纸浆洗涤过程建模及多目标优化  

Control of pulp washing process based on neural network

作　　者：单文娟[1] 汤伟[1,2] 侯顺利[1] 王孟效 

机构地区：[1]陕西科技大学造纸工程学院,西安710021 [2]陕西西微测控工程有限公司,陕西咸阳712081

出　　处：《中华纸业》2011年第10期14-18,共5页China Pulp & Paper Industry

基　　金：国家自然科学基金资助项目(30972322)

摘　　要：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.

关 键 词：纸浆洗涤过程 神经网络 最小二乘法 软测量 多目标优化 

分 类 号：TS736[轻工技术与工程—制浆造纸工程]