Experimental Study on the Filtration Characteristics and Sediment Distribution Influencing Factors of Sand Media Filters

Abstract

The sand media filter is a crucial component of micro-irrigation filtration systems. Investigating the effects of various factors on filtration performance and the migration patterns of sediment particles within the filter can enhance the healthy operation of sand media filters. This study, based on a sand media filter model, conducts indoor hydraulic experiments, selecting different experimental factors and observation indices for research under varying conditions. Results indicate that filter thickness and raw water concentration are positively correlated with the turbidity of filtered water samples, while changes in filtration rate have no obvious impact on turbidity. When filter thickness is large, particle content differs obviously from other filter thicknesses, and the effect of raw water concentration changes on particle content is similar to that of filter thickness changes. Sediment particle size distribution within the filter layer is primarily concentrated in the upper region. By focusing on the area situated 20 cm below the filter layer surface, the sediment retention rate reached 80% or higher at a depth of 20 cm below the filter layer surface. As the depth of the filter layer increases, the sediment retention characteristics show a decreasing trend for larger particle sizes and an increasing trend for smaller particle sizes. Head loss is positively correlated with filtration rate, raw water concentrations, and filter thickness changes. It is suggested that, when meeting micro-irrigation water quality requirements, the filter material particle size can be appropriately increased to improve filtration efficiency and reduce energy consumption. These research findings are highly significant for sand media filter material selection and working condition design.

A Standardized Treatment Model for Head Loss of Farmland Filters Based on Interaction Factors

“…Although numerous studies have explored the factors influencing filter performance, few have comprehensively considered the interactions among multiple factors [27][28][29][30][31]. By introducing interaction terms, this study not only unveils the complex relationships The different combinations of lines in Figure 11 represent different optimal solutions obtained by adjusting parameters through model algorithms when applying the response surface method.…”

“…Although numerous studies have explored the factors influencing filter performance, few have comprehensively considered the interactions among multiple factors [27][28][29][30][31]. By introducing interaction terms, this study not only unveils the complex relationships among these factors but also enhances the predictive accuracy of the model.…”