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<p>Oil refineries present a high risk of accidents due to the use of flammable substances, as well as the operation of equipment at elevated temperatures and pressures. To address this issue, this study aims to analyze data on various potential scenarios for accidents at an oil refinery and develop an artificial neural network (NENKAZ ANN) to predict potential risk areas using real-time manufacturing data. This research employed the following techniques and tools: HAZID (hazard identification) method, hazard modeling software (ALOHA), Matlab R2022b, and the Levenberg-Marquardt algorithm, as well as MSE and R2 metrics. The testing of the developed ANN showed that it achieves high accuracy in predicting the distance traveled by the chemical explosion at oil refineries. The results of the prediction demonstrate that the coefficients of determination during training, validation, and testing are all 0.99. The average absolute error values for calculations using NENKAZ ANN are 190.42, 294.31, and 688.97, respectively. The forecasts generated in the study determined safe air zone dimensions in the period of atmospheric pollution resulting from an oil refinery accident. The developed ANN will be utilized at oil refineries in Kazakhstan, with the potential for implementation at other refineries worldwide. In the field of refinery management, NENKAZ ANN provides a means to evaluate the safe distance from the site of an explosion of a hazardous chemical substance, considering preliminary weather forecasts received from the meteorological service.</p>

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Neural networks for predicting oil refinery accidents

  • 期刊论文

  • 发表日期：

    2025年7月31日

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  • Almas Uali,
  • Alexander Rukovich,
  • Aigul Naukenova

摘要

Oil refineries present a high risk of accidents due to the use of flammable substances, as well as the operation of equipment at elevated temperatures and pressures. To address this issue, this study aims to analyze data on various potential scenarios for accidents at an oil refinery and develop an artificial neural network (NENKAZ ANN) to predict potential risk areas using real-time manufacturing data. This research employed the following techniques and tools: HAZID (hazard identification) method, hazard modeling software (ALOHA), Matlab R2022b, and the Levenberg-Marquardt algorithm, as well as MSE and R2 metrics. The testing of the developed ANN showed that it achieves high accuracy in predicting the distance traveled by the chemical explosion at oil refineries. The results of the prediction demonstrate that the coefficients of determination during training, validation, and testing are all 0.99. The average absolute error values for calculations using NENKAZ ANN are 190.42, 294.31, and 688.97, respectively. The forecasts generated in the study determined safe air zone dimensions in the period of atmospheric pollution resulting from an oil refinery accident. The developed ANN will be utilized at oil refineries in Kazakhstan, with the potential for implementation at other refineries worldwide. In the field of refinery management, NENKAZ ANN provides a means to evaluate the safe distance from the site of an explosion of a hazardous chemical substance, considering preliminary weather forecasts received from the meteorological service.