Data-Driven Model Predictive Quality Control of Batch Processes

  • Prashant MhaskarEmail author
  • Abhinav Garg
  • Brandon Corbett
Part of the Advances in Industrial Control book series (AIC)


This Chapter considers the problem of driving a batch process to a specified product quality using data-driven model predictive control (MPC). To address the problem of unavailability of online quality measurements, an inferential quality model, which relates the process conditions over the entire batch duration to the final quality, is required. The accuracy of this type of quality model, however, is sensitive to the prediction of the future batch behavior until batch termination. In this work, we handle this “missing data" problem by integrating a previously developed data-driven modeling methodology, which combines multiple local linear models with an appropriate weighting function to describe nonlinearities, with the inferential model in a MPC framework. The key feature of this approach is that the causality and nonlinear relationships between the future inputs and outputs are accounted for in predicting the final quality and computing the manipulated input trajectory. The efficacy of the proposed predictive control design is illustrated via closed-loop simulations of a nylon-6,6 batch polymerization process with limited measurements.


  1. 1.
    Geladi, P., Kowalski, B.: Partial least-squares regression: a tutorial. Anal. Chim. Acta 185, 1–17 (1986)CrossRefGoogle Scholar
  2. 2.
    Wold, S., Geladi, P., Esbensen, K., Ahman, J.: Multi-way principal components-and PLS-analysis. J. Chemom. 1(1), 41–56 (1987)CrossRefGoogle Scholar
  3. 3.
    Nomikos, P., MacGregor, J.F.: Monitoring batch processes using multiway principal component analysis. AIChE J. 40(8), 1361–1375 (1994)CrossRefGoogle Scholar
  4. 4.
    Flores-Cerrillo, J., MacGregor, J.F.: Within-batch and batch-to-batch inferential-adaptive control of semibatch reactors: a partial least squares approach. Ind. Eng. Chem. Res. 42(14), 3334–3345 (2003)CrossRefGoogle Scholar
  5. 5.
    Undey, C., Cinar, A.: Statistical monitoring of multiphase, multistage batch processes. IEEE Control Syst. Mag. 22, 40–52 (2002)Google Scholar
  6. 6.
    Ng, Y.S., Srinivasan, R.: An adjoined multi-model approach for monitoring batch and transient operations. Comput. Chem. Eng. 33(4), 887–902 (2009)CrossRefGoogle Scholar
  7. 7.
    Flores-Cerrillo, J., MacGregor, J.F.: Control of particle size distributions in emulsion semibatch polymerization using mid-course correction policies. Ind. Eng. Chem. Res. 41(7), 1805–1814 (2002)CrossRefGoogle Scholar
  8. 8.
    Yabuki, Y., MacGregor, J.F.: Product quality control in semibatch reactors using midcourse correction policies. Ind. Eng. Chem. Res. 36(4), 1268–1275 (1997)CrossRefGoogle Scholar
  9. 9.
    Wang, D., Srinivasan, R.: Multi-model based real-time final product quality control strategy for batch processes. Comput. Chem. Eng. 33(5), 992–1003 (2009)CrossRefGoogle Scholar
  10. 10.
    Gunther, J.C., Conner, J.S., Seborg, D.E.: Process monitoring and quality variable prediction utilizing PLS in industrial fed-batch cell culture. J. Process Control 19(5), 914–921 (2009)CrossRefGoogle Scholar
  11. 11.
    Lu, N., Gao, F.: Stage-based process analysis and quality prediction for batch processes. Ind. Eng. Chem. Res. 44(10), 3547–3555 (2005)CrossRefGoogle Scholar
  12. 12.
    Zhao, C., Wang, F., Mao, Z., Lu, N., Jia, M.: Quality prediction based on phase-specific average trajectory for batch processes. AIChE J. 54(3), 693–705 (2008)CrossRefGoogle Scholar
  13. 13.
    Aumi, S., Mhaskar, P.: Integrating data-based modelling and nonlinear control tools for batch process control. AIChE J. 58, 2105–2119 (2012)CrossRefGoogle Scholar
  14. 14.
    Aumi, S., Corbett, B., Mhaskar, P., Clarke-Pringle, T.: Data-based modelling and control of nylon-6, 6 batch polymerization. IEEE Trans. Control Syst. Technol. (2011) (in press)Google Scholar
  15. 15.
    Kourti, T., Lee, J., Macgregor, J.F.: Experiences with industrial applications of projection methods for multivariate statistical process control. Comput. Chem. Eng. 20, 745–750 (1996)CrossRefGoogle Scholar
  16. 16.
    Kassidas, A., MacGregor, J.F., Taylor, P.A.: Synchronization of batch trajectories using dynamic time warping. AIChE J. 44(4), 864–875 (1998)CrossRefGoogle Scholar
  17. 17.
    Undey, C., Williams, B.A., Cinar, A.: Monitoring of batch pharmaceutical fermentations: data synchronization, landmark alignment, and real-time monitoring. In: Proceedings of the IFAC World Congress on Automatic Control, vol. 15. Barcelona (2002)Google Scholar
  18. 18.
    Huang, B., Ding, X.S., Qin, S.J.: Closed-loop subspace identification: an orthogonal projection approach. J. Process Control 15(1), 53–66 (2005)CrossRefGoogle Scholar
  19. 19.
    Patel, S., Yelchuru, R., Ryaliand, S., Gudi, R.D.: Discriminatory learning based performance monitoring of batch processes. In: Proceedings of the American Control Conference, pp. 2552–2557 (2011)Google Scholar
  20. 20.
    Bezdek, J.: A convergence theorem for the fuzzy isodata clustering algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 2(1), 1–8 (1980)CrossRefGoogle Scholar
  21. 21.
    Frigui, H., Krishnapuram, R.: Clustering by competitive agglomeration. Pattern Recognit. 30(7), 1109–1119 (1997)CrossRefGoogle Scholar
  22. 22.
    Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13, 841–847 (1991)CrossRefGoogle Scholar
  23. 23.
    Russell, S.A., Robertson, D.G., Lee, J.H., Ogunnaike, B.A.: Control of product quality for batch nylon-6,6 autoclaves. Chem. Eng. Sci. 53(21), 3685–3702 (1998)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Prashant Mhaskar
    • 1
    Email author
  • Abhinav Garg
    • 1
  • Brandon Corbett
    • 1
  1. 1.Department of Chemical EngineeringMcMaster UniversityHamiltonCanada

Personalised recommendations