Sensitivity analysis of a particle retention model and application to a pressurised sand bed filter for drip irrigation

Abstract

Accurate model predictions are fundamental when designing porous media filters in drip irrigation systems that reduce both energy and water consumption. Many studies have focused on improving filter hydraulics under clean water conditions but further advances may require consideration of particle retention by the granular media. Rapid deep bed filtration models employ conservative equations and empirical correlations to determine the behaviour of particle depositions on the media. These models involve many input parameters, some of which have an inherent uncertainty range. Therefore, thorough model sensitivity analyses must be carried out prior to their use as predictors for the assessment of new filter designs. This paper applies both local and global (variance-based Sobol indices) sensitivity methods to a comprehensive particle retention model that is able to describe the main three stages of the filtration process. Uncertainty ranges of 15 input variables were defined. Three model outputs were analysed (flow particle concentration at the filter's outlet, mass of retained particles per unit area, and total pressure drop through the porous media) at different flow times. The results of the global sensitivity analysis indicated that the relevant model parameters vary depending on the filter stage. The rank of influential input variables also varied depending on the chosen output variable. The least absolute shrinkage and selection operator (LASSO) regression analysis method was also applied but the high non-linearity of the model reduced its predictive capacity in most of the situations analysed. Conclusions from the global sensitivity analysis were employed for model calibration with experimental data