Advanced energy-saving optimization strategy in thermo-mechanical pulping by machine learning approach

Abstract

Thermo-mechanical Pulping (TMP) is one of the most energy-intensive industries where most of the electrical energy is consumed in the refining process. This paper proposes the energy-saving refining optimization strategy by integrating the machine learning algorithm and heuristic optimization method. First, refining specific energy consumption (RSEC) and pulp quality identification models are developed using Artificial Neural Networks. In the second step, the developed identification models are incorporated with the Genetic algorithm to minimize the total refining specific energy consumption while maintaining the same pulp quality. Simulation results prove that a deep multilayer perceptron neural network is a powerful tool for creating refining energy and quality identification models with the model correlation coefficients of 0.97, 0.94, 0.92, and 0.67 for the first-stage RSEC, second-stage RSEC, final pulp fiber length, and freeness prediction, respectively. Findings confirm that the average total RSEC reduction of 14 % is achievable by utilizing the proposed optimization method.