Application of Artificial Neural Networks for Investigation of Pressure
  Filtration Performance, a Zinc Leaching Filter Cake Moisture Modeling
Masoume Kazemi Davood Moradkhani Alireza A. Alipour
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.