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

Liu, Jianfeng; Su, Qinglin; Moreno, Mariana; Laird, Carl; Nagy, Zoltan; Reklaitis, Gintaras

doi:10.1016/j.cherd.2018.03.017

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

Journal Article · Tue Apr 03 00:00:00 EDT 2018 · Chemical Engineering Research and Design

DOI:https://doi.org/10.1016/j.cherd.2018.03.017· OSTI ID:1469623

Liu, Jianfeng ^[1]; Su, Qinglin ^[1]; Moreno, Mariana ^[1]; Laird, Carl ^[2]; Nagy, Zoltan ^[1]; Reklaitis, Gintaras ^[1]

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

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.

View Accepted Manuscript (DOE)

Research Organization:: Purdue Univ., West Lafayette, IN (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: Food and Drug Administration (FDA) (United States); USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 1469623

Report Number(s):: SAND--2018-9724J; PII: S026387621830131X

Journal Information:: Chemical Engineering Research and Design, Journal Name: Chemical Engineering Research and Design Vol. 134; ISSN 0263-8762

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
continuous pharmaceutical manufacturing
feeding-blending system
moving horizon state estimation
robust estimator

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

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