Abstract
Pulp washing process has the features of multivariate, time delay, nonlinearity. Considering the difficulties of modeling and optimal control in pulp washing process, a data-driven operational-pattern optimization method is proposed to model and optimize the pulp washing process in this paper. The most important quality indexes of pulp washing performance are residual soda in the washed pulp and Baume degree of extracted black liquor. Considering the difficulties of modeling, online measurement of these indexes, two-step neural networks, and multivariate logistic regression are used to establish the prediction models of residual soda and Baume degree. The mathematical model of the washing process can be identified, and the indexes can meet the production requirements. In the target of better product quality, low cost, and low energy consumption, a multiobjective problems is solved by ant colony optimization algorithm based on the optimized operational-pattern database. It shows that the theoretical analyses are correct and the practical applications are feasible, optimization control system has been designed for the pulp washing process, and the practical results show that pulp production increased by 20% and water consumption decreased by nearly 30%. This method is effective in the pulp washing process.
Highlights
Pulp washing is one of the important parts in the pulpmaking process
Its main purpose is to wash soluble inorganic and organic substances in pulp and obtain high concentration of black liquor [1]. e most important quality indexes to evaluate pulp washing performance are residual soda for the washed pulp and Baume degree for the yielded thick black liquor. e conductivity of the residual soda extracted from the washed pulp is a measurement of dissolved solid content; It is expected that the residual soda in the washed pulp should be as little as possible. e Baume degree is used to denote the consistency of the thick liquor obtained in the first stage of the pulp washer [2]; it is required to be as high as possible in production
E average of residual soda decreased, and the Baume degree is increased to 9.3Be. e concentration and flow of the inlet pulp tend to the upper limit of the production index, the pulp yield is increased by 20%, and the water consumption is decreased by nearly 30%
Summary
Pulp washing is one of the important parts in the pulpmaking process. Its main purpose is to wash soluble inorganic and organic substances in pulp and obtain high concentration of black liquor [1]. e most important quality indexes to evaluate pulp washing performance are residual soda for the washed pulp and Baume degree for the yielded thick black liquor. e conductivity of the residual soda extracted from the washed pulp is a measurement of dissolved solid content; It is expected that the residual soda in the washed pulp should be as little as possible. e Baume degree is used to denote the consistency of the thick liquor obtained in the first stage of the pulp washer [2]; it is required to be as high as possible in production. E most important quality indexes to evaluate pulp washing performance are residual soda for the washed pulp and Baume degree for the yielded thick black liquor. In this method, based on online learning and calculating the control quantity, the current state is matched by a large amount of process data. In view of this, aiming at the characteristics of the pulping process, a data-driven operational-pattern optimization method to model and optimize the pulp washing process is proposed in this paper.
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