Robust state estimation of feeding–blending systems in continuous pharmaceutical manufacturing
Abstract
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:
-
- Purdue Univ., West Lafayette, IN (United States). Davidson School of Chemical Engineering
- 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:
- 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)
- OSTI Identifier:
- 1469623
- Report Number(s):
- SAND-2018-9724J
Journal ID: ISSN 0263-8762; PII: S026387621830131X
- Grant/Contract Number:
- NA0003525; DHHS-FDA U01FD005535-01
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Chemical Engineering Research and Design
- Additional Journal Information:
- Journal Volume: 134; Journal ID: ISSN 0263-8762
- Publisher:
- Elsevier
- 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
Citation Formats
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., 2018.
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. https://doi.org/10.1016/j.cherd.2018.03.017
Liu, Jianfeng, Su, Qinglin, Moreno, Mariana, Laird, Carl, Nagy, Zoltan, and Reklaitis, Gintaras. Tue .
"Robust state estimation of feeding–blending systems in continuous pharmaceutical manufacturing". United States. https://doi.org/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 = {Tue Apr 03 00:00:00 EDT 2018},
month = {Tue Apr 03 00:00:00 EDT 2018}
}
Web of Science
Works referenced in this record:
A fast and versatile technique for constrained state estimation
journal, March 2011
- Abrol, Sidharth; Edgar, Thomas F.
- Journal of Process Control, Vol. 21, Issue 3
Data reconciliation and gross-error detection for dynamic systems
journal, October 1996
- Albuquerque, João S.; Biegler, Lorenz T.
- AIChE Journal, Vol. 42, Issue 10
Redescending estimators for data reconciliation and parameter estimation
journal, November 2001
- Arora, Nikhil; Biegler, Lorenz T.
- Computers & Chemical Engineering, Vol. 25, Issue 11-12
Handling Model Plant Mismatch in State Estimation Using a Multiple-Model-Based Approach
journal, April 2017
- Arulmaran, Kevin; Liu, Jinfeng
- Industrial & Engineering Chemistry Research, Vol. 56, Issue 18
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
journal, January 2002
- Arulampalam, M. S.; Maskell, S.; Gordon, N.
- IEEE Transactions on Signal Processing, Vol. 50, Issue 2
An overview of simultaneous strategies for dynamic optimization
journal, November 2007
- Biegler, Lorenz T.
- Chemical Engineering and Processing: Process Intensification, Vol. 46, Issue 11
Computational Approaches for Studying the Granular Dynamics of Continuous Blending Processes, 2 - Population Balance and Data-Based Methods: Computational Approaches for Studying the Granular Dynamics of Continuous …
journal, August 2011
- Boukouvala, Fani; Dubey, Atul; Vanarase, Aditya
- Macromolecular Materials and Engineering, Vol. 297, Issue 1
Simulation and data validation in multistage flash desalination plants
journal, March 1998
- Bourouis, Mahmoud; Pibouleau, Luc; Floquet, Pascal
- Desalination, Vol. 115, Issue 1
Optimal implementation of on-line optimization
journal, March 1998
- Chen, Xueyu; Pike, Ralph W.; Hertwig, Thomas A.
- Computers & Chemical Engineering, Vol. 22
DEM modelling of particulate flow in a screw feeder Model description
journal, January 2007
- Cleary, Paul W.
- Progress in Computational Fluid Dynamics, An International Journal, Vol. 7, Issue 2/3/4
Techniques for nonlinear least squares and robust regression
journal, January 1978
- Dennis, John E.; Welsch, Roy E.
- Communications in Statistics - Simulation and Computation, Vol. 7, Issue 4
Computational Approaches for Studying the Granular Dynamics of Continuous Blending Processes, 1 - DEM Based Methods
journal, March 2011
- Dubey, Atul; Sarkar, Avik; Ierapetritou, Marianthi
- Macromolecular Materials and Engineering, Vol. 296, Issue 3-4
Characterizing continuous powder mixing using residence time distribution
journal, February 2011
- Gao, Yijie; Vanarase, Aditya; Muzzio, Fernando
- Chemical Engineering Science, Vol. 66, Issue 3
Critical Evaluation of Extended Kalman Filtering and Moving-Horizon Estimation
journal, April 2005
- Haseltine, Eric L.; Rawlings, James B.
- Industrial & Engineering Chemistry Research, Vol. 44, Issue 8
Experiments and discrete element simulation of the dosing of cohesive powders in a simplified geometry
journal, January 2016
- Imole, Olukayode I.; Krijgsman, Dinant; Weinhart, Thomas
- Powder Technology, Vol. 287
Maximum likelihood data rectification: Steady-state systems: Maximum Likelihood Data Rectification: Steady-State Systems
journal, November 1995
- Johnston, Lloyd P. M.; Kramer, Mark A.
- AIChE Journal, Vol. 41, Issue 11
A new method for the nonlinear transformation of means and covariances in filters and estimators
journal, March 2000
- Julier, S.; Uhlmann, J.; Durrant-Whyte, H. F.
- IEEE Transactions on Automatic Control, Vol. 45, Issue 3
Robust Estimators for Data Reconciliation
journal, April 2015
- Llanos, Claudia E.; Sanchéz, Mabel C.; Maronna, Ricardo A.
- Industrial & Engineering Chemistry Research, Vol. 54, Issue 18
Flow Analysis and Markov Chain Modelling to Quantify the Agitation Effect in a Continuous Powder Mixer
journal, November 2006
- Marikh, K.; Berthiaux, H.; Mizonov, V.
- Chemical Engineering Research and Design, Vol. 84, Issue 11
On-line state estimation of nonlinear dynamic systems with gross errors
journal, November 2014
- Nicholson, Bethany; López-Negrete, Rodrigo; Biegler, Lorenz T.
- Computers & Chemical Engineering, Vol. 70
Theory and practice of simultaneous data reconciliation and gross error detection for chemical processes
journal, March 2004
- Özyurt, Derya B.; Pike, Ralph W.
- Computers & Chemical Engineering, Vol. 28, Issue 3
Moving Horizon Estimation for an Industrial gas Phase Polymerization Reactor
journal, January 2007
- Ramlal, Jasmeer; Allsford, Kenneth V.; Hedengren, John D.
- IFAC Proceedings Volumes, Vol. 40, Issue 12
Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations
journal, February 2003
- Rao, C. V.; Rawlings, J. B.; Mayne, D. Q.
- IEEE Transactions on Automatic Control, Vol. 48, Issue 2
Optimized continuous pharmaceutical manufacturing via model-predictive control
journal, August 2016
- Rehrl, Jakob; Kruisz, Julia; Sacher, Stephan
- International Journal of Pharmaceutics, Vol. 510, Issue 1
A multi-dimensional population balance model approach to continuous powder mixing processes
journal, January 2013
- Sen, Maitraye; Ramachandran, Rohit
- Advanced Powder Technology, Vol. 24, Issue 1
Mathematical Development and Comparison of a Hybrid PBM-DEM Description of a Continuous Powder Mixing Process
journal, December 2013
- Sen, Maitraye; Dubey, Atul; Singh, Ravendra
- Journal of Powder Technology, Vol. 2013
System-wide hybrid MPC–PID control of a continuous pharmaceutical tablet manufacturing process via direct compaction
journal, November 2013
- Singh, Ravendra; Ierapetritou, Marianthi; Ramachandran, Rohit
- European Journal of Pharmaceutics and Biopharmaceutics, Vol. 85, Issue 3
Simultaneous strategies for data reconciliation and gross error detection of nonlinear systems
journal, October 1991
- Tjoa, I. B.; Biegler, L. T.
- Computers & Chemical Engineering, Vol. 15, Issue 10
Recursive estimation in constrained nonlinear dynamical systems
journal, January 2005
- Vachhani, Pramod; Rengaswamy, Raghunathan; Gangwal, Vikrant
- AIChE Journal, Vol. 51, Issue 3
Robust and reliable estimation via Unscented Recursive Nonlinear Dynamic Data Reconciliation
journal, December 2006
- Vachhani, Pramod; Narasimhan, Shankar; Rengaswamy, Raghunathan
- Journal of Process Control, Vol. 16, Issue 10
On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
journal, April 2005
- Wächter, Andreas; Biegler, Lorenz T.
- Mathematical Programming, Vol. 106, Issue 1
Works referencing / citing this record:
Steady-State Data Reconciliation Framework for a Direct Continuous Tableting Line
journal, September 2018
- Moreno, Mariana; Liu, Jianfeng; Su, Qinglin
- Journal of Pharmaceutical Innovation, Vol. 14, Issue 3