间歇制浆蒸煮终点预测方法  

Prediction Method of Batch Pulp Cooking End Point

作　　者：李向阳[1] 朱学峰[1] 黄道平[1] 刘焕彬[2] 

机构地区：[1]华南理工大学电子与信息学院,广州510641 [2]华南理工大学制浆造纸工程国家重点实验室,广州510641

出　　处：《计算机与应用化学》2001年第2期117-122,共6页Computers and Applied Chemistry

基　　金：国家自然科学基金!(编号 :698740 14 );国家"863"项目!(编号 :863 -5 11-945 -0 0 7)

摘　　要：在分析常用蒸煮模型的基础上 ,提出了基于神经网络的制浆蒸煮过程建模方法。与BP神经网络相比 ,RBF神经网络具有最佳逼近能力、收敛速度快和不存在局部极小点等优点 ,因而选用了RBF神经网络作为建模工具。在决定RBF神经网络的输入和输出变量时 ,充分利用了现场可测量的物理量和制浆蒸煮过程知识 ,其输入变量比常用蒸煮模型增加了硫化度和木片合格率 ,其输出变量采用实际过程测量所需的终点H因子的对数 ,这样就减少了RBF神经网络的规模 ,提高了训练速度。对工厂的实际数据应用表明 ,该RBF神经网络模型的预测精度高于传统的Hatton模型。Based on the analyses of conventional cooking models including Hatton model, MODOCell model, Kerr model and Chari model, this paper presents a new modeling method, the neural network modeling method. Compared to the BP neural network, the RBF neural network (RBFNN) has the best approximation ability, higher training speed and no local minima. So, the RBF neural network is an effective modeling tool and this paper uses it. When choosing the input variables and the output variable of the RBFNN model, this paper exploits the measurable variables in the field and the knowledge of cooking process. The input variables of RBFNN model include Kappa number, effective alkali, sulfidity and chip quality. The later two input variables are not included in conventional cooking models.Among the conventional models, Hatton model, which reveals there is a linear relationship between the Kappa number and the logarithm of H factor, is relatively easily applicable. According to this revelation, the output variable of RBFNN model is the logarithm of H\|factor of cooking end point instead of the H\|factor itself. The advantage of the choice of the output variable of RBFNN is that it can reduce the degree of nonlinearity which the RBFNN model need express and so that it reduces the node number of the hidden layer of RBFNN and enhances the training speed. Both the RBFNN model and Hatton model are applied to the cooking data sampled at a cooking plant of a large paper\|making factory in Fujian, a southeast province of China. The software development platform is MATLAB. During the processes of model training and model predicting, shift data window technology and one\|step prediction technology are applied. The application results reveal that RBFNN model is superior to Hatton model in prediction precision. The reason why the RBFNN model has higher prediction precision than conventional cooking models is that the RBFNN model can use more input information and has the ability to approximate any nonlinear functions. The neural network model

关 键 词：蒸煮 人工神经网络模型 KAPPA值 H因子 径向基函数网络 间歇制浆 终点预测方法 

分 类 号：TS74[轻工技术与工程—制浆造纸工程] TP183[自动化与计算机技术—控制理论与控制工程]