东南大学学报（自然科学版）2025，Vol.55Issue(3)：743-750,8.DOI:10.3969/j.issn.1001-0505.2025.03.014
机器学习在有机固废厌氧消化中的应用进展与展望
Application of machine learning in anaerobic digestion of organic solid waste:Recent advances and prospects
摘要
Abstract
To further enhance the applicability and predictive performance of machine learning(ML)models in the field of anaerobic digestion(AD),recent related research progress was systematically reviewed.The machine learning modeling process,the characteristics of various models,and their application value and limi-tations in AD were summarized,and the future research directions were prospected.The results indicate that common ML models(random forest,support vector machine and artificial neural network)have good applica-tion prospects in AD gas production performance prediction,process parameter optimization,process monitor-ing and control,etc.However,there are still problems such as insufficient data volume,easy overfitting,lack of interpretability and poor prediction performance in complex situations.Data can be generated through meth-ods such as generative adversarial networks and artificial intelligence algorithms to reduce the risk of overfit-ting.In addition,integrating existing mechanistic models and data-driven ML models,combined with bioin-formatics approaches such as metagenomics,can further enhance the predictive performance and interpretabil-ity of models.关键词
厌氧消化/甲烷产生/机器学习/建模/过程优化Key words
anaerobic digestion/methane production/machine learning/modeling/process optimization分类
资源环境引用本文复制引用
何晓满,郭静远,沈德魁,林日琛..机器学习在有机固废厌氧消化中的应用进展与展望[J].东南大学学报（自然科学版）,2025,55(3):743-750,8.基金项目
国家自然科学基金资助项目(52276177) （52276177）
能源高效清洁利用全国重点实验室开放基金课题资助项目(ZJUCEU2023008). （ZJUCEU2023008）
