IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i21p5619-d435476.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/21/5619/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/21/5619/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dmitrii Glushkov & Geniy Kuznetsov & Kristina Paushkina, 2020. "Switching Coal-Fired Thermal Power Plant to Composite Fuel for Recovering Industrial and Municipal Waste: Combustion Characteristics, Emissions, and Economic Effect," Energies, MDPI, vol. 13(1), pages 1-17, January.
    2. Mohammad K. Najjar & Eduardo Linhares Qualharini & Ahmed W. A. Hammad & Dieter Boer & Assed Haddad, 2019. "Framework for a Systematic Parametric Analysis to Maximize Energy Output of PV Modules Using an Experimental Design," Sustainability, MDPI, vol. 11(10), pages 1-24, May.
    3. Sajjakaj Jomnonkwao & Savalee Uttra & Vatanavongs Ratanavaraha, 2020. "Forecasting Road Traffic Deaths in Thailand: Applications of Time-Series, Curve Estimation, Multiple Linear Regression, and Path Analysis Models," Sustainability, MDPI, vol. 12(1), pages 1-17, January.
    4. De, S. & Kaiadi, M. & Fast, M. & Assadi, M., 2007. "Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden," Energy, Elsevier, vol. 32(11), pages 2099-2109.
    5. Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 2020. "A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis," Applied Energy, Elsevier, vol. 267(C).
    6. Jaroslaw Krzywanski, 2019. "A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods," Energies, MDPI, vol. 12(23), pages 1-32, November.
    7. Kaytez, Fazil, 2020. "A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption," Energy, Elsevier, vol. 197(C).
    8. Lv, You & Lv, Xuguang & Fang, Fang & Yang, Tingting & Romero, Carlos E., 2020. "Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants," Energy, Elsevier, vol. 192(C).
    9. Manobel, Bartolomé & Sehnke, Frank & Lazzús, Juan A. & Salfate, Ignacio & Felder, Martin & Montecinos, Sonia, 2018. "Wind turbine power curve modeling based on Gaussian Processes and Artificial Neural Networks," Renewable Energy, Elsevier, vol. 125(C), pages 1015-1020.
    10. Smrekar, J. & Assadi, M. & Fast, M. & Kuštrin, I. & De, S., 2009. "Development of artificial neural network model for a coal-fired boiler using real plant data," Energy, Elsevier, vol. 34(2), pages 144-152.
    11. Lembke B., 1918. "√ a. p," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 111(1), pages 709-712, February.
    12. Zhao, Zhigang & Su, Sheng & Si, Ningning & Hu, Song & Wang, Yi & Xu, Jun & Jiang, Long & Chen, Gang & Xiang, Jun, 2017. "Exergy analysis of the turbine system in a 1000 MW double reheat ultra-supercritical power plant," Energy, Elsevier, vol. 119(C), pages 540-548.
    13. Luqman Razzaq & Muhammad Farooq & M. A. Mujtaba & Farooq Sher & Muhammad Farhan & Muhammad Tahir Hassan & Manzoore Elahi M. Soudagar & A. E. Atabani & M. A. Kalam & Muhammad Imran, 2020. "Modeling Viscosity and Density of Ethanol-Diesel-Biodiesel Ternary Blends for Sustainable Environment," Sustainability, MDPI, vol. 12(12), pages 1-20, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Muhammad Farooq & Fahid Riaz & Hassan Afroze Ahmad & Ahmad Hassan Kamal & Saqib Anwar & Ahmed M. El-Sherbeeny & Muhammad Haider Khan & Noman Hafeez & Arman, 2021. "Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics," Energies, MDPI, vol. 14(5), pages 1-18, February.
    2. Fahid Riaz & Fu Zhi Yam & Muhammad Abdul Qyyum & Muhammad Wakil Shahzad & Muhammad Farooq & Poh Seng Lee & Moonyong Lee, 2021. "Direct Analytical Modeling for Optimal, On-Design Performance of Ejector for Simulating Heat-Driven Systems," Energies, MDPI, vol. 14(10), pages 1-21, May.
    3. Wang, Lijin & Fan, Weipeng & Jiang, Guoqian & Xie, Ping, 2023. "An efficient federated transfer learning framework for collaborative monitoring of wind turbines in IoE-enabled wind farms," Energy, Elsevier, vol. 284(C).
    4. Zhang, Baoxin & Deng, Ze & Fu, Xuehai & Yu, Kun & Zeng, Fanhua (Bill), 2023. "An experimental study on the effects of acidization on coal permeability: Implications for the enhancement of coalbed methane production," Energy, Elsevier, vol. 280(C).
    5. Dorian Skrobek & Jaroslaw Krzywanski & Marcin Sosnowski & Anna Kulakowska & Anna Zylka & Karolina Grabowska & Katarzyna Ciesielska & Wojciech Nowak, 2020. "Prediction of Sorption Processes Using the Deep Learning Methods (Long Short-Term Memory)," Energies, MDPI, vol. 13(24), pages 1-16, December.
    6. Eslick, John C. & Zamarripa, Miguel A. & Ma, Jinliang & Wang, Maojian & Bhattacharya, Indrajit & Rychener, Brian & Pinkston, Philip & Bhattacharyya, Debangsu & Zitney, Stephen E. & Burgard, Anthony P., 2022. "Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application," Applied Energy, Elsevier, vol. 319(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Nikpey, H. & Assadi, M. & Breuhaus, P., 2013. "Development of an optimized artificial neural network model for combined heat and power micro gas turbines," Applied Energy, Elsevier, vol. 108(C), pages 137-148.
    3. Yu, Jianxi & Petersen, Nils & Liu, Pei & Li, Zheng & Wirsum, Manfred, 2022. "Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development," Energy, Elsevier, vol. 260(C).
    4. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Muhammad Farooq & Fahid Riaz & Hassan Afroze Ahmad & Ahmad Hassan Kamal & Saqib Anwar & Ahmed M. El-Sherbeeny & Muhammad Haider Khan & Noman Hafeez & Arman, 2021. "Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics," Energies, MDPI, vol. 14(5), pages 1-18, February.
    5. Nikpey, H. & Assadi, M. & Breuhaus, P. & Mørkved, P.T., 2014. "Experimental evaluation and ANN modeling of a recuperative micro gas turbine burning mixtures of natural gas and biogas," Applied Energy, Elsevier, vol. 117(C), pages 30-41.
    6. Bahadori, Alireza & Vuthaluru, Hari B., 2010. "A method for estimation of recoverable heat from blowdown systems during steam generation," Energy, Elsevier, vol. 35(8), pages 3501-3507.
    7. Bujak, Janusz, 2009. "Optimal control of energy losses in multi-boiler steam systems," Energy, Elsevier, vol. 34(9), pages 1260-1270.
    8. Sergei Rogosin & Maryna Dubatovskaya, 2017. "Letnikov vs. Marchaud: A Survey on Two Prominent Constructions of Fractional Derivatives," Mathematics, MDPI, vol. 6(1), pages 1-15, December.
    9. , Aisdl, 2019. "What Citizenship for What Transition?: Contradictions, Ambivalence, and Promises in Post-Socialist Citizenship Education in Vietnam," OSF Preprints jyqp5, Center for Open Science.
    10. Valerio Antonelli & Raffaele D'Alessio & Roberto Rossi, 2014. "Budgetary practices in the Ministry of War and the Ministry of Munitions in Italy, 1915-1918," Accounting History Review, Taylor & Francis Journals, vol. 24(2-3), pages 139-160, November.
    11. Karlsson, Martin & Nilsson, Therese & Pichler, Stefan, 2012. "What Doesn't Kill You Makes You Stronger? The Impact of the 1918 Spanish Flu Epidemic on Economic Performance in Sweden," Working Paper Series 911, Research Institute of Industrial Economics.
    12. Roger R. Betancourt, 1969. "R. A. EASTERLIN. Population, Labor Force, and Long Swings in Economic Growth: The American Experience. Pp. xx, 298. New York: National Bureau of Economic Research (Distributed by Columbia University P," The ANNALS of the American Academy of Political and Social Science, , vol. 384(1), pages 183-192, July.
    13. Singh, Nirupama & Kumari, Babita & Sharma, Shailja & Chaudhary, Surbhi & Upadhyay, Sumant & Satsangi, Vibha R. & Dass, Sahab & Shrivastav, Rohit, 2014. "Electrodeposition and sol–gel derived nanocrystalline N–ZnO thin films for photoelectrochemical splitting of water: Exploring the role of microstructure," Renewable Energy, Elsevier, vol. 69(C), pages 242-252.
    14. Rathberger Andreas, 2014. "The “Piano Virtuosos” of International Politics: Informal Diplomacy in the late nineteenth and early twentieth Century Ottoman Empire," New Global Studies, De Gruyter, vol. 8(1), pages 1-21, March.
    15. Seán Kenny & Jason Lennard & Kevin Hjortshøj O’Rourke, 2020. "An annual index of Irish industrial production, 1800-1921," Oxford Economic and Social History Working Papers _185, University of Oxford, Department of Economics.
    16. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
    17. Karlsson, Martin & Nilsson, Therese & Pichler, Stefan, 2014. "The impact of the 1918 Spanish flu epidemic on economic performance in Sweden," Journal of Health Economics, Elsevier, vol. 36(C), pages 1-19.
    18. Victoria Y. Fan & Dean T. Jamison & Lawrence H. Summers, 2016. "The Inclusive Cost of Pandemic Influenza Risk," NBER Working Papers 22137, National Bureau of Economic Research, Inc.
    19. Peter Willmott, 1969. "Some Social Trends," Urban Studies, Urban Studies Journal Limited, vol. 6(3), pages 286-308, November.
    20. Feldkamp, Cora L., 1945. "Vanilla: Culture, Processing and Economics: A List of References," USDA Miscellaneous 319330, United States Department of Agriculture.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5619-:d:435476. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.