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

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 Arafat, Sajawal Gul Niazi, Muhammad Waqas Rafique, Ahsan Amjad, Jawad Hussain, Hanan Jamil, Muhammad Shahbaz Kathia and Jaroslaw Krzywanski
Additional contact information
Waqar Muhammad Ashraf: Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal 57000, 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
Sajawal Gul Niazi: School of Mechanical and Electrical Engineering, 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
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

Energies, 2020, vol. 13, issue 21, 1-22

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.

Keywords: combustion; supercritical power plant; industry 4.0 for the power sector; generator power; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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