Comparison of Machine Learning Algorithms for Prediction of Textile Effluent Treatment Efficiency Using Anaerobic Process

Abstract

The prediction of pollutants removal efficiency from the generated effluent of a treatment plant is valuable and can reduce the time, sampling and energy required during performance assessment. The present study aims to predict the effect of different input parameters on the treatment efficiency of the developed microbial-based anaerobic process for textile effluent using machine leaning algorithms. The decolourisation and chemical oxygen demand (COD) reduction of the treated effluent were predicted on the basis of the three different input parameters pH, COD and colour value of the textile wastewater. The effectiveness of different machine learning algorithms, support vector machines (SVM), random forest (RF), gradient boost regressor (GBR), AdaBoost, extreme gradient boosting (XGB) regressor and voting regressor, were evaluated based on the correlation coefficient (R²) value. The results revealed that the RF achieved the highest accuracy for decolourisation (training data R²: ∼0.85 and test data R²: ∼0.84) as well as COD reduction (training data R²: ∼0.87 and test data R²: ∼0.94) compared to the other algorithms. These results were validated experimentally, confirming that RF can be used as a tool to predict the performance efficiency of a microbial-based treatment system.