Soft Sensing and Multi-objective Optimization of Pulp Washing Process Based on Data-Driven Technology

Due to the multi-variable, time-delay, and nonlinear characteristics of the pulp washing process, it is difficult to accurately measure and optimize the process. An asymmetrical nonlinear control system can be decomposed into a group of low-dimensional subsystems, which brings great convenience to the analysis and design of the system. In this paper, the concept of symmetry is used to simplify nonlinear optimal control problems, and data-driven theory is used to solve optimal policy problems. This paper proposes a data-driven operating model optimization method to model and optimizes the pulp washing process. The most important quality indicators of pulp washing performance are the alkali residue in washing pulp and the Baum é degree of the extracted black liquor. Considering the difficulty of modeling, online measurement of these indicators, two-step neural network, and multiple logistic regression were used to build a prediction model for soda water and Baum é degree. The mathematical model of the washing process can be identified, and the indicators meet the production requirements. With the goal of better product quality, low cost, and low energy consumption, based on the optimized operation mode database, the ant colony optimization (ACO) algorithm was used to solve the multi-objective problem. Theoretical analysis and practical application were carried out, and the optimal control system of the pulp washing process was designed. The actual results showed that the pulp output was increased by 20%, and the water consumption was reduced by nearly 30%. This method is effective during pulp washing.