机器学习在生物质烘焙研究中的现状及展望OA北大核心CSTPCD
The Current Status and Prospects of Machine Learning in Biomass Torrefaction Research
生物质资源丰富,对生物质进行烘焙预处理后,再利用机器学习(ML)对烘焙生物质进行燃料分级有利于实现生物质能源的规模化应用.本文介绍了机器学习算法在医疗诊断、图像识别、自然语言处理等领域的分类学方面的应用,重点综述了其对烘焙生物质的产物能量及固体产物产量等的预测效果.最后,根据机器学习分类和预测的相互转化,展望了未来将机器学习的分类技术用于烘焙生物质分类的前景.
Biomass resources were abundant.After torrefaction pretreatment,machine learning(ML)was used to classify torrefied biomass as fuel to realize the large-scale application of biomass energy.This paper introduced the application of machine learning algorithms in the fields of medical diagnosis,image recognition and natural language processing,etc.,and focused on the prediction of product energy and solid product yield of torrefied biomass.Finally,according to the mutual transformation of ML classification and prediction,the prospects of applying ML classification techniques to the rorrefied biomass classification in the future was proposed.
刘海云;孙云娟;徐卫;陈义峰;汪东;黄萍
中国林业科学研究院林产化学工业研究所||江苏省生物质能源与材料重点实验室||国家林业和草原局林产化学工程重点实验室||林木生物质低碳高效利用国家工程研究中心,江苏南京 210042||南京林业大学江苏省林业资源高效加工利用协同创新中心,江苏南京 210037中石化南京化工研究院有限公司,江苏南京 210048
化学工程
生物质烘焙机器学习原料分类
biomass torrefactionmachine learningraw material classification
《林产化学与工业》 2024 (006)
1-10 / 10
国家重点研发计划资助项目(2022YFB4202001);江苏省生物质能源与材料重点实验室基本科研业务费(JSBEM-S-202324)
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