Renewable Energy and Resource Recovery Systems Using AI Tools

Abstract

The global energy transition toward sustainability demands innovative solutions that enhance efficiency, resilience, and circularity across energy and resource systems. This chapter explores the transformative role of Artificial Intelligence (AI) in optimizing renewable energy generation and resource recovery processes, highlighting the convergence of data-driven intelligence, automation, and digital infrastructure. AI techniques—ranging from machine learning and deep learning to reinforcement learning and fuzzy logic systems—are increasingly enabling predictive analytics, adaptive control, and process optimization in solar, wind, bioenergy, and hybrid energy systems. Furthermore, intelligent modeling and optimization approaches are driving progress in waste-to-energy conversion, wastewater nutrient recovery, and circular material flows, reinforcing the principles of the circular economy. The chapter presents case studies and frameworks that illustrate how AI tools enhance system performance, reduce operational costs, and support real-time decision-making. Challenges such as data quality, interpretability, and integration complexity are critically examined, along with emerging trends including digital twins, IoT–AI integration, and quantum-assisted energy analytics. Ultimately, the chapter emphasizes that the synergy between AI and sustainable resource technologies holds the potential to redefine global energy systems, paving the way toward a decarbonized and intelligent future.