Agri-food waste valorisation is constrained by feedstock heterogeneity, process uncertainty, volatile markets, and evolving regulation, which collectively increase business risk and delay market entry. This review synthesizes evidence on how artificial intelligence (AI) reduces these uncertainties and enables opportunity identification and decision support across the valorisation value chain. At intake, AI coupled with rapid sensing supports batch-level characterization and variabilityaware quality prediction, improving logistics and operating-window selection. For process development, surrogate models and multi-objective optimization emerge as effective tools to navigate yield–cost–energy–sustainability trade-offs and to speed scale-up decisions under sparse data. At operation, digital twins and adaptive control, including reinforcement learning, strengthen robustness to disturbances and performance drift. Commercially, natural language processing (NLP) and network analytics enable quantitative market intelligence by mining patents, literature, consumer discourse, and regulatory texts to map trends, competitors, “white spaces”, and adoption barriers. Across applications, the review identifies data governance, transferability, interpretability, and rigorous validation as the main deployment bottlenecks, and outlines a roadmap toward deployable AI-assisted business strategies in circular bioeconomy markets.
AI-Assisted Business Strategies and Markets for Products Derived from Agri-Food Waste and By-Products
Bartolomeo Cosenza;
2026-01-01
Abstract
Agri-food waste valorisation is constrained by feedstock heterogeneity, process uncertainty, volatile markets, and evolving regulation, which collectively increase business risk and delay market entry. This review synthesizes evidence on how artificial intelligence (AI) reduces these uncertainties and enables opportunity identification and decision support across the valorisation value chain. At intake, AI coupled with rapid sensing supports batch-level characterization and variabilityaware quality prediction, improving logistics and operating-window selection. For process development, surrogate models and multi-objective optimization emerge as effective tools to navigate yield–cost–energy–sustainability trade-offs and to speed scale-up decisions under sparse data. At operation, digital twins and adaptive control, including reinforcement learning, strengthen robustness to disturbances and performance drift. Commercially, natural language processing (NLP) and network analytics enable quantitative market intelligence by mining patents, literature, consumer discourse, and regulatory texts to map trends, competitors, “white spaces”, and adoption barriers. Across applications, the review identifies data governance, transferability, interpretability, and rigorous validation as the main deployment bottlenecks, and outlines a roadmap toward deployable AI-assisted business strategies in circular bioeconomy markets.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
