries proximate to cluster farms and urban waste streams, reducing transport emissions and creating rural and peri-urban jobs. The model advances the field by translating dispersed methods into a reproducible blueprint adaptable to local agronomy and waste baselines. It also defines measurable key performance indicators conversion efficiency, energy return on energy invested, carbon intensity, and social co-benefits to drive continuous improvement and transparent reporting. Validation will pair pilots with sensitivity analyses and uncertainty quantification to establish replicability, bankability, and policy relevance. Results will inform standards, incentives, and equitable market participation.