Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 3737-13749 |
| Journal | Industrial and Engineering Chemistry Research |
| Volume | 58 |
| Issue number | 30 |
| DOIs | |
| Publication status | Published - 2019 |
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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
TY - JOUR
T1 - Model learning predictive control for batch processes:
T2 - A Reactive Batch Distillation Column Case Study
AU - Marquez Ruiz, Alejandro
AU - Loonen, M.A.C.
AU - Saltik, Bahadir
AU - Ozkan, Leyla
PY - 2019
Y1 - 2019
N2 - 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)
AB - 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)
UR - http://www.scopus.com/inward/record.url?scp=85065821431&partnerID=8YFLogxK
U2 - 10.1021/acs.iecr.8b06474
DO - 10.1021/acs.iecr.8b06474
M3 - Article
AN - SCOPUS:85065821431
VL - 58
SP - 3737
EP - 13749
JO - Industrial and Engineering Chemistry Research
JF - Industrial and Engineering Chemistry Research
SN - 0888-5885
IS - 30
ER -