An Intelligent Control Method for the Low-Carbon Operation of Energy-Intensive Equipment
Tianyou Chai,
Mingyu Li,
Zheng Zhou,
Siyu Cheng,
Yao Jia,
Zhiwei Wu
Affiliations
Tianyou Chai
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China; National Engineering Technology Research Center for Metallurgical Industry Automation (Shenyang), Northeastern University, Shenyang 110819, China; Corresponding author.
Mingyu Li
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China; National Engineering Technology Research Center for Metallurgical Industry Automation (Shenyang), Northeastern University, Shenyang 110819, China
Zheng Zhou
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China; National Engineering Technology Research Center for Metallurgical Industry Automation (Shenyang), Northeastern University, Shenyang 110819, China
Siyu Cheng
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China; National Engineering Technology Research Center for Metallurgical Industry Automation (Shenyang), Northeastern University, Shenyang 110819, China
Yao Jia
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China; National Engineering Technology Research Center for Metallurgical Industry Automation (Shenyang), Northeastern University, Shenyang 110819, China
Zhiwei Wu
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China; National Engineering Technology Research Center for Metallurgical Industry Automation (Shenyang), Northeastern University, Shenyang 110819, China
Based on an analysis of the operational control behavior of operation experts on energy-intensive equipment, this paper proposes an intelligent control method for low-carbon operation by combining mechanism analysis with deep learning, linking control and optimization with prediction, and integrating decision-making with control. This method, which consists of setpoint control, self-optimized tuning, and tracking control, ensures that the energy consumption per tonne is as low as possible, while remaining within the target range. An intelligent control system for low-carbon operation is developed by adopting the end–edge–cloud collaboration technology of the Industrial Internet. The system is successfully applied to a fused magnesium furnace and achieves remarkable results in reducing carbon emissions.
