A new predictive model for the filtered volume and outlet parameters in micro-irrigation sand filters fed with effluents using the hybrid PSO-SVM-based approach

Abstract

Filtration is a key operation in micro-irrigation for removing the particles carried by water that could clog drip emitters. Currently, there are not sufficiently accurate models available to predict the filtered volume and outlet parameters for the sand filters used in micro-irrigation systems. The aim of this study was to obtain a predictive model able to perform an early detection of the filtered volume and sand filter outlet values of dissolved oxygen (DO) and turbidity, both related to emitter clogging risks. This study presents a novel hybrid algorithm, based on support vector machines (SVMs) in combination with the particle swarm optimization (PSO) technique, for predicting the main filtration operation parameters from data corresponding to 769 experimental filtration cycles in a sand filter operating with effluent. This optimization technique involves kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. To this end, the most important physical-chemical parameters of this process are monitored and analyzed: effective sand media size, head loss across the filter and filter inlet values of dissolved oxygen (DO), turbidity, electrical conductivity (Ec), pH and water temperature. The results of the present study are two-fold. In the first place, the significance of each physical-chemical variables on the filtration is presented through the model. Secondly, a model for forecasting the filtered volume and sand filter outlet parameters is obtained with success. Indeed, regression with optimal hyperparameters was performed and coefficients of determination equal to 0.74 for outlet turbidity, 0.82 for filtered volume and 0.97 for outlet dissolved oxygen were obtained when this hybrid PSO-SVM-based model was applied to the experimental dataset, respectively. The agreement between experimental data and the model confirmed the good performance of the latter