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Data-Based Prediction for Energy Scheduling of Steel Industry

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Data-Driven Prediction for Industrial Processes and Their Applications

Part of the book series: Information Fusion and Data Science ((IFDS))

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Abstract

Based on the results of a number of different forecasting modes introduced in the previous chapters, this chapter provides a practical case study related to the optimal scheduling for energy system in steel industry based on the prediction outcomes. As for the by-product gas scheduling problem, a two-stage scheduling method is introduced here. On the prediction stage, the states of the optimized objectives, the consumption of the outsourcing natural gas and oil, the power generation, and the gas holder levels are forecasted by using the previous data-driven learning methods. On the optimal scheduling stage, a rolling optimization procedure is performed by employing the predicted results. More typically, with respect to the scheduling task for the oxygen/nitrogen system in steel industry, a similar two-stage method is also developed, in which a granular-computing (GrC)-based prediction model is firstly established on the stage of a long-term prediction, and the scheduling solution is also optimized later. Furthermore, the results of the scheduling system applications also indicate the effectiveness of the real-time prediction and scheduling optimization.

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Author information

Authors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Jun Zhao & Wei Wang

  2. Shandong University of Science and Technology, Qingdao, China

    Chunyang Sheng

Authors
  1. Jun Zhao
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  2. Wei Wang
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  3. Chunyang Sheng
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Zhao, J., Wang, W., Sheng, C. (2018). Data-Based Prediction for Energy Scheduling of Steel Industry. In: Data-Driven Prediction for Industrial Processes and Their Applications. Information Fusion and Data Science. Springer, Cham. https://doi.org/10.1007/978-3-319-94051-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-94051-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94050-2

  • Online ISBN: 978-3-319-94051-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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