Optimized continuous pharmaceutical manufacturing via model-predictive control

Rehrl, Jakob; Kruisz, Julia; Sacher, Stephan; Khinast, Johannes G.; Horn, Martin

doi:10.1016/j.ijpharm.2016.06.024

Cited by 51 publications

(24 citation statements)

References 39 publications

(50 reference statements)

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“…Figure 6a shows the response of the n-CSTR model to a rectangular inlet condition with a length of one MRT. While appearing artificial, this rectangular inlet condition is a good model for set point changes occurring in a continuous manufacturing process [9,10,29]. Even though the change in the inlet condition (e.g., mass flow, concentration) persists for one mean residence time, the peak at the outlet is damped significantly compared to the height of the rectangle.…”

Section: Filtering Of Mass Flow Fluctuations In a Continuous Manufactmentioning

confidence: 99%

“…The individual RTDs are then chained together by convolution integrals, in order to calculate the RTD of the overall process. With this information it is possible to predict how long the material remains, on average, in the process (mean residence time, MRT), the response of the system to fluctuations in the material stream (e.g., feeder refills), and to develop process control strategies [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Explicit Residence Time Distribution of a Generalised Cascade of Continuous Stirred Tank Reactors for a Description of Short Recirculation Time (Bypassing)

2019

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The tanks-in-series model (TIS) is a popular model to describe the residence time distribution (RTD) of non-ideal continuously stirred tank reactors (CSTRs) with limited back-mixing. In this work, the TIS model was generalised to a cascade of n CSTRs with non-integer non-negative n. The resulting model describes non-ideal back-mixing with n > 1. However, the most interesting feature of the n-CSTR model is the ability to describe short recirculation times (bypassing) with n < 1 without the need of complex reactor networks. The n-CSTR model is the only model that connects the three fundamental RTDs occurring in reactor modelling by variation of a single shape parameter n: The unit impulse at n→0, the exponential RTD of an ideal CSTR at n = 1, and the delayed impulse of an ideal plug flow reactor at n→∞. The n-CSTR model can be used as a stand-alone model or as part of a reactor network. The bypassing material fraction for the regime n < 1 was analysed. Finally, a Fourier analysis of the n-CSTR was performed to predict the ability of a unit operation to filter out upstream fluctuations and to model the response to upstream set point changes.

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Section: Filtering Of Mass Flow Fluctuations In a Continuous Manufactmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Explicit Residence Time Distribution of a Generalised Cascade of Continuous Stirred Tank Reactors for a Description of Short Recirculation Time (Bypassing)

2019

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“…It shows by monitoring CQAs in real time, Critical Process Parameters (CPPs) can be actively manipulated to enable more robust and flexible process operation via feedback control. The advantages of MPC compared to conventional Proportional-Integral-Derivative (PID) controllers are highlighted in [20] via comparison of the control strategies to a feeding blending unit used in continuous pharmaceutical manufacturing. In addition to the work done on MPC, RNNs had been proposed to be used in conjunction with MPC in previous studies.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network-Based Model Predictive Control for Continuous Pharmaceutical Manufacturing

et al. 2018

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The pharmaceutical industry has witnessed exponential growth in transforming operations towards continuous manufacturing to effectively achieve increased profitability, reduced waste, and extended product range. Model Predictive Control (MPC) can be applied for enabling this vision, in providing superior regulation of critical quality attributes. For MPC, obtaining a workable model is of fundamental importance, especially in the presence of complex reaction kinetics and process dynamics. Whilst physics-based models are desirable, it is not always practical to obtain one effective and fit-for-purpose model. Instead, within industry, datadriven system-identification approaches have been found to be useful and widely deployed in MPC solutions. In this work, we demonstrated the applicability of Recurrent Neural Networks (RNNs) for MPC applications in continuous pharmaceutical manufacturing. We have shown that RNNs are especially well-suited for modeling dynamical systems due to their mathematical structure and satisfactory closedloop control performance can be yielded for MPC in continuous pharmaceutical manufacturing.

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“…A current trend in pharmaceutical industry is toward continuous manufacturing to improve efficiency . Continuous manufacturing offers opportunities to exploit various inherent advantages compared to batch‐wise operation such as easier control, smaller inventories, reduced footprint, and reduced material and energy usage . In general, solvent selection and usage is important for the manufacture of pharmaceuticals because of its large impact on product quality, economics, and sustainability .…”

Section: Introductionmentioning

confidence: 99%

Integrated solvent and process design for continuous crystallization and solvent recycling using PC‐SAFT

Wang

Lakerveld

2017

AIChE Journal

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Solvent usage is a major source of environmental waste in pharmaceutical industry. The current paradigm shift toward continuous manufacturing in pharmaceutical industry has renewed the interest in continuous crystallization, which offers the prospect of easy solvent recycling. However, the selection of solvents for an integrated crystallization processes is nontrivial due to the likely trade‐off between optimal solvent properties for crystallization and solvent separation and recycling. A systematic approach for the simultaneous optimization of process conditions and solvent selection for continuous crystallization including solvent recycling is presented. A unified perturbed‐chain statistical associating fluid theory model framework is applied to predict thermodynamic properties related to solubility and vapor‐liquid equilibrium, which is integrated with a process model. A continuous mapping procedure is adopted to solve the optimization problem effectively. A case study based on continuous antisolvent crystallization of paracetamol with solvent separation via flash demonstrates the approach. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1205–1216, 2018

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Optimized continuous pharmaceutical manufacturing via model-predictive control

Cited by 51 publications

References 39 publications

Explicit Residence Time Distribution of a Generalised Cascade of Continuous Stirred Tank Reactors for a Description of Short Recirculation Time (Bypassing)

Explicit Residence Time Distribution of a Generalised Cascade of Continuous Stirred Tank Reactors for a Description of Short Recirculation Time (Bypassing)

Recurrent Neural Network-Based Model Predictive Control for Continuous Pharmaceutical Manufacturing

Integrated solvent and process design for continuous crystallization and solvent recycling using PC‐SAFT

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