A new hybrid approach based on probability distribution and an improved machine learning for multivariate risk assessment

• Autorzy: Azzedine Abdelhakim , Nouri Fatma Zohra , Bouhouche Salah

Identyfikatory

DOI

10.29354/diag/183666

• Języki publikacji: EN

Abstrakty

EN

A highly complex dynamic non-linear reactor is the blast furnace iron manufacturing system. It has possible dangers, including carbon monoxide, wide variety of chemical reactions, fire, high pressure and explosion, noise, split and fall, hot metal sparks, hit etc. To ensure a secure working, organizations must take the required measures to manage the risks and their effects. The approach for risk assessment discussed in this research attempts to increase blast furnace safety performance and reduce workers injuries. This approach uses probability distribution and an improved machine learning techniques such as radial basis function artificial neural networks (RBANN). The novelty here is to calculate a multivariate risk using a proposed method, namely exponential smoothing combined with radial basis function artificial neural networks (ES-RBANN). To identify their limits, the results of a research comparing conventional and novel techniques are confirmed using real data collected from the steel production operations ArcelorMittal-Annaba, Algeria.

Słowa kluczowe

EN

blast furnace; exponential smoothing; risk; safety; data driven techniques; probability distribution

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2024
• Tom: Vol. 25, No. 1
• Strony: art. no. 2024113
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Azzedine Abdelhakim

• Mathematical Modeling and Numerical Simulation Research Laboratory, Faculty of Sciences, Badji Mokhtar University, BP12, Annaba, Algeria

• https://orcid.org/0000-0001-5825-3121

autor

Nouri Fatma Zohra

• Mathematical Modeling and Numerical Simulation Research Laboratory, Faculty of Sciences, Badji Mokhtar University, BP12, Annaba, Algeria

• https://orcid.org/0000-0002-0828-5464

autor

Bouhouche Salah

• Research development, innovation and Technological Support Bureau - BRD InovScience. Adresse: UV12, Bloc09 Bureau 17BIS Industrial Group SIDER, S/Amar Annaba Algeria

• https://orcid.org/0000-0002-5368-5870

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).