Work

Artificial Neural Networks for Defining the Operational Parameters of Sand Filters for Irrigation

Year: 2025

Type: article

Abstract: ABSTRACT The difficulty in defining the working configuration during the operation of sand filters arises from the complexity of the relationships among the variables in the filtration and backwashing processes. The objective was to use artificial neural networks to model the pressure loss and removal efficiency of filtration in a commercial sand filter. The experimental tests covered three grain sizes of silica sand (fine, medium and coarse) and four filtration rates (20, 40, 60 and 75 m h −1 ). Three artificial neural networks (ANN1, ANN2 and ANN3) were built to define the duration of the filtration cycle, pressure loss and filtered volume. ANN1 was based on modelling pressure loss through the sand filter, whereas ANN2 and ANN3 were based on the effluent suspended solids. The variables used were the filtration rate, filtered volume, grain size, suspended solids and pressure loss. ANN1 presented good performance with the 5‐4‐1 architecture (five neurons in the input layer, four neurons in the hidden layer, and one neuron in the output layer). An increase in the filtration rate reduced the duration of the filtration cycle, whereas an increase in the grain size extended the duration. The ANN2 and ANN3 networks performed poorly and, therefore, were disregarded.

Source: Irrigation and Drainage

DOI: 10.1002/ird.70058

Authors: Mádilo Lages Vieira Passos, Fábio Ponciano de Deus, Evelise Roman Corbalan Góis Freire, Adriano Valentim Diotto, Michael Silveira Thebaldi

Institution: Universidade Federal de Lavras

Language: English

Topic: Engineering and Agricultural Innovations

Subfield: Aquatic Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Clean water and sanitation

Open Access status: hybrid

Estimated APC paid: $3,450