【摘 要】
：
The shortage of computation methods and storage devices has largely limited the development of multi-objective optimization in industrial processes.To improve the operational levels of the process industries,we propose a multi-objective optimization frame
【机 构】
：
The State Key Laboratory of Industrial Control Technology&College of Control Science and Engineering
论文部分内容阅读
The shortage of computation methods and storage devices has largely limited the development of multi-objective optimization in industrial processes.To improve the operational levels of the process industries,we propose a multi-objective optimization framework based on cloud services and a cloud distribution system.Real-time data from manufacturing procedures are first temporarily stored in a local database,and then transferred to the relational database in the cloud.Next,a distribution system with elastic com-pute power is set up for the optimization framework.Finally,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 process.With 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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