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Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

Author

Listed:
  • Waqar Muhammad Ashraf

    (Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal, Punjab 57000, Pakistan
    Department of Mechanical Engineering, University of Engineering & Technology, Lahore, Punjab 54890, Pakistan)

  • Ghulam Moeen Uddin

    (Department of Mechanical Engineering, University of Engineering & Technology, Lahore, Punjab 54890, Pakistan)

  • Syed Muhammad Arafat

    (Department of Mechanical Engineering, University of Engineering & Technology, Lahore, Punjab 54890, Pakistan
    Department of Mechanical Engineering, Faculty of Engineering & Technology, The University of Lahore, Lahore 54000, Pakistan)

  • Sher Afghan

    (Software and Tools for Computational Engineering, RWTH Aachen University, 52074 Aachen, Germany
    Department of Computer Science, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Punjab 64200, Pakistan)

  • Ahmad Hassan Kamal

    (Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal, Punjab 57000, Pakistan)

  • Muhammad Asim

    (Department of Mechanical Engineering, University of Engineering & Technology, Lahore, Punjab 54890, Pakistan)

  • Muhammad Haider Khan

    (Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal, Punjab 57000, Pakistan
    Institute of Energy & Environment Engineering, University of the Punjab, Lahore, Punjab 54000, Pakistan)

  • Muhammad Waqas Rafique

    (Department of Mechanical Engineering, University of Engineering & Technology, Lahore, Punjab 54890, Pakistan)

  • Uwe Naumann

    (Software and Tools for Computational Engineering, RWTH Aachen University, 52074 Aachen, Germany)

  • Sajawal Gul Niazi

    (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
    Center for System Reliability and Safety, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Hanan Jamil

    (Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal, Punjab 57000, Pakistan
    Department of Mechanical Engineering, University of Engineering & Technology, Lahore, Punjab 54890, Pakistan)

  • Ahsaan Jamil

    (Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal, Punjab 57000, Pakistan)

  • Nasir Hayat

    (Department of Mechanical Engineering, University of Engineering & Technology, Lahore, Punjab 54890, Pakistan)

  • Ashfaq Ahmad

    (Department of Mechanical Engineering, University of Engineering & Technology, Lahore, Punjab 54890, Pakistan)

  • Shao Changkai

    (Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal, Punjab 57000, Pakistan)

  • Liu Bin Xiang

    (Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal, Punjab 57000, Pakistan)

  • Ijaz Ahmad Chaudhary

    (Department of Industrial Engineering, University of Management and Technology, Lahore, Punjab 54770, Pakistan)

  • Jaroslaw Krzywanski

    (Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland)

Abstract

This paper presents a comprehensive step-wise methodology for implementing industry 4.0 in a functional coal power plant. The overall efficiency of a 660 MW e supercritical coal-fired plant using real operational data is considered in the study. Conventional and advanced AI-based techniques are used to present comprehensive data visualization. Monte-Carlo experimentation on artificial neural network (ANN) and least square support vector machine (LSSVM) process models and interval adjoint significance analysis (IASA) are performed to eliminate insignificant control variables. Effective and validated ANN and LSSVM process models are developed and comprehensively compared. The ANN process model proved to be significantly more effective; especially, in terms of the capacity to be deployed as a robust and reliable AI model for industrial data analysis and decision making. A detailed investigation of efficient power generation is presented under 50%, 75%, and 100% power plant unit load. Up to 7.20%, 6.85%, and 8.60% savings in heat input values are identified at 50%, 75%, and 100% unit load, respectively, without compromising the power plant’s overall thermal efficiency.

Suggested Citation

  • Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5592-:d:434978
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