Application of Statistical and Machine Learning Techniques for Laboratory-Scale Pressure Filtration: Modeling and Analysis of Cake Moisture

Abstract

ABSTRACT This study deals with the modeling and analysis of the pressure filtration process using statistical and machine learning techniques. The effects of externally controllable process-influencing factors such as pressure, pH, temperature, solids concentration, filtration time, air-blow time, and cake thickness on filtration performance, measured in terms of cake moisture, were modeled. A 9-factor regression model based on an exhaustive search algorithm and a 7-6-1 artificial neural network (ANN) model based on a resilient backpropagation algorithm were developed and gave R2 values of 0.84 and 0.94, respectively. Relative importance of input variables was analyzed using novel methods such as added-variable plots based on the regression model and Olden’s method based on the ANN model. Results from both methods established a negative correlation for pressure, solids concentration, filtration time, temperature, and air-blow time and a positive correlation for cake thickness and pH. Analysis from regression and ANN models indicated pH to be the most significant process-influencing factor. Even though both models served as good interpretable models, the ANN model outperformed the regression model in terms of predictive capability, with an R2 value of 0.965 compared with the regression model’s 0.750 for the test dataset.