Anwesha Chaudhury

Model-Based Control-Loop Performance of a Continuous Direct Compaction Process

This study is concerned with enhanced model-based control of a continuous direct compression pharmaceutical process. The control-loop performance is assessed in silico and results obtained will be incorporated into the pilot plant facility of the continuous direct compaction process at the NSF Engineering Research Center of Rutgers University. The models used in the study are obtained via system identification from a combination of first principles-based dynamic models, experimental data, and/or literature data. The main objective of the paper is to formulate an effective control strategy at the basic/regulatory level, for the integrated continuous operation of the direct compaction process, and to maintain the process at the desired set-points, taking into account the multivariable process interactions and disturbances. Simulations show that that at very mild interactions, the proposed regulatory control strategy is able to maintain set-points at desired values. However, at moderate to high process interactions, oscillatory behavior of controlled variables is seen. The presence of disturbances also resulted in poor control-loop performance. Results also lend credence to the development of advanced control strategies in such scenarios and will be addressed in future work. Optimal control tuning parameters are obtained from a derivative-free optimization algorithm.

Computer-Aided Flowsheet Simulation of a Pharmaceutical Tablet Manufacturing Process Incorporating Wet Granulation

In this work, a dynamic flowsheet model for the production of pharmaceutical tablets through a continuous wet granulation process is developed. The unit operation models which are integrated to compose the process line form a hybrid configuration which is comprised of a combination of mechanistic models, population balance models, and empirical correlations, based on the currently available process knowledge for each individual component. The main objective of this study is to provide guidance in terms of the necessary steps which are required in order to move from the unit operation level to the simulation of an integrated continuous plant operation. Through this approach, not only significant process conditions for each individual process are identified but also crucial interconnecting parameters which affect critical material properties of the processed powder stream are distinguished. Through the integration of the dynamic flowsheet with a final component of tablet dissolution, the connection of the processing history of a set of powders which undergo wet granulation and are contained in each produced tablet to the release rate of the pharmaceutical ingredient is enabled. The developed flowsheet is used for the simulation of different operating scenarios and disturbances which are often encountered during operation for the assessment of their effects towards critical material attributes, product properties, and the operation of further downstream processes. Simulation results demonstrate that granulation and milling which control the particle size distribution of the processed powder mixture highly affect the hardness and dissolution of the produced tablets.

A combined experimental and modeling approach to study the effects of high-shear wet granulation process parameters on granule characteristics

The purpose of the current work is to study the effects of high-shear wet granulation process parameters on granule characteristics using both experimental and modeling techniques. A full factorial design of experiments was conducted on three process parameters: water amount, impeller speed and wet massing time. Statistical analysis showed that the water amount has the largest impact on the granule characteristics, and that the effect of other process variables was more pronounced at higher water amount. At high water amounts, an increase in impeller speed and/or wet massing time showed a decrease in granule porosity and compactability. A strong correlation between granule porosity and compactability was observed. A three-dimensional population balance model which considers agglomeration and consolidation was employed to model the granulation process. The model was calibrated using the particle size distribution from an experimental batch to ensure a good match between the simulated and experimental particle size distribution. The particle size distribution of three other batches were predicted, each of which was manufactured under different process parameters (water amount, impeller speed and wet massing time). The model was able to capture and predict successfully the shifts in granule particle size distribution with changes in these process parameters.

Multi-scale flowsheet simulation of an integrated continuous purification–downstream pharmaceutical manufacturing process

Properties of active pharmaceutical ingredients influence the critical quality attributes (CQAs) of final solid dosage forms (e.g. tablets). In the last decade, continuous manufacturing has been shown to be a promising alternative to batch processing in the pharmaceutical industry. Therefore, a quantitative model-based analysis of the influence of upstream API properties on downstream processing quality metrics will lead to enhanced QbD in pharmaceutical drug product manufacturing (Benyahia et al., 2012). In this study, a dynamic flowsheet simulation of an integrated API purification step (crystallization), followed by filtration and drying, with a downstream process (powder mixing) is presented. Results show that the temperature profile of a cooling crystallization process influences the crystal size distribution which in turn impacts the RSD and API concentration of the powder mixing process, which in turn has a direct effect on tablet properties (Boukouvala et al., 2012). A hybrid PBM-DEM model is also presented to demonstrate the coupling of particle-scale information with process-scale information leading to enhanced elucidation of the dynamics of the overall flowsheet simulation.

Closed-Loop Feedback Control of a Continuous Pharmaceutical Tablet Manufacturing Process via Wet Granulation

The wet granulation route of tablet manufacturing in a pharmaceutical manufacturing process is very common due to its numerous processing advantages such as enhanced powder flow and decreased segregation. However, this route is still operated in batch mode with little (if any) usage of an automatic control system. Tablet manufacturing via wet granulation, integrated with online/inline real time sensors and coupled with an automatic feedback control system, is highly desired for the transition of the pharmaceutical industry toward quality by design as opposed to quality by testing. In this manuscript, an efficient, plant-wide control strategy for an integrated continuous pharmaceutical tablet manufacturing process via wet granulation has been designed in silico. An effective controller parameter tuning strategy involving an integral of time absolute error method coupled with an optimization strategy has been used. The designed control system has been implemented in a flowsheet model that was simulated in gPROMS (Process System Enterprise) to evaluate its performance. The ability of the control system to reject the unknown disturbances and track the set point has been analyzed. Advanced techniques such as anti-windup and scale-up factor have been used to improve controller performance. Results demonstrate enhanced achievement of critical quality attributes under closed-loop operation, thus illustrating the potential of closed-loop feedback control in improving pharmaceutical tablet manufacturing operations.

Population Balance Model Development, Validation, and Prediction of CQAs of a High-Shear Wet Granulation Process: Towards QbD in Drug Product Pharmaceutical Manufacturing

This paper focuses on the predictive model development for a pharmaceutically relevant model granulation process. A population balance modeling (PBM) framework has been employed for modeling purposes which is then utilized to obtain accurate predictions of the process. The model is aligned to adequately describe the high-shear mode of granulation operation in a batch process. The model is calibrated using the particle swarm algorithm (PSA) in the form of a multiobjective optimization problem. The multiobjective optimization problem was implemented based on the ε-constraint method which involves the handling of multiple cost functions in the form of constraints with the minimization of one primary objective function from the entire set of cost functions. The resultant solutions obtained from the model are Pareto optimal. The effects of the impeller speed, liquid-to-solid ratio, and wet massing time on the particle size distributions were characterized, and predicted size distributions were in agreement with experimental results. The predictive model framework lends itself to the quality by design (QbD) initiative undertaken by the US Food and Drug Administration (US FDA).

A computationally efficient technique for the solution of multi-dimensional PBMs of granulation via tensor decomposition

Abstract Multi-dimensional population balance equations (PBEs) are commonly used to describe the dynamics of particulate processes such as granulation. Such a class of equations are numerically complex and computationally intensive to solve due to the multiple internal coordinates involved. A computationally efficient model reduction technique would overcome the computational overheads associated with the solution of multi-dimensional PBEs. Moreover, this enables the process model to be used efficiently in process control and optimization. This study is concerned with the development of a novel reduced order model for a three-dimensional population balance model (PBM) for granulation, using a tensor decomposition technique in combination with separation of variables and singular value decomposition. These techniques were used to decompose the complex aggregation and breakage integrals. The developed model is faster by two orders of magnitude, requires less memory allocation for the storage of variables and results in negligible error when compared with the full model.

Integrated Population Balance Model Development and Validation of a Granulation Process

This study is concerned with the development of an integrated three-dimensional population balance model (PBM) that describes the combined effect of key granulation mechanisms that occur during the course of a granulation process. Results demonstrate the importance of simulating the different mechanisms within a population balance model framework to elucidate realistic granulation dynamics. The incorporation of liquid addition in the model also aids in demarcating the dynamics in different regimes such as premixing, granulation (during liquid addition) and wet massing (after liquid addition). For the first time, the effect of primary particle size distributions and mode of binder addition on key granule properties was studied using an integrated PBM. Experimental data confirms the validity of the overall model as compared to traditional models in the literature that do not integrate the different granulation mechanisms.

Parallel simulation of population balance model-based particulate processes using multicore CPUs and GPUs

Computer-aided modeling and simulation are a crucial step in developing, integrating, and optimizing unit operations and subsequently the entire processes in the chemical/pharmaceutical industry. This study details two methods of reducing the computational time to solve complex process models, namely, the population balance model which given the source terms can be very computationally intensive. Population balancemodels are also widely used to describe the time evolutions and distributions of many particulate processes, and its efficient and quick simulation would be very beneficial. The first method illustrates utilization of MATLABs Parallel Computing Toolbox (PCT) and the second method makes use of another toolbox, JACKET, to speed up computations on the CPU and GPU, respectively. Results indicate significant reduction in computational time for the same accuracy using multicore CPUs. Many-core platforms such as GPUs are also promising towards computational time reduction for larger problems despite the limitations of lower clock speed and device memory. This lends credence to the use of highfidelity models (in place of reduced order models) for control and optimization of particulate processes.

Population Balance Models for Pharmaceutical Processes

The pharmaceutical industry is predominantly dominated by the handling of particulate matter in the form of solids and emulsions. With the enforcement of the Quality by Design (QbD) initiative by the Food and Drug Association (FDA), a process systems engineering based case toward particulate process design is advantageous. This suggests the need for mechanistic modeling approaches that can be used for an accurate representation of the process dynamics. The inherent discrete nature of population balance models (PBM) makes it an appropriate framework for modeling particulate processes. With the representation of the particulate processes used for pharmaceutical product manufacturing using various modeling frameworks, advancements can be made to improved control and optimization of the process. This chapter provides a detailed review on the applicability and significance of PBMs in drug product manufacturing and is aimed to provide greater insight into the field of process systems engineering.