Multi-objective optimal scheduling of laminar cooling water supply system for hot rolling mills driven by digital twin for energy-saving

Abstract

As a result of the complex structure of laminar cooling water supply systems, both facets of strict process requirements and various intermittent running conditions could cause challenges of energy-saving and equipment upkeep. Given the energy wastage issue of the laminar cooling system of hot rolling, this paper develops an optimal scheduling system according to digital twin using Online Sequential Extreme Learning Machine (OS-ELM) and multi-objective evolutionary optimization using Improved Sparrow Search Algorithm (ISSA). The optimal scheduling system according to digital twins can accurately predict the water consumption trend of the water supply system and optimize the scheduling instructions and operation scheme through dynamic information interaction and mapping of process constraints, intermittent operating conditions, rolling rhythm, measured data, etc. between physical space and virtual space. Experimental results reveal that the proposed method can lessen power usage by 13.60% and water consumption by 10.54% regarding the premise of ensuring what is needed of cooling procedures. In addition, the water pump can maintain high effectiveness during operation to guarantee the security and stability of laminar cooling water supply systems.