### Improved Multi-model Predictive Control to Reject Very Large Disturbances on a Distillation Column

*Abdul Wahid, Arshad Ahmad*

**Abstract**: A multi model

predictive control and proportional-integral controller switching (MMPCPIS)

approach is proposed to control a nonlinear distillation column. The study was

implemented on a multivariable nonlinear distillation column (Column A). The

setpoint tracking and disturbance rejection performances of the proposed

MMPCPIS were evaluated and compared to a proportional-integral

(PI) controller and the hybrid controller (HC). MMPCPIS developed to overcome the

HC’s limitation when dealing with very large disturbance changes (50%). MMPCPIS provided improvements by 27% and 31%

of the ISE (integral of square error) for feed flow rate and

feed composition disturbance changes, respectively, compared

with the PI controller, and 24% and 54% of the ISE for feed flow rate and feed

composition disturbance

change, respectively, compared with HC.

**Keywords**: Distillation; Multi-model; Predictive; Very large disturbance

Full PDF Download

#### References

Anjum, A., Ilyas, M.U., 2013. Activity Recognition using Smartphone Sensors. First Workshop on People Centric Sensing and Communications. Las Vegas

Allgöwer, F., Zheng, A., 2012. Nonlinear Model Predictive Control. Springer Basel AG

Andrikopoulos, G., Nikolakopoulos, G., Manesis, S., 2013. Pneumatic Artificial Muscles: A Switching Model Predictive Control Approach. Control Engineering Practice, Volume 21, pp. 1653–1664

Åström, K.J., 2002. Control System Design. University of California

Bachnas, A.A., Tóth, R., Ludlage, J.H.A., Mesbah, A., 2014. A Review on Data-driven Linear Parameter-varying Modeling Approaches: A High-purity Distillation Column Case Study. Journal of Process Control, Volume 24, pp.

–285

Chan, H.-C., Yu, C.-C., 1995. Autotuning of Gain-scheduled pH Control: An Experimental Study. Ind. Eng. Chem. Res., Volume 34, pp. 1718–1729

Dougherty, D., Cooper, D., 2003. A Practical Multiple Model Adaptive Strategy for Single-loop MPC. Control Engineering Practice, Volume 11, pp. 141–159

Gustafsson, T.K., Skrifvars, B.O., Katarina, V., Sandstram, Waller, K.V., 1995. Modeling of pH for Control. Ind. Eng. Chem. Res., Volume 34, pp. 820–827

Lundstrom, P., Lee, J.H., Morari, M., Skogestad, S., 1995. Limitations of Dynamic Matrix Control. Computers and Chemical Engineering, Volume 19(4), pp. 409–421

Luyben, W.L., 2006. Distillation Design and Control Using AspenTM Simulation. John Wiley & Sons, Inc., Hoboken, New Jersey

Luyben, W.L., Chien, I-Lung., 2010. Design and Control of Distillation Systems for Separating Azeotropes. John Wiley & Sons, Inc., Hoboken, New Jersey

Marlin, T., 2000. Process Control: Designing Processes and Control Systems for Dynamic Performance. 2nd Edition, McGraw-Hill, New York

Mathur, Umesh, Rounding, R.D., Webb, D.R., Conroy, R.J., 2008. Use

Model-predictive Control to Improve Distillation Operations. Chemical Engineering Progress, Volume 104(1), pp. 35–41

Ogunnaike, B.A., Lemaire, J.P., Morari, M., Ray, W.H., 1983. Advanced Multivariable Control of a Pilot-plant Distillation Column. AlChE Journal, Volume 29(4), pp. 632–640

Pearson, R.K., 2006. Nonlinear Empirical Modeling Techniques. Computers and Chemical Engineering, Volume 30, pp. 1514–1528

Qin, S.J., Badgwell, T.A., 2003. A Survey of Industrial Model Predictive Control Technology. Control Engineering Practice, Volume 11, pp. 733–764

Skogestad, S., 1997. Dynamics and Control of Distillation Columns – A Critical Survey. Modeling, Identification and Control, Volume 18(3), pp. 177–217

Wahid, A., Ahmad, A., 2015. Min-max Controller Output Configuration to Improve Multi-model Predictive Control when Dealing with Disturbance Rejection. International Journal of Technology, Volume 6(3), pp. 504–515