Biowaste valorization: Integrating circular economy principles with artificial intelligence-driven optimization for sustainable energy solutions

Biowaste, encompassing food waste and agricultural residues, poses significant environmental challenges while offering transformative opportunities. Traditionally relegated to landfills or incineration, biowaste is increasingly recognized as a renewable resource for producing biofuels, biochemicals, biomaterials, and animal feed. This review offers a systems-level analysis of biowaste characteristics, conversion processes, and derived bioenergy products such as biofuels, biogas, biodiesel, biohydrogen, bioelectricity, and other valuable chemicals. Diverse conversion methods are explored, including anaerobic digestion, fermentation, microbial fuel cells, pyrolysis, and gasification, alongside conventional practices like landfilling and incineration. Emerging analytical advancements are revolutionizing biowaste valorization. Artificial intelligence (AI) and machine learning (ML) techniques are highlighted for their transformative impact. For example, AI-driven optimization of pyrolysis conditions has enhanced biochar yield and quality, while predictive modeling using neural networks has improved the efficiency of anaerobic digestion. Additionally, techno-economic analysis (TEA) demonstrates a significant reduction in operational costs through AI-driven process optimization, and life cycle assessment (LCA) quantifies reductions in environmental impacts, such as greenhouse gas emissions and energy consumption, due to AI-informed process adjustments. Advanced assessment tools, including material flow analysis (MFA), further evaluate resource dynamics within biowaste-to-energy systems. Artificial intelligence and ML optimize waste processing, improving efficiency and product quality. Key analytical tools like TEA, LCA, and MFA assess the financial and environmental viability of these processes.