机器学习在有机固体废物资源化的应用进展  

Advances in machine learning applications to resource technology for organic solid waste

作　　者：马鸿志 刘忆婵 赵继华 费凡 高明 汪群慧 MA Hongzhi;LIU Yichan;ZHAO Jihua;FEI Fan;GAO Ming;WANG Qunhui(School of Energy and Environmental Engineering,University of Science and Technology Beijing,Beijing 100083,China;Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants,Beijing 100083,China;School of Resources and Environment,Yili Normal University,Yining 835000,China)

机构地区：[1]北京科技大学能源与环境工程学院,北京100083 [2]工业典型污染物资源化处理北京市重点实验室,北京100083 [3]伊犁师范大学资源与环境学院,伊宁835000

出　　处：《工程科学学报》2025年第3期550-561,共12页Chinese Journal of Engineering

基　　金：国家重点研发计划资助项目(2022YFE0105700);鄂尔多斯市科技计划项目(ZD20232319);国家自然科学基金青年科学基金资助项目(72004067);国家重点实验室专项基金课题(22K02ESPCT);新疆维吾尔自治区重大科研专项(2022B02021)。

摘　　要：机器学习(ML)方法,以其卓越的数据解析和模式识别能力,已在有机固体废物(OSW)处理领域展现出显著的应用潜力.随着对OSW处理需求的日益增长及技术革新的推进,ML在该领域的应用正迅速普及.聚焦ML技术在OSW资源化处理中的应用,首先界定了OSW的范畴,针对OSW处理中存在的异质性和复杂性问题,指出了传统处理技术在进行OSW产量预测和条件优化时的局限性.通过对2018—2023年相关学术成果进行系统梳理和分析,揭示了ML在OSW处理中的研究趋势和潜力.特别是发现以人工神经网络(ANN)、支持向量机(SVM)、决策树(DT)、随机森林(RF)和极端梯度提升(XGBoost)为代表的常用模型结合遗传(GA)优化算法,成为提高OSW处理效率和资源回收率的研究热点.分析了这些模型在源头产生与分类、热化学转化处理、厌氧生物处理和好氧堆肥等具体应用中的现状及应用频率,同时评估了它们的优缺点及适用性.研究发现,ML技术能够有效提高OSW处理的预测精度和工艺优化能力,尤其是在废物特性预测和生物处理过程模拟方面展现出显著优势.然而,数据质量、模型的泛化能力以及算法选择仍然是ML技术应用中的关键挑战.为此,提出开发综合模型、加强跨学科技术融合等一系列解决策略,以期为OSW资源化提供科学指导和技术支持.Machine learning(ML)techniques,with their advanced data analysis and pattern recognition capabilities,are highly effective for addressing the complexities of organic solid waste(OSW)treatment and resource recovery.As global waste generation continues to increase,the need for efficient and sustainable OSW management solutions is growing.Traditional waste treatment technologies often face challenges in managing the heterogeneous and complex nature of OSW,which varies widely in composition.In contrast,ML can optimize treatment processes,improve resource recovery rates,and enhance decision-making.This study explores a range of commonly used ML models,including artificial neural network(ANN),support vector machine(SVM),decision tree,random forest,and extreme gradient boosting(XGBoost).These models have been used to predict waste characteristics,classify diverse types of OSW,and optimize treatment parameters across various processes,such as thermochemical conversion,anaerobic digestion,and aerobic composting.A key focus of this work is the combination of ML models with optimization algorithms like Genetic Algorithm,which improves the performance of ML models by optimizing hyperparameters and enhancing prediction accuracy.This approach is particularly useful in complex processes such as biological treatment and resource recovery,where ML models can predict waste characteristics and optimize treatment conditions.This work also presents a comprehensive analysis of the application frequency of these ML models in various stages of OSW treatment,including source generation,classification,and treatment processes like pyrolysis,gasification,and composting.This analysis identifies the strengths and weaknesses of each model,highlighting the importance of selecting the most appropriate ML approach based on the specific characteristics of the OSW treatment task.ANN,for example,is particularly useful for complex,nonlinear relationships within biological treatment processes,while SVM is effective for small datasets and high-dimensio

关 键 词：有机固体废物 机器学习 资源化 高效处理 建模预测 过程优化 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] X705[自动化与计算机技术—控制科学与工程]