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Title: Robust state estimation of feeding–blending systems in continuous pharmaceutical manufacturing

State estimation is a fundamental part of monitoring, control, and real-time optimization in continuous pharmaceutical manufacturing. For nonlinear dynamic systems with hard constraints, moving horizon estimation (MHE) can estimate the current state by solving a well-defined optimization problem where process complexities are explicitly considered as constraints. Traditional MHE techniques assume random measurement noise governed by some normal distributions. However, state estimates can be unreliable if noise is not normally distributed or measurements are contaminated with gross or systematic errors. In this paper, to improve the accuracy and robustness of state estimation, we incorporate robust estimators within the standard MHE skeleton, leading to an extended MHE framework. The proposed MHE approach is implemented on two pharmaceutical continuous feeding–blending system (FBS) configurations which include loss-in-weight (LIW) feeders and continuous blenders. Numerical results show that our MHE approach is robust to gross errors and can provide reliable state estimates when measurements are contaminated with outliers and drifts. Finally and moreover, the efficient solution of the MHE realized in this work, suggests feasible application of on-line state estimation on more complex continuous pharmaceutical processes.
Authors:
 [1] ;  [1] ;  [1] ;  [2] ;  [1] ;  [1]
  1. Purdue Univ., West Lafayette, IN (United States). Davidson School of Chemical Engineering
  2. Purdue Univ., West Lafayette, IN (United States). Davidson School of Chemical Engineering; Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Center for Computing Research
Publication Date:
Report Number(s):
SAND-2018-9724J
Journal ID: ISSN 0263-8762; PII: S026387621830131X
Grant/Contract Number:
NA0003525; DHHS-FDA U01FD005535-01
Type:
Accepted Manuscript
Journal Name:
Chemical Engineering Research and Design
Additional Journal Information:
Journal Volume: 134; Journal ID: ISSN 0263-8762
Publisher:
Elsevier
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Purdue Univ., West Lafayette, IN (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA); Food and Drug Administration (FDA) (United States)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; moving horizon state estimation; robust estimator; continuous pharmaceutical manufacturing; feeding-blending system
OSTI Identifier:
1469623

Liu, Jianfeng, Su, Qinglin, Moreno, Mariana, Laird, Carl, Nagy, Zoltan, and Reklaitis, Gintaras. Robust state estimation of feeding–blending systems in continuous pharmaceutical manufacturing. United States: N. p., Web. doi:10.1016/j.cherd.2018.03.017.
Liu, Jianfeng, Su, Qinglin, Moreno, Mariana, Laird, Carl, Nagy, Zoltan, & Reklaitis, Gintaras. Robust state estimation of feeding–blending systems in continuous pharmaceutical manufacturing. United States. doi:10.1016/j.cherd.2018.03.017.
Liu, Jianfeng, Su, Qinglin, Moreno, Mariana, Laird, Carl, Nagy, Zoltan, and Reklaitis, Gintaras. 2018. "Robust state estimation of feeding–blending systems in continuous pharmaceutical manufacturing". United States. doi:10.1016/j.cherd.2018.03.017. https://www.osti.gov/servlets/purl/1469623.
@article{osti_1469623,
title = {Robust state estimation of feeding–blending systems in continuous pharmaceutical manufacturing},
author = {Liu, Jianfeng and Su, Qinglin and Moreno, Mariana and Laird, Carl and Nagy, Zoltan and Reklaitis, Gintaras},
abstractNote = {State estimation is a fundamental part of monitoring, control, and real-time optimization in continuous pharmaceutical manufacturing. For nonlinear dynamic systems with hard constraints, moving horizon estimation (MHE) can estimate the current state by solving a well-defined optimization problem where process complexities are explicitly considered as constraints. Traditional MHE techniques assume random measurement noise governed by some normal distributions. However, state estimates can be unreliable if noise is not normally distributed or measurements are contaminated with gross or systematic errors. In this paper, to improve the accuracy and robustness of state estimation, we incorporate robust estimators within the standard MHE skeleton, leading to an extended MHE framework. The proposed MHE approach is implemented on two pharmaceutical continuous feeding–blending system (FBS) configurations which include loss-in-weight (LIW) feeders and continuous blenders. Numerical results show that our MHE approach is robust to gross errors and can provide reliable state estimates when measurements are contaminated with outliers and drifts. Finally and moreover, the efficient solution of the MHE realized in this work, suggests feasible application of on-line state estimation on more complex continuous pharmaceutical processes.},
doi = {10.1016/j.cherd.2018.03.017},
journal = {Chemical Engineering Research and Design},
number = ,
volume = 134,
place = {United States},
year = {2018},
month = {4}
}