A new hybrid approach based on probability distribution and an improved machine learning for multivariate risk assessment
More details
Hide details
Mathematical Modeling and Numerical Simulation Research Laboratory, Faculty of Sciences, Badji Mokhtar University, BP12, Annaba, Algeria.
Research development, innovation and Technological Support Bureau - BRD InovScience. Adresse: UV12, Bloc09 Bureau 17BIS Industrial Group SIDER, S/AMAR ANNABA ALGERIA.
Submission date: 2023-03-06
Final revision date: 2023-11-11
Acceptance date: 2024-02-04
Online publication date: 2024-02-06
Publication date: 2024-02-06
Corresponding author
Abdelhakim Azzedine   

Mathematical Modeling and Numerical Simulation Research Laboratory, Faculty of Sciences, Badji Mokhtar University, BP12, Annaba, Algeria.
Diagnostyka 2024;25(1):2024213
A highly complex dynamic non-linear reactor is the blast furnace iron manufacturing system. It has potential hazards like carbon monoxide, wide variety of chemical reactions, fire, high pressure and explosion, noise, split and fall, hot metal sparks, emission of air contaminants like particulate matter, hit etc. To do work safely, organization must take the required measures to manage the risks and their effects. The approach for risk assessment discussed in this paper aims to increase blast furnace safety performance and prevent workers from accidents. 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.
The authors are extremely appreciative to ArcelorMittal Annaba Algeria for supplying the reliable data necessary to complete this modest work.
This research received no external funding.
Abdelaziz M, Ahmed A, Riad A, Abderrezak G, Djida AA. Forecasting daily confirmed COVID-19 cases in Algeria using ARIMA models. 2020; 2020.12.18. 20248340. https://doi.org/10.1101/2020.1....
Alam MA, Emura K, Farnham C, Yuan J. Best-Fit Probability Distributions and Return Periods for Maximum Monthly Rainfall in Bangladesh. Climate 2018; 6(1): 9. https://doi.org/10.3390/cli601....
Bttinger D, Fritschek H, Kronberger T, Mauhart J, Schaler M. Risk and Knowledge Management With Blast Furnace Process Optimization Systems. IRON & STEEL TECHNOLOGY 2018.
BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP and OIML, Joint Committee for Guides in Metrology - JCGM 106:2012, Evaluation of Measurement Data - The Role of Measurement Uncertainty in Conformity Assessment (2012).
Clemen RT, Winkler RL. Combining Probability Distributions From Experts in Risk Analysis. Risk Analysis 1999; 19(2): 187–203. https://doi.org/10.1111/j.1539....
Faris H, Aljarah I, Mirjalili S. Chapter 28 - Evolving Radial Basis Function Networks Using Moth–Flame Optimizer. Handbook of Neural Computation 2017 s. 537–50. https://doi.org/10.1016/B978-0....
Gao X, Zhou X, Zou H, Wang Q, Zhou Z, Chen R, i in. Exposure characterization and risk assessment of ultrafine particles from the blast furnace process in a steelmaking plant. Journal of Occupational Health 2021; 63(1): e12257. https://doi.org/10.1002/1348-9....
Ge Z, Song Z, Ding S, Huang B, Data mining and analytics in the process industry: The role of machine learning. IEEE Access 20590-20616 2017. https://doi.org/10.1109/ACCESS....
Kamo K, Hamamoto K, Narazaki H, Maeda T, Yakeya M, Tanaka Y. Method for predicting gas channeling in blast furnace. Kobelco Technology Review 2019; 37: 41-47.
Kara Y, Canal MR, Sefa İ, Boran FE. Selecting and Analyzing Appropriate Probability Distributions for Reliability of Electrical Transmission Lines. Gazi University Journal of Science Part C: Design and Technology 2021; 9(1): 108–21. https://doi.org/10.29109/gujsc....
Li, Junpeng and Hua, Changchun and Yang, Yana and Guan, Xinping. "Data-driven Bayesian-based takagi-sugeno fuzzy modeling for dynamic prediction of hot metal silicon content in blast furnace". IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020. https://doi.org/10.1109/TSMC.2....
Liu S, Sun W. Attention mechanism-aided data and knowledge-driven soft sensors for predicting blast furnace gas generation. Energy 2023; 262: 125498. https://doi.org/10.1016/j.ener....
Luo Z, Chen L, Zhang M, Liu L, Zhao J, Mu Y. Analysis of melting reconstruction treatment and cement solidification on ultra-risk municipal solid waste incinerator fly ash–blast furnace slag mixtures. Environmental Science and Pollution Research 2020; 27(25): 32139–51. https://doi.org/10.1007/s11356....
Modarres M, Kaminskiy MP, Krivtsov V. Reliability engineering and risk analysis: a practical guide. CRC press 2017. https://doi.org/10.1201/978131....
Richardson JW, Klose SL, Gray AW. An Applied Procedure for Estimating and Simulating Multivariate Empirical (MVE) Probability Distributions In Farm-Level Risk Assessment and Policy Analysis. Journal of Agricultural and Applied Economics 2000; 32(2): 299–315. https://doi.org/10.1017/S10740....
Sun W, Wang Z, Wang Q. Hybrid event, mechanism and data-driven prediction of blast furnace gas generation. Energy 2020; 199: 117497. https://doi.org/10.1016/j.ener....
Suresh, R and Sathyanathan, M and Visagavel, K and Rajesh Kumar, M, Risk assessment for blast furnace using FMEA. Int J Res Eng Technol 2014; 3.
Xie T, Yu H, Wilamowski B. Comparison between traditional neural networks and radial basis function networks. 2011 IEEE International Symposium on Industrial Electronics 2011; 1194–9. https://doi.org/10.1109/ISIE.2....
Zhang X, Kano M, Matsuzaki S. Ensemble pattern trees for predicting hot metal temperature in blast furnace. Computers & Chemical Engineering 2019; 121: 442–9. https://doi.org/10.1016/j.comp....
Tang X, Zhuang L, Jiang C. Prediction of silicon content in hot metal using support vector regression based on chaos particle swarm optimization. Expert Systems with Applications 2009; 36(9): 11853–7. https://doi.org/10.1016/j.eswa....
Journals System - logo
Scroll to top