能源环境保护2026，Vol.40Issue(2)：36-47,12.DOI:10.20078/j.eep.20260315
基于人工智能的有机固废资源化技术研究进展
Research Progress in AI-Based Technologies for Organic Solid Waste Resource Recovery
摘要
Abstract
Global generation of organic solid waste(OSW)is rapidly increasing due to population growth and urbanization,posing severe environmental risks when improperly managed.Accordingly,developing sustainable and efficient resource recovery strategies is essential to enable a circular economy.Thermal treatment technologies—primarily pyrolysis,gasification,and incineration—are critical and efficient conversion routes that substantially reduce waste volume and convert heterogeneous organic feedstocks into high-value biofuels,syngas,and biochemicals.However,the intrinsic heterogeneity of OSW and the complex,nonlinear multiphase reactions involved in thermal processes limit the accuracy and applicability of traditional kinetic and statistical models.In this context,advanced artificial intelligence(AI)techniques have emerged as an area of growing interest in environmental engineering and energy research for enabling intelligent,precise,and robust resource recovery.This review systematically evaluates recent advances in AI methods applied to OSW resource recovery,with particular emphasis on applications in core thermal treatment pathways.We critically examine the performance of mainstream machine learning and deep learning algorithms—including artificial neural networks(ANNs),random forests(RFs),support vector machines(SVMs),and state-of-the-art deep learning architectures—across diverse thermal scenarios.Analyses indicate that,compared with conventional statistical models,AI-assisted approaches can improve feedstock property prediction accuracy by approximately 15％on average and can more reliably predict pyrolysis product distributions.Nevertheless,significant challenges persist in cross-scale,multi-source data fusion and in maintaining dynamic adaptability under fluctuating industrial conditions.AI also contributes to real-time optimization of operational conditions and to intelligent control of secondary pollutant emissions(e.g.,nitrogen oxides and dioxins).Beyond single-reactor applications,we summarize broader AI-enabled developments,including dynamic life cycle assessment(LCA)frameworks and digital twin systems that couple multi-sensor data with AI to provide comprehensive environmental impact assessments and to support sustainable decision-making.We further identify key bottlenecks that hinder industrial-scale deployment,notably the scarcity of standardized,high-quality industrial datasets and the limited mechanistic interpretability of black-box models.Finally,we propose corresponding solutions and research directions to facilitate the deeper integration of AI with thermal treatment technologies,thereby promoting efficient,high-value,and intelligent resource recovery of OSW.关键词
人工智能/热处置/数字孪生/生命周期评价/资源化Key words
Artificial intelligence(AI)/Thermal treatment/Digital twin/Life cycle assessment(LCA)/Resource recovery分类
资源环境引用本文复制引用
李丹妮,袁浩然,李承宇,王亚琢,陈虹媛,萧垚鑫,于振强,连希希,孔德新,单锐..基于人工智能的有机固废资源化技术研究进展[J].能源环境保护,2026,40(2):36-47,12.基金项目
国家重点研发计划资助项目(2023-67) （2023-67）
