IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i13p3020-d1188874.html
   My bibliography  Save this article

Performance of an Adaptive Optimization Paradigm for Optimal Operation of a Mono-Switch Class E Induction Heating Application

Author

Listed:
  • Saddam Aziz

    (Centre for Advances in Reliability and Safety, New Territories, Hong Kong, China)

  • Cheung-Ming Lai

    (Centre for Advances in Reliability and Safety, New Territories, Hong Kong, China)

  • Ka Hong Loo

    (Centre for Advances in Reliability and Safety, New Territories, Hong Kong, China
    Department of Electronic and Information Engineering, The Hong Kong Polytechnic University (PolyU), Hong Kong, China)

Abstract

The progress of technology involves the continuous improvement of current machines to attain higher levels of energy efficiency, operational dependability, and effectiveness. Induction heating is a thermal process that involves the heating of materials that possess electrical conductivity, such as metals. This technique finds diverse applications, including induction welding and induction cooking pots. The optimization of the operating point of the inverter discussed in this study necessitated the resolution of a pair of non-convex mathematical models to enhance the energy efficiency of the inverters and mitigate switching losses. In order to determine the most advantageous operational location, a sophisticated surface optimization was conducted, requiring the implementation of a sophisticated optimization methodology, such as the adaptive black widow optimization algorithm. The methodology draws inspiration from the resourceful behavior of female black widow spiders in their quest for nourishment. Its straightforward control variable design and limited computational complexity make it a feasible option for addressing multi-dimensional engineering problems within confined constraints. The primary objective of utilizing the adaptive black widow optimization algorithm in the context of induction heating is to optimize the pertinent process parameters, including power level, frequency, coil design, and material properties, with the ultimate goal of efficiently achieving the desired heating outcomes. The utilization of the adaptive black widow optimization algorithm presents a versatile and robust methodology for addressing optimization problems in the field of induction heating. This is due to its capacity to effectively manage intricate, non-linear, and multi-faceted optimization predicaments. The adaptive black widow optimization algorithm has been modified in order to enhance the optimization process and guarantee the identification of the global optimum. The empirical findings derived from an authentic inverter setup were compared with the hypothetical results.

Suggested Citation

  • Saddam Aziz & Cheung-Ming Lai & Ka Hong Loo, 2023. "Performance of an Adaptive Optimization Paradigm for Optimal Operation of a Mono-Switch Class E Induction Heating Application," Mathematics, MDPI, vol. 11(13), pages 1-18, July.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:3020-:d:1188874
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/13/3020/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/13/3020/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mustafa, Faizan E & Ahmed, Ijaz & Basit, Abdul & Alvi, Um-E-Habiba & Malik, Saddam Hussain & Mahmood, Atif & Ali, Paghunda Roheela, 2023. "A review on effective alarm management systems for industrial process control: Barriers and opportunities," International Journal of Critical Infrastructure Protection, Elsevier, vol. 41(C).
    2. Mei Li & Gai-Ge Wang & Helong Yu, 2021. "Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem," Mathematics, MDPI, vol. 9(18), pages 1-30, September.
    3. Muhammad Ahsan Ayub & Saddam Aziz & Yitao Liu & Jianchun Peng & Jian Yin, 2023. "Design and Control of Novel Single-Phase Multilevel Voltage Inverter Using MPC Controller," Sustainability, MDPI, vol. 15(1), pages 1-17, January.
    4. Ghazanfar Ali Anwar & Mudasir Hussain & Muhammad Zeshan Akber & Mustesin Ali Khan & Aatif Ali Khan, 2023. "Sustainability-Oriented Optimization and Decision Making of Community Buildings under Seismic Hazard," Sustainability, MDPI, vol. 15(5), pages 1-21, March.
    5. Yong Wang & Kuichao Li & Gai-Ge Wang, 2022. "Combining Key-Points-Based Transfer Learning and Hybrid Prediction Strategies for Dynamic Multi-Objective Optimization," Mathematics, MDPI, vol. 10(12), pages 1-34, June.
    6. Rongquan Zhang & Saddam Aziz & Muhammad Umar Farooq & Kazi Nazmul Hasan & Nabil Mohammed & Sadiq Ahmad & Nisrine Ibadah, 2021. "A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor," Energies, MDPI, vol. 14(11), pages 1-22, May.
    Full references (including those not matched with items on IDEAS)

    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. Ádám Sleisz & Dániel Divényi & Beáta Polgári & Péter Sőrés & Dávid Raisz, 2022. "A Novel Cost Allocation Mechanism for Local Flexibility in the Power System with Partial Disintermediation," Energies, MDPI, vol. 15(22), pages 1-18, November.
    2. Anwar, Ghazanfar Ali & Zhang, Xiaoge, 2024. "Deep reinforcement learning for intelligent risk optimization of buildings under hazard," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    3. Lei Zhang & Xin Huang & Hui Sun, 2024. "Study on Multi-Index Evaluation Technology of Seismic Performance of Green Ecological Building Structure," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 18(1), pages 1-12, January.
    4. Yatindra Gopal & Yarrem Narasimhulu Vijaya Kumar & Akanksha Kumari & Om Prakash & Subrata Chowdhury & Abdullah A. Almehizia, 2023. "Reduced Device Count for Self Balancing Switched-Capacitor Multilevel Inverter Integration with Renewable Energy Source," Sustainability, MDPI, vol. 15(10), pages 1-22, May.
    5. Lining Xing & Rui Wu & Jiaxing Chen & Jun Li, 2022. "Handling Irregular Many-Objective Optimization Problems via Performing Local Searches on External Archives," Mathematics, MDPI, vol. 11(1), pages 1-19, December.
    6. Liu, Chunming & Wang, Chunling & Yin, Yujun & Yang, Peihong & Jiang, Hui, 2022. "Bi-level dispatch and control strategy based on model predictive control for community integrated energy system considering dynamic response performance," Applied Energy, Elsevier, vol. 310(C).
    7. Yao, Leyi & Liu, Zeyuan & Chang, Weiguang & Yang, Qiang, 2023. "Multi-level model predictive control based multi-objective optimal energy management of integrated energy systems considering uncertainty," Renewable Energy, Elsevier, vol. 212(C), pages 523-537.
    8. Barone, G. & Buonomano, A. & Forzano, C. & Palombo, A. & Russo, G., 2023. "The role of energy communities in electricity grid balancing: A flexible tool for smart grid power distribution optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    9. Saddam Aziz & Muhammad Talib Faiz & Adegoke Muideen Adeniyi & Ka-Hong Loo & Kazi Nazmul Hasan & Linli Xu & Muhammad Irshad, 2022. "Anomaly Detection in the Internet of Vehicular Networks Using Explainable Neural Networks (xNN)," Mathematics, MDPI, vol. 10(8), pages 1-23, April.
    10. Guo, Zhilong & Xu, Wei & Yan, Yue & Sun, Mei, 2023. "How to realize the power demand side actively matching the supply side? ——A virtual real-time electricity prices optimization model based on credit mechanism," Applied Energy, Elsevier, vol. 343(C).
    11. Lingling Li & Jiarui Pei & Qiang Shen, 2023. "A Review of Research on Dynamic and Static Economic Dispatching of Hybrid Wind–Thermal Power Microgrids," Energies, MDPI, vol. 16(10), pages 1-23, May.
    12. Nurbanu Catalbas & Ahmet Gungor Pakfiliz & Gokhan Soysal, 2024. "Multilevel Aircraft-Inverter Design Based on Wavelet PWM for More Electric Aircraft," Energies, MDPI, vol. 17(9), pages 1-23, April.
    13. Abedrabboh, Khaled & Al-Fagih, Luluwah, 2023. "Applications of mechanism design in market-based demand-side management: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    14. Wisam Abed Kattea Al-Maliki & Hayder Q. A. Khafaji & Hasanain A. Abdul Wahhab & Hussein M. H. Al-Khafaji & Falah Alobaid & Bernd Epple, 2022. "Advances in Process Modelling and Simulation of Parabolic Trough Power Plants: A Review," Energies, MDPI, vol. 15(15), pages 1-15, July.
    15. Di Liang & Jieyi Wang & Ran Bhamra & Liezhao Lu & Yuting Li, 2022. "A Multi-Service Composition Model for Tasks in Cloud Manufacturing Based on VS–ABC Algorithm," Mathematics, MDPI, vol. 10(21), pages 1-24, October.

    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:jmathe:v:11:y:2023:i:13:p:3020-:d:1188874. 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.