Energy simulation and variable analysis of refining process in thermo-mechanical pulp mill using machine learning approach

Data from two thermo-mechanical pulp mills are collected to simulate the refining process using deep learning. A multilayer perceptron neural network is utilized for pattern recognition of the refining variables. Results show the impressive capability of artificial intelligence methods in refining energy simulation so that the correlation coefficient of 98% is accessible. A comprehensive parametric study has been made to investigate the effect of refining disturbance variables, plate gap and dilution water on refining energy simulation. The generated model reveals the non-linear hidden pattern between refining variables, which can be used for optimal refining control strategy. Considering the disturbance variables' effect in refining energy simulation, model accuracy could increase by 15%. Removing the plate gape from predictive variables reduces the simulation determination coefficient by up to 25% in both mills, while the mentioned value for removing dilution water is 9-17% in mill 1 and about 35% in mill 2.