Model-Based Control for Moisture in Paper Making Process

  • C. Karthik
  • K. Suresh
  • K. Valarmathi
  • R. Jacob Rajesh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)


This project deals with the performance evaluation on the comparison of model-based control for drying process of paper industry. The dryer section is the last part of the paper machine and consists of a large number of rotating steam-heated cast iron cylinders by adjusting the set point of the stream pressure controller to the cylinders. In the design of model reference adaptive control, schema is used, in which the adaptive law has been developed by MIT rule. Similarly, design of PID and MRAC controller is used. This paper presents a nonlinear dynamic control, based on heat and mass balance for steam, cylinder, and paper. The control was performed to the combined drying process system using both the adaptive control algorithm and MPC controller method and its results were analyzed. A simulation is carried out using MATLAB. Simulation results reveal clear benefits of the model reference adaptive control over traditional controller and MPC controller methods. Thus, by controlling, this process proves real incentives for industrial implementation.


Drying section System identification PID MPC MRAC 


  1. 1.
    C. Karthik, K. Valarmathi, M. Rajalakshmi, Non linear modeling of moisture control of drying process in paper meachine. Sci. Direct Trans. Procedia Eng. 38, 1104–1111 (2012)Google Scholar
  2. 2.
    J. De Temmermana, P. Dufourb, B. Nicolaia, H. Ramona, MPC as control strategy for pasta drying processes. Sci. Direct Trans. Comput. Chem. Eng. 33, 50–57 (2011)CrossRefGoogle Scholar
  3. 3.
    L. Obregon, L. Quinones, C. Velazquez, Model predictive control of a fluidized bed dryer with an inline NIR as moisture sensor. Sci. Direct Trans. Control Eng. Pract. 21, 509–517 (2012)CrossRefGoogle Scholar
  4. 4.
    V.M. Cristea, M. Baldea, P. Agachi, Model predictive control of an industrial Dryer. Science direct symposium transactions on computer aided process engineering, 10 (2012)Google Scholar
  5. 5.
    A. Cortinovis, M. Mercang, T. Mathur, J. Poland, M. Blaumann, Nonlinear coal mill modeling and its application to model predictive control. Sci. Direct Trans. Dept. control Eng. 21, 308–320 (2013)Google Scholar
  6. 6.
    A.J. Gallego, E.F. Camacho, Adaptative state-space model predictive control of a parabolic-trough field. Sci. Direct Trans. Dept. Control Eng. 20, 904–911 (2012)Google Scholar
  7. 7.
    M. Morari, U. Maede, Nonlinear offset-free model predictive control. Sci. Direct Trans. Dept. Autom. 48, 2059–2067 (2012)MATHGoogle Scholar
  8. 8.
    P. James, M.J. Balas, Direct model reference adaptive control of linear systems with input/output delays. Sci. Direct Trans. Dept. Electr. Comput. Eng. 3, 445–462 (2013)MATHGoogle Scholar
  9. 9.
    M. Duarte-Mermoud, P. La Rosa, MRAC using observers with unknown inputs. Dept. Electr. Eng. (2007)Google Scholar
  10. 10.
    J. Guo, G. Tao, A multivariable MRAC scheme with sensor uncertainty. IEEE Trans. Dept. Electr. Comput. Eng. 22904, 6632–6637 (2009)Google Scholar
  11. 11.
    C. Karthik, M. Rajalakshmi, Nonlinear identification of pH process using NNARX model. CIIT Int. J. Artif. Intell. Syst. Mach. Learn. 4(8), 502–506 (2012)Google Scholar
  12. 12.
    C. Karthik, M. Rajalakshmi, On linear structure identification of pH process, in IEEE - ICAESM (2012), p. 45Google Scholar
  13. 13.
    C. Karthik, M. Rajalakshmi, K. Valarmathi, Nonlinear modeling of moisture control of drying process in paper machine. Elsevier Procedia Eng. 38, 1104–1111 (2012)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • C. Karthik
    • 1
  • K. Suresh
    • 1
  • K. Valarmathi
    • 2
  • R. Jacob Rajesh
    • 1
  1. 1.Kalasalingam UniversityKrishnankoilIndia
  2. 2.P.S.R. Engineering CollegeSivakasiIndia

Personalised recommendations