Digital Twin and Scheduling of Parallel Walking Beam Reheating Furnaces

Digital twin (DT) is a novel technology with a promising prospect in smart manufacturing. It can realize high-fidelity mapping of physical entities, data fusion of physical and virtual space, and real-time monitoring of physical entities. This makes it easy to make optimal decisions and precise control. The steel industry has long production processes, complex equipment operation mechanisms, and dynamic production environment. Due to the lack of digital representation of its production process, many optimization and control schemes obtained by existing methods have large deviations in their implementation processes. How to provide accurate digital representation and make optimal decisions based on it is a great challenge. The development of DT technology in industrial scenarios provides a solution to this issue. In the steel production process, reheating furnaces are highly energy-consuming facilities connecting the continuous casting and hot rolling processes. This work proposes a five-dimension DT faced to parallel walking beam reheating furnaces. In it, we construct a three-stage model for virtual entities. It can accurately obtain the charging/discharging times and real-time positions of slabs, along with simulating the slabs’ logistics processes. An adaptive large neighborhood tabu search algorithm based on DT, named as ALTS-DT, is further proposed for parallel reheating furnace scheduling (PRFS) problems. By conducting numerous computational experiments, we verify the following: first, the high accuracy of the proposed three-stage model of DT, with relative error under 0.98%; second, the vital role of DT in PRFS; third, the excellent performance of the proposed ALTS-DT in solving actual PRFS problems.