Development of a Jacobian Module for Advanced Pulp and Paper Simulation and Control
Source abstract
Abstract
Pulp and paper processes frequently present nonlinear behavior, which means that process dynam-ics change with the operating points. These nonlinearities can challenge process control. PID controllers are the most popular controllers because they are simple and robust. However, a fixed set of PID tuning parameters is gen-erally not sufficient to optimize control of the process. Problems related to nonlinearities such as sluggish or oscilla-tory response can arise in different operating regions. Gain scheduling is a potential solution. In processes with mul-tiple control objectives, the control strategy must further evaluate loop interactions to decide on the pairing of manipulated and controlled variables that minimize the effect of such interactions and hence, optimize controller’s performance and stability. Using the CADSIM Plus™ commercial simulation software, we developed a Jacobian sim-ulation module that enables automatic bumps on the manipulated variables to calculate process gains at different operating points. These gains can be used in controller tuning. The module also enables the control system designer to evaluate loop interactions in a multivariable control system by calculating the Relative Gain Array (RGA) matrix, of which the Jacobian is an essential part.
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Research context
OpenAlex topics: Advanced Control Systems Design · Advanced Control Systems Optimization · Real-time simulation and control systems
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References (12)
Show source-provided references
1. Sell, N.J., Process control fundamentals for the pulp & paper industry, TAPPI Press, Atlanta, Georgia, USA, 1995.
2. Bequette, B.W., Process control: modeling, design, and simulation, Prentice Hall, Upper Saddle River, New Jersey, USA, 2003.
3. Bialkowski, W.L., Pulp & Paper 72(12): 79 (1998).10.1016/S0269-915X(98)80050-5 ↗
4. Corripio, A.B., Tuning of industrial control systems, 2nd Edn., Instrument Society of America, Research Triangle Park, North Carolina, USA, 2001.
5. Gao, J. and Budman, H., J. Process Control, 15(7): 807 (2005).10.1016/j.jprocont.2005.02.003 ↗
6. Doyle, J., Kwatra, S., and Schwaber, S., Chem. Eng. Sci., 53(15): 2675 (1998).10.1016/S0009-2509(98)00089-X ↗
7. Edgar, T.F. and Lee, J. “Interaction measure for decentralized con-trol of multivariable processes,” Proceedings of 2002 American Control Conference Vol. I, American Automatic Control Council and IEEE, Anchorage, Alaska, p.454.10.1109/acc.2002.1024847 ↗
8. Wasik L., Nelson, D., and Mittet, G., TAPPI J., 83(3): 94(2000).
9. Laperrière, L. and Wasik, L., TAPPI J., 1(4): 7(2002).10.1016/S1365-6937(02)08027-9 ↗
10. Seborg, D.E., and Edgar, T.F., Process dynamics and control, 2nd Edn., John Wiley & Sons, Hoboken, New Jersey, USA, 2004.
11. Iverson, W., “Washing out energy costs,” http://www.automation-world.com/, 2004.
12. Smith, C.A. and Corripio, A.B., Principles and practice of automatic pro-cess control, 2nd Edn., John Wiley & Sons, Hoboken, New Jersey, USA, 1997.