Reconstruction and analysis of negatively buoyant jets with interpretable machine learning
This work addresses environmental impact assessment for wastewater discharge processes, but it is incremental as it applies existing machine learning methods to a specific domain.
The paper tackled predicting the geometry of negatively buoyant jets from wastewater discharge to minimize environmental harm, achieving an R2 of 0.98 and RMSE of 0.28 using an Artificial Neural Network.
In this paper, negatively inclined buoyant jets, which appear during the discharge of wastewater from processes such as desalination, are observed. To minimize harmful effects and assess environmental impact, a detailed numerical investigation is necessary. The selection of appropriate geometry and working conditions for minimizing such effects often requires numerous experiments and numerical simulations. For this reason, the application of machine learning models is proposed. Several models including Support Vector Regression, Artificial Neural Networks, Random Forests, XGBoost, CatBoost and LightGBM were trained. The dataset was built with numerous OpenFOAM simulations, which were validated by experimental data from previous research. The best prediction was obtained by Artificial Neural Network with an average of R2 0.98 and RMSE 0.28. In order to understand the working of the machine learning model and the influence of all parameters on the geometrical characteristics of inclined buoyant jets, the SHAP feature interpretation method was used.