纸浆洗涤过程建模及多目标优化

Control of pulp washing process based on neural network

纸浆洗涤过程是一个影响因素多、延时大的非线性过程。基于神经网络和最小二乘法建立了洗后浆残碱和首段黑液波美度的软测量模型,并从优质,高产,低耗三方面对洗涤过程进行了多目标优化。计算机仿真和现场数据比较表明,残碱和黑液波美度软测量模型预估性良好,多目标优化不仅保证了洗涤质量,并且大幅提高了纸浆洗涤系统的出浆量。

Pulp washing process is a process of multi-factors,long time delay,nonlinear characteristics.The soft sensor model of the residual soda of washed pulp and the Baume degree of the first stage black liquor were modeled via two-step neural network identification method and least square method.A multi-objective optimization model of pulp washing process was done from three aspects of high quality,high yield,and low consumption.Simulation results and the practical application indicate that the soft sensor model of residual soda and Baume degree have a good esti-mation.Multi-objective optimization not only ensures the quality of pulp washing but also improves the output of washed pulp.