A Machine Learning Approach for Fire-Fighting Detection in the Power Industry

Firas Alnaimi, Ammar Al Bazi, Rami Hikmat Fouad Al-Hadeethi, Matthew Victor

    Research output: Contribution to journalArticlepeer-review

    5 Citations (Scopus)
    2 Downloads (Pure)

    Abstract

    Coal kept in the coal storage yard spontaneously catches on fire, which results in wastage and can even cause a massive fire to break out. This phenomenon is known as the spontaneous combustion of coal. It is a complex process that has non-linear relationships between its causing variables. Preventive measures to prevent the fire from spreading to other coal piles in the vicinity have already been implemented. However, the predictive aspect before the fire occurs is of great necessity for the
    power generation sector. This research investigates various prediction models for spontaneous coal combustion, explicitly selecting input and output parameters to identify a proper clinker formation prediction model. Feed-Forward Neural Network (FFNN) is proposed as a proper prediction model. Two Hidden Layers (2HL) network is found to be the best with 5 minutes prediction capability. A sensitivity analysis study is also conducted to determine the influence of random input variables on
    their respective response variables.
    Original languageEnglish
    Pages (from-to)475-482
    Number of pages8
    JournalJordan Journal of Mechanical and Industrial Engineering
    Volume15
    Issue number5
    Publication statusPublished - Nov 2021

    Funder

    This research was financially supported by Universiti Tenaga Nasional, Malaysia through BOLD refresh publication fund 2021(J510050002-BOLDRefresh2025-Centre of Excellence).

    Keywords

    • Spontaneous combustion of coal
    • Artificial Neural Network
    • Clinker Formation Prediction Models
    • Coal-fired power plant

    Fingerprint

    Dive into the research topics of 'A Machine Learning Approach for Fire-Fighting Detection in the Power Industry'. Together they form a unique fingerprint.

    Cite this