Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors

Reconstruction and analysis of negatively buoyant jets with interpretable machine learning

In this paper, negatively inclined buoyant jets, which appear during the discharge of wastewater from processes such as desalination, are observed. A detailed numerical investigation is necessary to minimize harmful effects and assess environmental impact. Selecting 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, validated by experimental data from previous research. The average prediction of ML models has R² 0.94±0.05, RMSE 0.42±0.14 and RRSE 0.24 ± 0.09, whereas the best prediction was obtained by Artificial Neural Network with R² 0.98, RMSE 0.28 and RRSE 0.16. To understand the influence of input parameters on the geometrical characteristics of inclined buoyant jets, the SHAP feature interpretation method was used.

Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete

Geopolymer concrete (GPC) based on fly ash (FA) is being studied as a possible alternative solution with a lower environmental impact than Portland cement mixtures. However, the accuracy of the strength prediction still needs to be improved. This study was based on the investigation of various types of machine learning (ML) approaches to predict the compressive strength (C-S) of GPC. The support vector machine (SVM), multilayer perceptron (MLP), and XGBoost (XGB) techniques have been employed to check the difference between the experimental and predicted results of the C-S for the GPC. The coefficient of determination (R²) was used to measure how accurate the results were, which usually ranged from 0 to 1. The results show that the XGB was a more accurate model, indicating an R² value of 0.98, as opposed to SVM (0.91) and MLP (0.88). The statistical checks and k-fold cross-validation (CV) also confirm the high precision level of the XGB model. The lesser values of the errors for the XGB approach, such as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), were noted as 1.49 MPa, 3.16 MPa, and 1.78 MPa, respectively. These lesser values of the errors also indicate the high precision of the XGB model. Moreover, the sensitivity analysis was also conducted to evaluate the parameter’s contribution towards the anticipation of C-S of GPC. The use of ML techniques for the prediction of material properties will not only reduce the effort of experimental work in the laboratory but also minimize the cast and time for the researchers.