Research On Optimization Of Batch Digester Parameters

Pulp cooking process turns papermaking feedstock into pulp and determines paper's quality to a great extent. Meanwhile, Pulp cooking process is characteristic of high energy consumption, high materials consumption and high pollution. Today, we are vigorously developing a resource-conserving and environment-friendly society, it is an important task for paper and pulp industry about how to realize saving energy, lowering consumption and reducing pollutants discharge. Considering the viewpoint of energy efficiency, optimization of batch digester parameters was researched. The innovation and contribution of this thesis are as follows:(1) The mechanism of batch digester was analyzed, and the related batch digester parameter models were established, including quality model, yield model and energy consumption model. Firstly, according to the production data of one papermaking mill, a SVM model about kappa number was developed, which accurately reflect the relationship between kappa number and cooking parameters. Secondly, based on Hatton's empirical model, a model about pulp yield was developed. Thirdly, after analyzing batch digester's steam consumption, a model about batch digester's energy consumption was developed.(2) Because it is difficult to use SVM model in conventional optimization approach, an improved Genetic Algorithm using niching technology and adaptive crossover technology was presented, which realizes energy conservation and consumption reduction on the premise of guaranteeing the pulp's quality. On the basis of Standard Genetic Algorithm, the improved Genetic Algorithm integrates niching technology and adaptive crossover technology to ensure solution's convergence and distribution, thereby enhance its ability of local and global search. This method was used on the optimization of one papermaking mill, the simulation results showed good performance. It saved 3.52% of steam,17.18% of white liquid dosage and 0.37% of sulfidity, comparing to real production data.(3) To realize really multi-objective optimization, an improved NSGA-II algorithm for optimization of batch digester parameters method was presented. On the basis of former optimization model, this method adds in one more optimization goal, which is improving the pulp yield. So the purpose of the optimization method is to realize energy conservation and consumption reduction, improve pulp yield on the premise of guaranteeing the pulp's quality. As to the improved NSGA-II algorithm, a fitness assessment method which combines non-dominated front and crowding distance was introduced to describe the individual's quality. In order to generate better individuals, an adaptive crossover and mutation mechanism introduced by Srinivas was employed. Moreover, a foreign group immigration strategy was adopted to avoid falling into local Pareto optimal solution. Simulation results show the improved NSGA-II algorithm can better ensure the convergence and diversity of solutions, compared with NSGA-II. The improved NSGA-II algorithm was used on multi-objective optimization of batch digester parameters, and it received obvious good performance.