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<p>Anaerobic digestion (AD) and waste-to-energy (WTE) technologies could convert organic waste into renewable energy sustainably and can be able to meet the goals of the circular bioeconomy. Though, inefficiency and instability in the process, utilization of heterogeneous feedstocks, and limited real-time control are among the factors preventing the improvement and large-scale deployment of these technologies. This paper is an attempt to review the literature and assess the function of artificial intelligence (AI) in the enhancement of waste preprocessing, process prediction monitoring control, and operational reliability in AD and WTE systems. The reviewed studies reveal that AI-based waste sorting can reach an accuracy of 92, 97% and the level of cross-contamination can be decreased to &lt; 5% through the use of AI-assisted preprocessing, which allows for an increase in energy recovery by around 15, 18%. Machine learning and deep learning models are capable of reducing the prediction errors in AD systems by about 20, 30% against traditional kinetic methods. This, in turn, makes easier the earlier detection of process instability, control improvement of methane production, organic loading rate, hydraulic retention time, and inhibitory conditions. The combination of IoT-enabled sensing, soft sensing, predictive maintenance, reinforcement learning, and digital twins is made possible mostly for real-time monitoring and adaptive process optimization. Yet, the panel of experts at the conference concluded that the industrial deployment is still challenging due to the heterogeneity of data, limited transferability of models, high computational and infrastructure costs, low interpretability of models, as well as data-governance issues that have not been resolved yet. In the future, research efforts should aim at the standardization of data systems, the development of interpretable and cost-effective AI architectures, multi-site field validation, and privacy-preserving federated learning. To sum up, AI deployment can contribute to the speedy transformation of AD and WTE systems into intelligent, adaptive, and commercially viable platforms for circular bioeconomy development.</p>

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Harnessing artificial intelligence-driven optimization of anaerobic digestion and waste-to-energy systems for advancing circular bioeconomy strategies

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月5日

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  • Joselyn B. C. Toomey,
  • Rachan Karmakar,
  • Suman Naithani,
  • Monica Gangopadhyay,
  • Debasis Mitra,
  • Arkadeep Mukherjee,
  • Rajat Singh,
  • Addisu Assefa

摘要

Anaerobic digestion (AD) and waste-to-energy (WTE) technologies could convert organic waste into renewable energy sustainably and can be able to meet the goals of the circular bioeconomy. Though, inefficiency and instability in the process, utilization of heterogeneous feedstocks, and limited real-time control are among the factors preventing the improvement and large-scale deployment of these technologies. This paper is an attempt to review the literature and assess the function of artificial intelligence (AI) in the enhancement of waste preprocessing, process prediction monitoring control, and operational reliability in AD and WTE systems. The reviewed studies reveal that AI-based waste sorting can reach an accuracy of 92, 97% and the level of cross-contamination can be decreased to < 5% through the use of AI-assisted preprocessing, which allows for an increase in energy recovery by around 15, 18%. Machine learning and deep learning models are capable of reducing the prediction errors in AD systems by about 20, 30% against traditional kinetic methods. This, in turn, makes easier the earlier detection of process instability, control improvement of methane production, organic loading rate, hydraulic retention time, and inhibitory conditions. The combination of IoT-enabled sensing, soft sensing, predictive maintenance, reinforcement learning, and digital twins is made possible mostly for real-time monitoring and adaptive process optimization. Yet, the panel of experts at the conference concluded that the industrial deployment is still challenging due to the heterogeneity of data, limited transferability of models, high computational and infrastructure costs, low interpretability of models, as well as data-governance issues that have not been resolved yet. In the future, research efforts should aim at the standardization of data systems, the development of interpretable and cost-effective AI architectures, multi-site field validation, and privacy-preserving federated learning. To sum up, AI deployment can contribute to the speedy transformation of AD and WTE systems into intelligent, adaptive, and commercially viable platforms for circular bioeconomy development.