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  • 1.
    Ashraf, Waqar Muhammad
    et al.
    Energy Pvt Ltd Sahiwal Coal Power Complex, Huaneng Shandong Ruyi Pakistan, Sahiwal 57000, Punjab, Pakistan.
    Rafique, Yasir
    Univ Engn & Technol, Dept Mech Engn, Taxila 47080, Punjab, Pakistan.
    Uddin, Ghulam Moeen
    Univ Engn & Technol, Dept Mech Engn, Lahore 54890, Punjab, Pakistan.
    Riaz, Fahid
    Natl Univ Singapore, Dept Mech Engn, Singapore 117575, Singapore.
    Asim, Muhammad
    Univ Engn & Technol, Dept Mech Engn, Lahore 54890, Punjab, Pakistan.
    Farooq, Muhammad
    Univ Engn & Technol, Dept Mech Engn, Lahore 54890, Punjab, Pakistan.
    Hussain, Abid
    Univ Engn & Technol, Dept Mech Engn, Taxila 47080, Punjab, Pakistan.
    Salman, Chaudhary Awais
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing2022In: Alexandria Engineering Journal, ISSN 1110-0168, E-ISSN 2090-2670, Vol. 61, no 3, p. 1864-1880Article in journal (Refereed)
    Abstract [en]

    The vibrations of bearings holding the high-speed shaft of a steam turbine are critically controlled for the safe and reliable power generation at the power plants. In this paper, two artificial intelligence (AI) process models, i.e., artificial neural network (ANN) and support vector machine (SVM) based relative vibration modeling of a steam turbine shaft bearing of a 660 MW supercritical steam turbine system is presented. After extensive data processing and machine learning based visualization tests performed on the raw operational data, ANN and SVM models are trained, validated and compared by external validation tests. ANN has outperformed SVM in terms of better prediction capability and is, therefore, deployed for simulating the constructed operating scenarios. ANN process model is tested for the complete load range of power plant, i.e., from 353 MW to 662 MW and 4.07% reduction in the relative vibration of the bearing is predicted by the network. Further, various vibration reduction operating strategies are developed and tested on the validated and robust ANN process model. A selected operating strategy which has predicted a promising reduction in the relative vibration of bearing is selected. In order to confirm the effectiveness of the prediction of the ANN process model, the selected operating strategy is implemented on the actual operation of the power plant. The resulting reduction in the relative vibrations of the turbine's bearing, which is less than the alarm limit, are confirmed. This cements the role of ANN process model to be used as an operational excellence tool resulting in vibration reduction of high-speed rotating equipment. (c) 2021 THE AUTHORS. Production and hosting by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

  • 2.
    Liu, G.
    et al.
    Institute of the Building Environment & Sustainability Technology, School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
    Xiao, T.
    State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace, Xi'an Jiaotong University, Xi'an, 710049, China.
    Wang, X.
    Institute of the Building Environment & Sustainability Technology, School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
    Yang, X.
    Institute of the Building Environment & Sustainability Technology, School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Numerical analysis of inner heating tube position for improving solid-phase transition in a shell-and-tube heat accumulator2023In: Alexandria Engineering Journal, ISSN 1110-0168, E-ISSN 2090-2670, Vol. 65, p. 771-784Article in journal (Refereed)
    Abstract [en]

    Latent heat thermal storage (LHTS) system is vital to reduce environment pollution. In the shell-and-tube heat accumulator, the position of the inner heating tube plays a vital role in the thermal storage. To analyze the effect of the inner tube position on the phase transition, a two-dimensional numerical model is developed. The structure has the minimum full melting time of 3480 s when the inner tube is 12 mm (L = 12 mm) from the center. Compared with L = 0 mm, the full melting time at L = 12 mm can be reduced by 13.4%. 

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