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<p>This study predicts pollutant propagation using numerical simulation and machine learning methods downstream the Ural River and into the Caspian Sea as a result of an accidental oil spill near the Ural River estuary near Atyrau, Kazakhstan. A CFD model based on the Reynolds-averaged Navier-Stokes equations, coupled with concentration and temperature equations, was developed to simulate the hydrodynamic behavior of the oil product selected as a pollutant. The simulation domain covers the northeastern part of the Caspian Sea. The direct computation CFD model was numerically solved using the SIMPLE algorithm, which relates pressures and velocities. To reduce the computation time, the Bi-LSTM neural network was used to predict the value of pollutant concentrations at specific locations. The Bi-LSTM model was trained based on the data from the direct computations performed using the CFD model at specific control points of the computational domain. The results show that the BiLSTM architecture effectively predicts pollutant values, although improvements are needed for the models at some points with higher error levels and noise. The results demonstrate the effectiveness of combining CFD calculations and machine learning methods to reduce the computation time in predicting and monitoring the spread of oil spills. The use of this approach in the future will lead to the fact that it will be possible to carry out operational calculations using machine learning methods using less computing resources compared to direct calculations.</p>

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Prediction of the concentration value of emissions from an enterprise using machine learning and computational fluid dynamics methods

  • 期刊论文

  • 发表日期：

    2026年3月23日

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  • Alibek Issakhov,
  • Nurtugan Rysmambetov,
  • Aidana Sabyrkulova,
  • Aizhan Abylkassymova

摘要

This study predicts pollutant propagation using numerical simulation and machine learning methods downstream the Ural River and into the Caspian Sea as a result of an accidental oil spill near the Ural River estuary near Atyrau, Kazakhstan. A CFD model based on the Reynolds-averaged Navier-Stokes equations, coupled with concentration and temperature equations, was developed to simulate the hydrodynamic behavior of the oil product selected as a pollutant. The simulation domain covers the northeastern part of the Caspian Sea. The direct computation CFD model was numerically solved using the SIMPLE algorithm, which relates pressures and velocities. To reduce the computation time, the Bi-LSTM neural network was used to predict the value of pollutant concentrations at specific locations. The Bi-LSTM model was trained based on the data from the direct computations performed using the CFD model at specific control points of the computational domain. The results show that the BiLSTM architecture effectively predicts pollutant values, although improvements are needed for the models at some points with higher error levels and noise. The results demonstrate the effectiveness of combining CFD calculations and machine learning methods to reduce the computation time in predicting and monitoring the spread of oil spills. The use of this approach in the future will lead to the fact that it will be possible to carry out operational calculations using machine learning methods using less computing resources compared to direct calculations.