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On-line Energy Allocation Based on Approximate Dynamic Programming for Iron and Steel Industry

机译：基于近似动态规划的钢铁行业在线能源分配

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摘要

Energy allocation in iron and steel industry is the assignment of available energy to various production users. With the increasing price of energy, a perfect allocation plan should ensure that nothing gets wasted and no shortage. This is challenging because the energy demand is dynamic due to the changes of orders, production environment, technological level, etc. This paper try to realize on-line energy resources allocation under the situation of dynamic production plan and environment based on typical energy consumption process of steel enterprises. Without definite analytical model, it is a tough task to make the energy allocation plan tracks the dynamic change of production environment in real time. This paper proposes to deal with dynamic energy allocation problem by interactive learning with time-varying environment using Approximate Dynamic Programming method. The problem is formulated as a dynamic model with variable right-hand items, which is an updated energy demand obtained by on-line learning. Reinforcement learning method is designed to learn the energy consumption principle from the historical data to predict energy consumption level corresponding to current production environment and the production plan in future horizon. Using the prediction results, on-line energy allocation plan is made and its performance is demonstrated by comparison with static allocation method.
机译：钢铁行业的能源分配是将可用能源分配给各种生产用户。随着能源价格的上涨，完善的分配计划应确保没有浪费，也没有短缺。这是有挑战性的，因为能源需求因订单，生产环境，技术水平等的变化而动态变化。本文试图根据典型的能耗过程，在动态生产计划和环境的情况下实现在线能源配置。钢铁企业。没有明确的分析模型，使能源分配计划实时跟踪生产环境的动态变化是一项艰巨的任务。本文提出了使用近似动态规划方法通过在时变环境下进行交互式学习来解决动态能量分配问题。该问题被公式化为带有可变右手项目的动态模型，该模型是通过在线学习获得的更新的能源需求。强化学习方法旨在从历史数据中学习能耗原理，以预测与当前生产环境和未来生产计划相对应的能耗水平。利用预测结果，制定了在线能源分配方案，并与静态分配方法进行比较，证明了其性能。

著录项

  • 来源
    《ISIJ international》 |2016年第12期|2214-2223|共10页
  • 作者

    Yanyan ZHANG; Qingxin GUO; Lixin TANG;

  • 作者单位

    State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819 People's Republic of China,Institute of Industrial Engineering & Logistics Optimization, Northeastern University, Shenyang, 110819 People's Republic of China;

    State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819 People's Republic of China,Institute of Industrial Engineering & Logistics Optimization, Northeastern University, Shenyang, 110819 People's Republic of China;

    Institute of Industrial Engineering & Logistics Optimization, Northeastern University, Shenyang, 110819 People's Republic of China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    approximate dynamic programming; energy prediction; on-line energy allocation;

    机译：近似动态规划;能量预测;在线能源分配;

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