Applicability of several machine learning models in estimation of vortex tube trapping efficiency

Abstract

A vortex tube ejector comprises a tube with a slitted crown that lies flushed across the entire width of the channel bed surface. The bed and suspended loads are ejected with minimal flushing water through the slit with the same efficacy as any other alternative extractors. The whirling flow phenomena through the vortex duct are very complex, so ordinary classical models have resulted contrary to required design guidelines. So, machine learning (ML) models; artificial neural network (ANN), deep neural network (DNN), gradient boosting machine (GBM), stacked ensemble (SE), and adaptive neuro-fuzzy inference system (ANFIS) are used to predict vortex tube trapping efficiency (VTE). The input dataset takes the size of the sediment (Sz), intensity (I) of the sediment, the ratio of slit thickness to diameter of the tube (th/dia), and extraction ratio (Extro) while trapping efficiency (T.E.) is taken as output. Based on statistical assessments, GBM appears to be better than all-proposed models. However, other proposed ML models give comparable performance. The classical models, multivariate linear, and nonlinear regression techniques are also comparatively providing good results. According to sensitivity analyses, the extraction ratio is the most relevant parameter in evaluating the VTE.