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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 2. Power Generation

Author

Listed:
  • Waqar Muhammad Ashraf

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

  • Ghulam Moeen Uddin

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

  • Ahmad Hassan Kamal

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

  • Muhammad Haider Khan

    (Institute of Energy & Environmental Engineering, University of the Punjab, Lahore 54000, Pakistan)

  • Awais Ahmad Khan

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

  • Hassan Afroze Ahmad

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

  • Fahad Ahmed

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

  • Noman Hafeez

    (Department of Computer Science, Government College University, Lahore 54000, Pakistan)

  • Rana Muhammad Zawar Sami

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

  • Syed Muhammad Arafat

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

  • 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)

  • Muhammad Waqas Rafique

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

  • Ahsan Amjad

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

  • Jawad Hussain

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

  • Hanan Jamil

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

  • Muhammad Shahbaz Kathia

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

  • Jaroslaw Krzywanski

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

Abstract

Modern data analytics techniques and computationally inexpensive software tools are fueling the commercial applications of data-driven decision making and process optimization strategies for complex industrial operations. In this paper, modern and reliable process modeling techniques, i.e., multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LSSVM), are employed and comprehensively compared as reliable and robust process models for the generator power of a 660 MW e supercritical coal combustion power plant. Based on the external validation test conducted by the unseen operation data, LSSVM has outperformed the MLR and ANN models to predict the power plant’s generator power. Later, the LSSVM model is used for the failure mode recovery and a very successful operation control excellence tool. Moreover, by adjusting the thermo-electric operating parameters, the generator power on an average is increased by 1.74%, 1.80%, and 1.0 at 50% generation capacity, 75% generation capacity, and 100% generation capacity of the power plant, respectively. The process modeling based on process data and data-driven process optimization strategy building for improved process control is an actual realization of industry 4.0 in the industrial applications.

Suggested Citation

  • Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Ahmad Hassan Kamal & Muhammad Haider Khan & Awais Ahmad Khan & Hassan Afroze Ahmad & Fahad Ahmed & Noman Hafeez & Rana Muhammad Zawar Sami & Syed Muhammad , 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation," Energies, MDPI, vol. 13(21), pages 1-22, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5619-:d:435476
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