Informatics Publishing Limited

C. Karthik ¹, K. Valarmathi ¹, M. Rajalakshmi ¹

Affiliations
1 Kalasalingam University, Tamilnadu, IN

Abstract

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. By adjusting the set point of the steam pressure controllers to the cylinders we can control the moisture in the paper. 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 then time is directly correlated to economic profit. The process model is identified regularly and the changes in its characteristics are observed periodically. This method of identification gives a great advantage over the conventional controller tuning methods, which uses the process model at the nominal operating conditions. A number of structures are available for modeling a process 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.

Keywords

Drying Process of Paper Machine, System Identification, Artificial Neural Network, T-S Fuzzy Modelling, Hammerstein-Weiner Model.

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M. Rajalakshmi ¹, C. Karthik ²

Affiliations
1 Department of EIE, Kamaraj College of Engineering and Technology, Virudhunagar, Tamilnadu, IN
2 Department of EIE, Kalasalingam University, Krishnankoil, Tamilnadu, IN

Abstract

This paper discusses the application of Neural Network Auto-Regressive model with eXogenous inputs (NNARX) in the area of identification of nonlinear dynamical systems. The main contribution of this paper is to identify suitable model and model structure of nonlinear dynamic system. In this paper Neural Network Auto-Regressive model with eXogenous inputs (NNARX) and ANFIS models are applied to identify highly nonlinear dynamic process and comparison was made between NNARX and ANFIS. The simulation results show that ANFIS is very effective to identify the nonlinear system.

Keywords

pH Process, NNARX, ANFIS.

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