Solar-Assisted Thermochemical Valorization of Agro-Waste to Biofuels: Performance Assessment and Artificial Intelligence Application Review

Abstract

The rapid growth and seasonal availability of agricultural materials, such as straws, stalks, husks, shells, and processing wastes, present both a disposal challenge and an opportunity for renewable fuel production. Solar-assisted thermochemical conversion, such as solar-driven pyrolysis, gasification, and hydrothermal routes, provides a pathway to produce bio-oils, syngas, and upgraded chars with substantially reduced fossil energy inputs compared to conventional thermal systems. Recent experimental research and plant-level techno-economic studies suggest that integrating concentrated solar thermal (CSP) collectors, falling particle receivers, or solar microwave hybrid heating with thermochemical reactors can reduce fossil auxiliary energy demand and enhance life-cycle greenhouse gas (GHG) performance. The primary challenges are operational intermittency and the capital costs of solar collectors. Alongside, machine learning (ML) and AI tools (surrogate models, Bayesian optimization, physics-informed neural networks) are accelerating feedstock screening, process control, and multi-objective optimization, significantly reducing experimental burden and improving the predictability of yields and emissions. This review presents recent experimental, modeling, and techno-economic literature to propose a unified classification of feedstocks, solar-integration modes, and AI roles. It reveals urgent research needs for standardized AI-ready datasets, long-term field demonstrations with thermal storage (e.g., integrating PCM), hybrid physics-ML models for interpretability, and region-specific TEA/LCA frameworks, which are most strongly recommended. Data’s reporting metrics and a reproducible dataset template are provided to accelerate translation from laboratory research to farm-level deployment.