In Bioresource technology
Anaerobic digestion (AD) is a promising technology for recovering value-added resources from organic waste, thus achieving sustainable waste management. The performance of AD is dictated by a variety of factors including system design and operating conditions. This necessitates developing suitable modelling and optimization tools to quantify its off-design performance, where the application of machine learning (ML) and soft computing approaches have received increasing attention. Here, we succinctly reviewed the latest progress in black-box ML approaches for AD modelling with a thrust on global and local model interpretability metrics (e.g., Shapley values, partial dependence analysis, permutation feature importance). Categorical applications of the ML and soft computing approaches such as what-if scenario analysis, fault detection in AD systems, long-term operation prediction, and integration of ML with life cycle assessment are discussed. Finally, the research gaps and scopes for future work are summarized.
Gupta Rohit, Zhang Le, Hou Jiayi, Zhang Zhikai, Liu Hongtao, You Siming, Sik Ok Yong, Li Wangliang
2022-Dec-08
Artificial intelligence, Bioenergy, Data-driven Modelling, Renewable energy, Sustainable waste management
