Fast stochastic model predictive control of end-to-end continuous pharmaceutical manufacturing 1 1Financial support from Novartis is acknowledged.

Paulson, Joel A.; Streif, Stefan; Findeisen, Rolf; Braatz, Richard D.; Mesbah, Ali

doi:10.1016/b978-0-444-63963-9.00014-2

Cited by 12 publications

(6 citation statements)

References 41 publications

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“…Accordingly, they considered the proposed control strategy to facilitate products' critical quality attributes (CQAs) in the presence of uncertainties and possible drifts in operating units. In, 247 they advanced by utilizing stochastic MPC for end‐to‐end continuous pharmaceutical manufacturing and considering uncertainties by a probability density function. Sacher et al 248 implemented MPC to synthesize drug substances continuously and validated the results experimentally.…”

Section: Applicationsmentioning

confidence: 99%

Control strategy for biopharmaceutical production by model predictive control

Eslami,

Jungbauer

2024

Biotechnology Progress

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The biopharmaceutical industry is rapidly advancing, driven by the need for cutting‐edge technologies to meet the growing demand for life‐saving treatments. In this context, Model Predictive Control (MPC) has emerged as a promising solution to address the complexity of modern biopharmaceutical production processes. Its ability to optimize operations and ensure consistent product yields has made it an attractive option for manufacturers in this sector. Furthermore, MPC's alignment with the Process Analytical Technology (PAT) initiative provides an additional layer of assurance, facilitating real‐time monitoring and enabling swift adjustments to maintain process integrity. This comprehensive review delves into the various applications of MPC, ranging from robust control to stochastic model predictive control, thereby equipping biotechnologists and process engineers with a powerful toolset. By harnessing the capabilities of MPC, as elucidated in this review, manufacturers can confidently navigate the intricate bioprocessing landscape and unlock this approach's full potential in their production processes.

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Section: Applicationsmentioning

confidence: 99%

Control strategy for biopharmaceutical production by model predictive control

Eslami,

Jungbauer

2024

Biotechnology Progress

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“…Biomanufacturing Process Optimization: Built on pre-specified process specifications, classic biomanufacturing control strategies tend to maintain CPPs within required ranges in order to guarantee product quality (Jiang and Braatz, 2016) through techniques such as feed-forward, feedback control (Hong et al, 2018;Kee et al, 2009;Blanchini et al, 2018), and model predictive control (Mesbah et al, 2017;Paulson et al, 2018;Kocijan et al, 2004;Lakerveld et al, 2013). However, the existing control strategies derived from PDE/ODE-based mechanistic models often ignore process inherent stochasticity and model uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

Opportunities of Hybrid Model-based Reinforcement Learning for Cell Therapy Manufacturing Process Control

Zheng¹,

Xie²,

Wang³

et al. 2022

Preprint

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Driven by the key challenges of cell therapy manufacturing, including high complexity, high uncertainty, and very limited process observations, we propose a hybrid model-based reinforcement learning (RL) to efficiently guide process control. We first create a probabilistic knowledge graph (KG) hybrid model characterizing the risk-and science-based understanding of biomanufacturing process mechanisms and quantifying inherent stochasticity, e.g., batch-to-batch variation. It can capture the key features, including nonlinear reactions, nonstationary dynamics, and partially observed state. This hybrid model can leverage existing mechanistic models and facilitate learning from heterogeneous process data. A computational sampling approach is used to generate posterior samples quantifying model uncertainty. Then, we introduce hybrid model-based Bayesian RL, accounting for both inherent stochasticity and model uncertainty, to guide optimal, robust, and interpretable dynamic decision making. Cell therapy manufacturing examples are used to empirically demonstrate that the proposed framework can outperform the classical deterministic mechanistic model assisted process optimization.

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“…However, controlling these processes is challenging and demands reliable monitoring of the process operation [156]. Moreover, most biochemical and chemical industrial processes are complex, multi-scale, high-dimensional systems that exhibit large nonlinearity and are affected by uncertainties [63,193,134]. The design of the control strategy must take all these aspects into account.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Process Control of (Bio)Chemical Processes

Himmel¹,

Matschek²,

Kok³

et al. 2023

Preprint

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The control of manufacturing processes must satisfy high quality and efficiency requirements while meeting safety requirements. A broad spectrum of monitoring and control strategies, such as model-and optimization-based controllers, are utilized to address these issues. Driven by rising demand for flexible yet energy and resource-efficient operations existing approaches are challenged due to high uncertainties and changes. Machine learning algorithms are becoming increasingly important in tackling these challenges, especially due to the growing amount of available data. The ability for automatic adaptation and learning from human operators offer new opportunities to increase efficiency yet provide flexible operation. Combining machine learning algorithms with safe or robust controls offers novel reliable operation methods. This chapter highlights ways to fuse machine learning and control for the safe and improved operation of chemical and biochemical processes. We outline and summarize both -learning models for control and learning the control components. We offer a general overview, including a literature review, to provide a guideline for utilizing machine learning techniques in control structures.

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Fast stochastic model predictive control of end-to-end continuous pharmaceutical manufacturing 1 1Financial support from Novartis is acknowledged.

Cited by 12 publications

References 41 publications

Control strategy for biopharmaceutical production by model predictive control

Control strategy for biopharmaceutical production by model predictive control

Opportunities of Hybrid Model-based Reinforcement Learning for Cell Therapy Manufacturing Process Control

Machine Learning for Process Control of (Bio)Chemical Processes

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