AIoE-Enhanced Multi-objective Optimization for Sustainable Bioprocesses in Smart Bioreactors

The increasing demand for sustainable biomanufacturing has driven the integration of Artificial Intelligence of Everything (AIoE) and Digital Twin (DT) technologies into smart bioreactors. Traditional bioprocess control systems often struggle to simultaneously optimize bioproduct yield, energy consumption, and operational costs, as these objectives can sometimes conflict. This study introduces an AIoE-powered multi-objective optimization framework that leverages real-time digital twins and metaheuristic algorithms to enhance bioprocess management. Four advanced optimization techniques—NSGA-II, Firefly Optimization Algorithm (FOA), Greedy Man Optimization Algorithm (GMOA), and Bat Optimization Algorithm (BOA)—are evaluated across various problem scenarios. The findings indicate that GMOA consistently outperforms the other algorithms, demonstrating faster convergence, lower energy consumption, reduced operational expenses, and improved bioproduct yield. Statistical significance tests, including T-tests at a 95% confidence level, confirm that GMOA delivers superior bioprocess optimization. Moreover, the integration of digital twins enables real-time process simulation and adaptive control, helping to reduce uncertainties and enhance overall system sustainability. This research highlights how the combination of AIoE-driven digital twins and advanced optimization techniques provides an effective solution for real-time bioprocess optimization. The proposed framework advances autonomous, self-optimizing smart bioreactors, offering scalable applications in biopharmaceuticals, enzyme production, and biofuel synthesis.