Modelling and control of combined cooling and antisolvent crystallization processes

Although for decades nearly all pharmaceuticals have been purified by crystallization, there have been a disproportionate number of problems associated with the operation and control of these processes. This paper provides an overview of the recent advances in model-based and model-free (direct design) approaches to control the crystallization of pharmaceuticals, treating both antisolvent and cooling crystallization. A model-based combined technique which simultaneously controls the antisolvent addition rate and the cooling profile is presented. A population balance model of the combined cooling-antisolvent addition system is developed and a moments model is used in optimal control strategies with various objective functions. The simulation and experimental results show the advantages of the combined approach

Averaging level control to reduce off-spec material in a continuous pharmaceutical pilot plant

The judicious use of buffering capacity is important in the development of future continuous pharmaceutical manufacturing processes. The potential benefits are investigated of using optimal-averaging level control for tanks that have buffering capacity for a section of a continuous pharmaceutical pilot plant involving two crystallizers, a combined filtration and washing stage and a buffer tank. A closed-loop dynamic model is utilized to represent the experimental operation, with the relevant model parameters and initial conditions estimated from experimental data that contained a significant disturbance and a change in setpoint of a concentration control loop. The performance of conventional proportional-integral (PI) level controllers is compared with optimal-averaging level controllers. The aim is to reduce the production of off-spec material in a tubular reactor by minimizing the variations in the outlet flow rate of its upstream buffer tank. The results show a distinct difference in behavior, with the optimal-averaging level controllers strongly outperforming the PI controllers. In general, the results stress the importance of dynamic process modeling for the design of future continuous pharmaceutical processes

Mathematical modeling and design of layer crystallization in a concentric annulus with and without recirculation

A solution layer crystallization process in a concentric annulus is presented that removes the need for filtration. A dynamic model for layer crystallization with and without a recirculation loop is developed in the form of coupled partial differential equations describing the effects of mass transfer, heat transfer, and crystallization kinetics. The model predicts the variation of the temperature, concentration, and dynamic crystal thickness along the pipe length, and the concentration and temperature along the pipe radius. The model predictions are shown to closely track experimental data that were not used in the model's construction, and also compared to an analytical solution that can be used for quickly obtaining rough estimates when there is no recirculation loop. The model can be used to optimize product yield and crystal layer thickness uniformity, with constraints on the supersaturation to avoid bulk nucleation by adjusting cooling temperatures in the core and jacket. © 2013 American Institute of Chemical Engineers

Mathematical modeling and design of layer crystallization in a concentric annulus with and without recirculation [Abstract]

Mathematical modeling and design of layer crystallization in a concentric annulus with and without recirculation [Abstract

Distributional uncertainty analysis using polynomial chaos expansions

Abstract—A computationally efficient approach is presented that quantifies the influence of parameter uncertainties on the states and outputs of finite-time control trajectories for nonlinear systems, based on the approximate representation of the model via polynomial chaos expansion. The approach is suitable for studying the uncertainty propagation in open-loop or closed-loop systems. A quantitative and qualitative assessment of the method is performed in comparison to the Monte Carlo simulation technique that uses the nonlinear model for uncertainty propagation. The polynomial chaos expansion-based approach is characterized by a significantly lower computational burden compared to Monte Carlo approaches, while providing a good approximation of the shape of the uncertainty distribution of the process outputs. The techniques are applied to the crystallization of an inorganic chemical with uncertainties in the nucleation and growth parameters

Kernel density reconstruction of the posterior PDFs from the PF at (a) 100 s, (b) 500 s, (c) 1000 s, and (d) 1500 s.

<p>Kernel density reconstruction of the posterior PDFs from the PF at (a) 100 s, (b) 500 s, (c) 1000 s, and (d) 1500 s.</p

An example of a SWNT-based sensor array system.

<p>An example of a SWNT-based sensor array system.</p

Averaged RMSEs of estimates from the PF and KF and associated computation time (in seconds) with increasing number of sensors.

<p>¹The values are averaged over 100 runs.</p><p>²The computation time was recorded in seconds using a workstation with 3.40 GHz CPU and 8GB RAM.</p><p>Averaged RMSEs of estimates from the PF and KF and associated computation time (in seconds) with increasing number of sensors.</p

Variation of the concentration of signal molecules with different cell states.

<p>Variation of the concentration of signal molecules with different cell states.</p

Averaged RMSEs and of the estimates from PF-MCMC and KF-GPB2 and associated computation time for increasing number of sensors.

<p>¹The values are averaged over 100 runs.</p><p>²The computation time was recorded in seconds using a workstation with 3.40 GHz CPU, 8GB RAM.</p><p>Averaged RMSEs and of the estimates from PF-MCMC and KF-GPB2 and associated computation time for increasing number of sensors.</p