摘要
热处理工艺数据蕴含企业长期运行的经验,可揭示工艺条件、参数与碳排放量的关联关系,但实际生产中常常被割裂放置,为此,提出一种基于历史工艺数据集的BRBP-MOSOA融合数据驱动方法实现热处理工艺参数优化以降低碳排放量。构建热处理工艺历史数据库;根据热处理工艺的碳排放运行特点识别碳排放源,构建碳排放模型;用历史数据集训练BRBP神经网络建立热处理工艺参数与硬度、碳排放之间的关系模型,实现对特定工艺条件下碳排放和硬度的预测;利用MOSOA算法建立工艺过程低碳优化模型,通过碳效率评估,输出满足低碳排放量的最优热处理工艺参数。案例研究表明所提方法可提升热处理综合碳效率6.57%,为热处理工艺的低碳运行提供了一种使能工具。
Heat treatment process data contains long-term operation experience of enterprises,which can reveal the relationship between process conditions,parameters and carbon emissions.However,the process data are often fragmented and stored separately during the production process.Therefore,a BRBP-MOSOA hybrid data-driven method based on historical process data sets is proposed to optimize the parameters of heat treatment process in order to reduce carbon emissions.Firstly,the heat treatment process history database is constructed;according to the carbon emission operation characteristics of heat treatment process,the carbon emission sources are identified and the emission model is built.Then,the historical process data set are used in training BRBP network,which is used to reveal the relationship between heat treatment process parameters,hardness and carbon emissions,as well as predicted the carbon emissions and hardness under specific process conditions.The process parameters optimization model is established by using MOSOA optimization algorithm,and the optimal heat treatment process parameters satisfying low carbon emissions are output through carbon efficiency evaluation.The case study shows that the comprehensive carbon efficiency optimization of heat treatment reaches 6.57%,which can guarantee the product performance and reduce the carbon emission of heat treatment.An enabling tool for low carbon operation of heat treatment process is provided.
作者
易茜
刘益君
卓俊康
李聪波
易树平
YI Qian;LIU Yijun;ZHUO Junkang;LI Congbo;YI Shuping(State Key Laboratory of Mechanical transmission,Chongqing University,Chongqing 400044;College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2022年第16期370-383,共14页
Journal of Mechanical Engineering
基金
国家自然科学基金(52005062)
国家重点研发计划(2018YFB1701205)资助项目
关键词
数据驱动
热处理
低碳
优化
data-driven
heat treatment
low carbon
optimization
