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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

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, Hanan Jamil, Ahsaan Jamil, Nasir Hayat, Ashfaq Ahmad, Shao Changkai, Liu Bin Xiang, Ijaz Ahmad Chaudhary and Jaroslaw Krzywanski
Additional contact information
Waqar Muhammad Ashraf: Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal, Punjab 57000, 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
Sher Afghan: Software and Tools for Computational Engineering, RWTH Aachen University, 52074 Aachen, Germany
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
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
Hanan Jamil: Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal, Punjab 57000, 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

Energies, 2020, vol. 13, issue 21, 1-33

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.

Keywords: combustion; supercritical power plant; industry 4.0 for the power sector; artificial intelligence; thermal efficiency (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: Add references at CitEc
Citations: View citations in EconPapers (4)

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