Abstract
This study proposes a cloud-edge collaboration framework for temperature regulation in continuous annealing processes. A multiobjective optimization is formulated by ensuring the control accuracy of the temperature to reduce energy consumption and increase efficiency with cloud computing. Based on process analytics, a framework for clustering operating conditions with high real-time requirements is proposed. Further, a recommendation mechanism for furnace temperatures with low real-time requirements is developed in the cloud. Compared with traditional architectures, the cloud-edge collaboration approach improves energy savings and control stability, which demonstrates its effectiveness and practicality.
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