Nonlinear modeling of moisture control of drying process in paper machine

被引:5
|
作者
Karthik, C. [1 ]
Valarmathi, K. [1 ]
Rajalakshmi, M. [1 ]
机构
[1] Kalasalingam Univ, Srivilliputhur 626190, India
关键词
Drying process of paper machine; system identification; Artificial neural network; T-S fuzzy modelling; Hammerstein-weinner model;
D O I
10.1016/j.proeng.2012.06.139
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper is focuses modeling of the last part of the paper machine - the drying section. Paper is dried by letting it pass through a series of steam heated group of cylinders and the evaporation is thus performed by the latent heat of vaporization of the steam. The moisture in the paper is controlled by adjusting the set point of the steam pressure controllers to the cylinders. There exist several incentives to focus on the performance of the moisture control. The time to perform a grade change is often limited by the moisture and shorter grade change time is directly correlated to economic profit. The plant model is identified periodically and the changes in its dynamic characteristics are observed. Periodic identification gives a great advantage over the conventional controller tuning methods, which uses the plant model at the nominal operating conditions. A variety of model structures are available to assist in modelling a system. Model for the drying process of Paper industry is established based on gathering 1000 groups of 2500 real-time sample data. Based on the collection of data, that was adapted to both the conventional and intelligent modelling process. Finally the suitable model is tuned with the suitable controller for optimal control of the drying process. (c) 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Noorul Islam Centre for Higher Education
引用
收藏
页码:1104 / 1111
页数:8
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