Application of Artificial Neural Networks for Investigation of Pressure Filtration Performance, a Zinc Leaching Filter Cake Moisture Modeling

TL;DR

This study developed an artificial neural network model to accurately predict the moisture content of filter cakes in zinc pressure filtration, considering multiple process parameters, demonstrating high predictive performance with R2 values above 0.8.

Contribution

The paper presents a novel ANN model for predicting filter cake moisture in zinc filtration, incorporating multiple parameters and validated with extensive experimental data.

Findings

ANN achieved R2 of 0.88 and 0.83 for two fabric types.

Model demonstrated low MSE and MAE, indicating high accuracy.

Predictive model can optimize filtration process parameters.

Abstract

Machine Learning (ML) is a powerful tool for material science applications. Artificial Neural Network (ANN) is a machine learning technique that can provide high prediction accuracy. This study aimed to develop an ANN model to predict the cake moisture of the pressure filtration process of zinc production. The cake moisture was influenced by seven parameters: temperature (35 and 65 Celsius), solid concentration (0.2 and 0.38 g/L), pH (2, 3.5, and 5), air-blow time (2, 10, and 15 min), cake thickness (14, 20, 26, and 34 mm), pressure, and filtration time. The study conducted 288 tests using two types of fabrics: polypropylene (S1) and polyester (S2). The ANN model was evaluated by the Coefficient of determination (R2), the Mean Square Error (MSE), and the Mean Absolute Error (MAE) metrics for both datasets. The results showed R2 values of 0.88 and 0.83, MSE values of 6.243x10-07 and 1.086x10-06, and MAE values of 0.00056 and 0.00088 for S1 and S2, respectively. These results indicated that the ANN model could predict the cake moisture of pressure filtration in the zinc leaching process with high accuracy.