IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v284y2023ics0360544223026154.html
   My bibliography  Save this article

Bayesian optimization of multiscale kernel principal component analysis and its application to model Gas-to-liquid (GTL) process data

Author

Listed:
  • Fezai, Radhia
  • Malluhi, Byanne
  • Basha, Nour
  • Ibrahim, Gasim
  • Choudhury, Hanif A.
  • Challiwala, Mohamed S.
  • Nounou, Hazem
  • Elbashir, Nimir
  • Nounou, Mohamed

Abstract

Kernel methods map the data from original space into a higher-dimensional space in which linear methods are applied. In many applications, the inverse mapping is also important, and the pre-image of a feature vector must be found in the original space. Kernel principal component analysis (KPCA) based kernel density estimation (KDE) has been developed to solve this problem. However, the performance of the KPCA technique greatly depends on the choice of some parameters which can lead to poor modeling performance when these parameters are not well identified. Thus, fully Bayesian optimization KPCA (BOKPCA) is proposed to enhance the performance of the KPCA model. BOKPCA method aims to automatically select the best parameters of the KPCA model. Generally, kernel methods struggle to handle nonlinear data contaminated with high levels of noise. This is because the noise affects every principal component, making it challenging to mitigate its influence during the reconstruction step. Consequently, to further enhance the ability of KPCA and BOKPCA models, we propose to integrate multi-scale filtering with these two models. The efficiency of the proposed methods are evaluated using a simulated nonlinear process and real data generated from a bench-scale Fischer–Tropsch (FT) process.

Suggested Citation

  • Fezai, Radhia & Malluhi, Byanne & Basha, Nour & Ibrahim, Gasim & Choudhury, Hanif A. & Challiwala, Mohamed S. & Nounou, Hazem & Elbashir, Nimir & Nounou, Mohamed, 2023. "Bayesian optimization of multiscale kernel principal component analysis and its application to model Gas-to-liquid (GTL) process data," Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223026154
    DOI: 10.1016/j.energy.2023.129221
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223026154
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129221?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fezai, R. & Mansouri, M. & Trabelsi, M. & Hajji, M. & Nounou, H. & Nounou, M., 2019. "Online reduced kernel GLRT technique for improved fault detection in photovoltaic systems," Energy, Elsevier, vol. 179(C), pages 1133-1154.
    2. Hui Hwang Goh & Ling Liao & Dongdong Zhang & Wei Dai & Chee Shen Lim & Tonni Agustiono Kurniawan & Kai Chen Goh & Chin Leei Cham, 2022. "Denoising Transient Power Quality Disturbances Using an Improved Adaptive Wavelet Threshold Method Based on Energy Optimization," Energies, MDPI, vol. 15(9), pages 1-21, April.
    3. Kutateladze, Varlam, 2022. "The kernel trick for nonlinear factor modeling," International Journal of Forecasting, Elsevier, vol. 38(1), pages 165-177.
    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. Tonni Agustiono Kurniawan & Mohd Hafiz Dzarfan Othman & Xue Liang & Muhammad Ayub & Hui Hwang Goh & Tutuk Djoko Kusworo & Ayesha Mohyuddin & Kit Wayne Chew, 2022. "Microbial Fuel Cells (MFC): A Potential Game-Changer in Renewable Energy Development," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    2. Hong, Ying-Yi & Pula, Rolando A., 2022. "Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network," Energy, Elsevier, vol. 246(C).
    3. Bakdi, Azzeddine & Bounoua, Wahiba & Mekhilef, Saad & Halabi, Laith M., 2019. "Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV," Energy, Elsevier, vol. 189(C).
    4. Rajveer Jat & Daanish Padha, 2024. "Kernel Three Pass Regression Filter," Papers 2405.07292, arXiv.org, revised Jun 2024.

    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:eee:energy:v:284:y:2023:i:c:s0360544223026154. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.