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.

翻译：机器学习（ML）是材料科学应用中的强大工具。人工神经网络（ANN）作为一种机器学习技术，能够提供高预测精度。本研究旨在开发一个ANN模型，用于预测锌生产过程中压力过滤的滤饼水分。滤饼水分受七个参数影响：温度（35和65摄氏度）、固体浓度（0.2和0.38克/升）、pH值（2、3.5和5）、吹气时间（2、10和15分钟）、滤饼厚度（14、20、26和34毫米）、压力及过滤时间。研究使用两种织物（聚丙烯S1和聚酯S2）进行了288次测试。通过决定系数（R²）、均方误差（MSE）和平均绝对误差（MAE）指标对ANN模型在两个数据集上进行了评估。结果表明，S1和S2的R²值分别为0.88和0.83，MSE值分别为6.243×10⁻⁷和1.086×10⁻⁶，MAE值分别为0.00056和0.00088。这些结果表明，ANN模型能够高精度预测锌浸出过程中压力过滤的滤饼水分。