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Indexed by:会议论文

Date of Publication:2017-05-28

Included Journals:EI、CPCI-S

Page Number:137-142

Key Words:steel industry; energy scheduling; adaptive dynamic programming; reinforcement learning; evolutionary computing

Abstract:It is significant to perform an effective scheduling of byproduct gas system in steel industry for reducing cost and protecting environment. The existing studies largely focused on extracting specific knowledge from human experience or directly optimizing the scheduling performance, which failed to provide a dynamic optimization process for making the scheduling scheme updated online. In this study, an action-dependent heuristic dynamic programming (ADHDP) framework is proposed for the Linz Donawitz converter gas (LDG) scheduling, in which the scheduling amount is calculated based on the gas system states by utilizing a Tagaki-Sugeno-Kang (TSK) fuzzy model, while a utility function is introduced in the critic network considering the time delay of the gas system to evaluate the scheduling performance over time. For achieving online learning process, the concept of a modified evolutionary algorithm is combined with the ADHDP to obtain the near-optimal scheduling policy at each time instance. To demonstrate the performance of the proposed method, the practical data coming from the energy center of a steel plant are employed. The results show that the proposed method can supply the human operators with effective solution for secure and economically justified optimization of the LDG system.