Intelligent Ironmaking Optimization Service on a Cloud Computing Platform by Digital Twin
作者机构：The State Key Laboratory of Industrial Control Technology&College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China
出 版 物：《Engineering》 (工程（英文）)
年 卷 期：2021年第7卷第9期
页 面：1274-1281页
核心收录：
学科分类：0710[理学-生物学] 12[管理学] 080602[工学-钢铁冶金] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0806[工学-冶金工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：This work was supported in part by the National Natural Science Foundation of China(61933015)
主 题：Cloud factory Blast furnace Multi-objective optimization Distributed computation
摘 要：The shortage of computation methods and storage devices has largely limited the development of multiobjective optimization in industrial *** improve the operational levels of the process industries,we propose a multi-objective optimization framework based on cloud services and a cloud distribution ***-time data from manufacturing procedures are first temporarily stored in a local database,and then transferred to the relational database in the ***,a distribution system with elastic compute power is set up for the optimization ***,a multi-objective optimization model based on deep learning and an evolutionary algorithm is proposed to optimize several conflicting goals of the blast furnace ironmaking *** the application of this optimization service in a cloud factory,iron production was found to increase by 83.91 t∙d^(-1),the coke ratio decreased 13.50 kg∙t^(-1),and the silicon content decreased by an average of 0.047%.
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