Encyclopedia of Systems and Control

Living Edition
| Editors: John Baillieul, Tariq Samad

Control and Optimization of Batch Processes

  • Dominique Bonvin
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4471-5102-9_251-1


A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent “time” variables, namely, the run time during a batch and the batch counter. Accordingly, the control and optimization objectives can be defined for a given batch or over several batches. This entry describes the various control and optimization strategies available for the operation of batch processes. These include conventional feedback control, predictive control, iterative learning control, and run-to-run control on the one hand and model-based repeated optimization and model-free self-optimizing schemes on the other.


Batch control Batch process optimization Iterative learning control Run-to-run control Dynamic optimization Run-to-run optimization 
This is a preview of subscription content, log in to check access.


  1. Abel O, Helbig A, Marquardt W, Zwick H, Daszkowski T (2000) Productivity optimization of an industrial semi-batch polymerization reactor under safety constraints. J Process Control 10(4):351–362CrossRefGoogle Scholar
  2. Bonvin D (1998) Optimal operation of batch reactors – a personal view. J Process Control 8(5–6):355–368CrossRefGoogle Scholar
  3. Bonvin D, Srinivasan B, Hunkeler D (2006) Control and optimization of batch processes: improvement of process operation in the production of specialty chemicals. IEEE Control Syst Mag 26(6):34–45CrossRefGoogle Scholar
  4. Francois G, Srinivasan B, Bonvin D (2005) Use of measurements for enforcing the necessary conditions of optimality in the presence of constraints and uncertainty. J Process Control 15(6):701–712CrossRefGoogle Scholar
  5. Moore KL (1993) Iterative learning control for deterministic systems. Advances in industrial control. Springer, LondonCrossRefMATHGoogle Scholar
  6. Nagy ZK, Braatz RD (2003) Robust nonlinear model predictive control of batch processes. AIChE J 49(7):1776–1786CrossRefGoogle Scholar
  7. Rahman S, Palanki S (1996) State feedback synthesis for on-line optimization in the presence of measurable disturbances. AIChE J 42:2869–2882CrossRefGoogle Scholar
  8. Rastogi A, Fotopoulos J, Georgakis C, Stenger HG (1992) The identification of kinetic expressions and the evolutionary optimization of specialty chemical batch reactors using tendency models. Chem Eng Sci 47(9–11):2487–2492CrossRefGoogle Scholar
  9. Seborg DE, Edgar TF, Mellichamp DA (2004) Process dynamics and control. Wiley, New YorkGoogle Scholar
  10. Srinivasan B, Bonvin D (2007a) Controllability and stability of repetitive batch processes. J Process Control 17(3):285–295CrossRefGoogle Scholar
  11. Srinivasan B, Bonvin D (2007b) Real-time optimization of batch processes by tracking the necessary conditions of optimality. Ind Eng Chem Res 46(2):492–504CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Laboratoire d’AutomatiqueÉcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland