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<p>The increasing global generation of municipal, industrial, and agricultural waste, coupled with rising energy demand and stringent environmental regulations, has accelerated the development of intelligent Waste-to-Energy (WtE) systems. Although WtE technologies including incineration, gasification, pyrolysis, anaerobic digestion, and plasma conversion, offer significant potential for sustainable waste management and renewable energy production, their performance is constrained by feedstock heterogeneity, nonlinear process dynamics, emission control requirements, and economic uncertainty. Artificial Intelligence (AI) has emerged as a transformative enabler capable of addressing these challenges through advanced data-driven modeling, optimization, and decision-making frameworks. This review provides a comprehensive and critical synthesis of AI applications across the WtE value chain, including feedstock characterization, process optimization, intelligent control, emission monitoring, predictive maintenance, and techno-economic planning. Techniques such as machine learning, deep learning, reinforcement learning, evolutionary algorithms, and hybrid physics-informed models are systematically evaluated. The analysis shows that AI-based approaches can improve energy efficiency by 5–15%, reduce emissions by 10–25%, and decrease operational downtime by up to 40%, while enhancing prediction accuracy beyond 90% in key applications. Beyond summarizing existing studies, this review contributes a unified systems-level perspective and quantitative comparison of AI techniques, while identifying critical challenges related to data quality, model interpretability, cybersecurity, transfer learning, and regulatory integration. Emerging trends, including digital twins, edge AI, explainable AI, and large language model-driven decision support, are highlighted as key enablers of next-generation WtE systems. Overall, the integration of AI into WtE systems represents a paradigm shift from reactive operation to predictive, adaptive, and autonomous management, supporting circular economy strategies and global decarbonization goals.</p>

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Artificial Intelligence applications for optimization control and decision making in Waste to Energy systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月29日

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  • Afam Uzorka,
  • Mundu Muhamad Mustafa,
  • Saphina Biira,
  • Tyoyima John Ayua,
  • David Kibirige,
  • Oluwole Akiyode,
  • Musa Bawa,
  • Living Ounyesiga

摘要

The increasing global generation of municipal, industrial, and agricultural waste, coupled with rising energy demand and stringent environmental regulations, has accelerated the development of intelligent Waste-to-Energy (WtE) systems. Although WtE technologies including incineration, gasification, pyrolysis, anaerobic digestion, and plasma conversion, offer significant potential for sustainable waste management and renewable energy production, their performance is constrained by feedstock heterogeneity, nonlinear process dynamics, emission control requirements, and economic uncertainty. Artificial Intelligence (AI) has emerged as a transformative enabler capable of addressing these challenges through advanced data-driven modeling, optimization, and decision-making frameworks. This review provides a comprehensive and critical synthesis of AI applications across the WtE value chain, including feedstock characterization, process optimization, intelligent control, emission monitoring, predictive maintenance, and techno-economic planning. Techniques such as machine learning, deep learning, reinforcement learning, evolutionary algorithms, and hybrid physics-informed models are systematically evaluated. The analysis shows that AI-based approaches can improve energy efficiency by 5–15%, reduce emissions by 10–25%, and decrease operational downtime by up to 40%, while enhancing prediction accuracy beyond 90% in key applications. Beyond summarizing existing studies, this review contributes a unified systems-level perspective and quantitative comparison of AI techniques, while identifying critical challenges related to data quality, model interpretability, cybersecurity, transfer learning, and regulatory integration. Emerging trends, including digital twins, edge AI, explainable AI, and large language model-driven decision support, are highlighted as key enablers of next-generation WtE systems. Overall, the integration of AI into WtE systems represents a paradigm shift from reactive operation to predictive, adaptive, and autonomous management, supporting circular economy strategies and global decarbonization goals.