Model learning predictive control for batch processes:: A Reactive Batch Distillation Column Case Study

Alejandro Marquez Ruiz, M.A.C. Loonen, Bahadir Saltik, Leyla Ozkan (Corresponding author)

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

In this paper, we present the control of batch processes using Model Predictive Control (MPC) and iterative learning Control (ILC). Existing combinations of MPC and ILC are based on learning of the inputs of the process from previous batches for a fixed linear time-invariant model (LTI). However, batch processes are inherently time varying therefore, LTI models are limited in capturing the relevant dynamic behaviour for control. An attractive alternative is to use Linear Parameter Varying (LPV) models because of their ability to capture nonlinearities in the control of batch processes. Therefore, in this work we propose a novel method combining MPC and ILC based on LPV models and we call this method Model Learning Predictive Control (ML-MPC). Basically, the idea behind the method is to update the LPV model of the MPC iteratively, by using the repetitive behavior of the batch process. To this end, three different application-dependant options to estimate the parameters and disturbances of the model are proposed and are compared in simulation on a nonlinear batch reactor. Finally, the ML-MPC with one of the estimation methods is applied to an industrial Reactive Batch Distillation Column (RBD)
LanguageEnglish
Pages3737-13749
JournalIndustrial and Engineering Chemistry Research
Volume58
Issue number30
DOIs
StatePublished - 2019

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Distillation columns
Model predictive control
Batch reactors

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Marquez Ruiz, Alejandro ; Loonen, M.A.C. ; Saltik, Bahadir ; Ozkan, Leyla. / Model learning predictive control for batch processes: A Reactive Batch Distillation Column Case Study. In: Industrial and Engineering Chemistry Research. 2019 ; Vol. 58, No. 30. pp. 3737-13749
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title = "Model learning predictive control for batch processes:: A Reactive Batch Distillation Column Case Study",
abstract = "In this paper, we present the control of batch processes using Model Predictive Control (MPC) and iterative learning Control (ILC). Existing combinations of MPC and ILC are based on learning of the inputs of the process from previous batches for a fixed linear time-invariant model (LTI). However, batch processes are inherently time varying therefore, LTI models are limited in capturing the relevant dynamic behaviour for control. An attractive alternative is to use Linear Parameter Varying (LPV) models because of their ability to capture nonlinearities in the control of batch processes. Therefore, in this work we propose a novel method combining MPC and ILC based on LPV models and we call this method Model Learning Predictive Control (ML-MPC). Basically, the idea behind the method is to update the LPV model of the MPC iteratively, by using the repetitive behavior of the batch process. To this end, three different application-dependant options to estimate the parameters and disturbances of the model are proposed and are compared in simulation on a nonlinear batch reactor. Finally, the ML-MPC with one of the estimation methods is applied to an industrial Reactive Batch Distillation Column (RBD)",
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Marquez Ruiz, A, Loonen, MAC, Saltik, B & Ozkan, L 2019, 'Model learning predictive control for batch processes: A Reactive Batch Distillation Column Case Study' Industrial and Engineering Chemistry Research, vol. 58, no. 30, pp. 3737-13749. DOI: 10.1021/acs.iecr.8b06474

Model learning predictive control for batch processes: A Reactive Batch Distillation Column Case Study. / Marquez Ruiz, Alejandro; Loonen, M.A.C.; Saltik, Bahadir; Ozkan, Leyla (Corresponding author).

In: Industrial and Engineering Chemistry Research, Vol. 58, No. 30, 2019, p. 3737-13749.

Research output: Contribution to journal › Article › Academic › peer-review

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Marquez Ruiz A, Loonen MAC, Saltik B, Ozkan L. Model learning predictive control for batch processes: A Reactive Batch Distillation Column Case Study. Industrial and Engineering Chemistry Research. 2019;58(30):3737-13749. Available from, DOI: 10.1021/acs.iecr.8b06474

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