Publications

Authors: Ravendra Singh, Marianthi Ierapetritou, Rohit Ramachandran

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Abstract: A novel manufacturing strategy based on continuous processing integrated with online monitoring tools coupled with efficient automatic feedback control system is highly desired for efficient Quality by Design (QbD) based manufacturing of the next generation of pharmaceutical products with optimal consumption of time, space and resources. In this manuscript, an efficient plant-wide control strategy for an integrated continuous pharmaceutical tablet manufacturing process via roller compaction has been designed in silico. The designed control system consists of five cascade control loops and three single control loops resulting in 42 controller tuning parameters. An effective controller parameter tuning strategy involving an ITAE method coupled with an optimization strategy has been proposed and the designed control system has been implemented in a first principle model-based flowsheet that was simulated in gPROMS (Process System Enterprise). The advanced techniques (e.g. anti-windup) have been employed to improve the performance of the control system. The ability of the control system to reject the unknown disturbances as well as to track the set point has been analyzed. Results demonstrated enhanced performance of critical quality attributes (CQAs) under closed-loop control compared to open-loop operation thus illustrating the potential of closed-loop feedback control in improving pharmaceutical manufacturing operations.

Authors: Dana Barrasso, Rohit Ramachandran

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Abstract: Three-dimensional (3-D) population balance equations are often used to model wet granulation processes, simulating distributions in particle size, liquid binder content, and porosity. In multi-component granulation, two or more solid components are present, and granule composition becomes a fourth distributed parameter. In this study, a four-dimensional (4-D) population balance model for multi-component wet granulation is presented and solved. Population balance models of high order are computationally expensive, limiting their applicability in analysis and design. The 4-D model was reduced to a combination of lower-dimensional models using the lumped parameter technique, in which one or more particle characteristics is assumed to be fixed within the remaining distributions. The reduced order models were compared with the full 4-D model for accuracy and computation time. Significant time savings were observed for all reduced order models. The 3-D model with gas volume as the lumped parameter showed the most promising results as an alternative to the 4-D model, which can be attributed to the limited influence of gas volume on aggregation and breakage rates.

Authors: Maitraye Sen, Ravendra Singh, Aditya Vanarase, Joyce John, Rohit Ramachandran

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Abstract: It has been recognized that the application of quality by design (QbD) to continuous processing in the pharmaceutical industry leads to better process control, improved product quality and mitigates scale-up issues (Schaber et al., 2011), whereby a component of QbD involves the development quantitative model-based representation of the process. In this work a population balance model (PBM) framework has been developed to model the dynamics of a continuous powder mixing process which is an important and complex unit operation used in a pharmaceutical tablet manufacturing process. Our previous studies have shown that PBM is effective in determining the various critical quality attributes (CQAs) (relative standard deviation (RSD), API composition and residence time distribution (RTD)) associated with mixing. It can also account for the key design and process parameters such as mixer RPM, processing angle, blender dimensions and number of radial and axial compartments. The developed PBM has been quantitatively validated by fitting experimentally obtained values of the above mentioned CQAs for different operating conditions. The model is dynamic and computationally tractable compared to traditional discrete element model (DEM) representations of mixing processes. This lends credence to the use of the model as an effective tool in control and optimization of blending process and can have future implementation in designing a Process Analytical Technology (PAT) system which will allow considerable improvements on the current manufacturing framework.

Authors: Preetanshu Pandey, Jing Tao, Anwesha Chaudhury, Rohit Ramachandran, Julia Z. Gao & Dilbir S. Bindra

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Abstract: 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.

Authors: Fani Boukouvala, Vasilios Niotis, Rohit Ramachandran, Fernando J.Muzzio, Marianthi G.Ierapetritou

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Abstract: Manufacturing of powder-based products is a focus of increasing research in the recent years. The main reason is the lack of predictive process models connecting process parameters and material properties to product quality attributes. Moreover, the trend towards continuous manufacturing for the production of multiple pharmaceutical products increases the need for model-based process and product design. This work aims to identify the challenges in flowsheet model development and simulation for solid-based pharmaceutical processes and show its application and advantages for the integrated simulation and sensitivity analysis of two tablet manufacturing case studies: direct compaction and dry granulation. The developed flowsheet system involves a combination of hybrid, population balance and data-based models. Results show that feeder refill fluctuations propagate downstream and cause fluctuations in the mixing uniformity of the blend as well as the tablet composition. However, this effect can be mitigated through recycling. Dynamic sensitivity analysis performed on the developed flowsheet, classifies the most significant sources of variability, which are material properties such as mean particle size and bulk density of powders.

Authors: Rohit Ramachandran, Mansoor A.Ansari, Anwesha Chaudhury, Avi Kapadia, Anuj V.Prakash, Frantisek Stepanek

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Abstract: This study is concerned with quantifying the effect of primary particle size polydispersity on granule inhomogeneity for fluid-bed granulation. Specifically, it looks at how the variability in the PSD affects key granule properties at the granulation end-point. For the first time, the distribution of primary particles among different size fractions of the final granules was investigated computationally, together with experimental validation. Granulation was carried out from primary particles with the same mean size but different widths of the size distribution and the granules were subsequently “disassembled” both physically and computationally to analyze their composition. The particle size distribution did not have any effect on the size distribution of the granules, but strongly influenced their composition and porosity. Interestingly, the incidence of coarse primary particles () was highest within the smallest granule size fractions, and conversely, large granules contained predominantly fine () primary particles. These findings have significant implications for the granulation of heterogeneous powder mixtures (e.g. API and excipient).

Authors: Rohit Ramachandran, Anwesha Chaudhury

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Abstract: This paper is concerned with enhanced process design and control of a multiple-input multiple-output (MIMO) granulation process. The work is based on a first-principles mechanistic three-dimensional population balance model (3D-PBM), which has been previously validated against experiments at the laboratory-scale for various operating conditions and formulations. The main objective of this study is via a novel process design, to control and operate the granulation process under more optimal conditions. Novelty of the work lies in the usage of the validated 3D-PBM to extract suitable multiple control-loop pairings from which an overall control loop performance is qualitatively and quantitatively assessed. Results show that for most existing granulation process configurations, enhanced control-loop performance is not achieved and as a result an alternative process design strategy is necessary. The proposed design demonstrates increased efficiency in the control and operation of the granulation process, which is required for further efficient control and operation of subsequent downstream processes.

Authors: Fani Boukouvala, Atul Dubey, Aditya Vanarase, Rohit Ramachandran, Fernando J. Muzzio, Marianthi Ierapetritou

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Abstract: The application of computationally inexpensive modeling methods for a predictive study of powder mixing is discussed. A multidimensional population balance model is formulated to track the evolution of the distribution of a mixture of particle populations with respect to position and time. Integrating knowledge derived from a discrete element model, this method can be used to predict residence time distribution, mean and relative standard deviation of the API concentration in a continuous mixer. Low-order statistical models, including response surface methods, kriging, and high-dimensional model representations are also presented. Their efficiency for design optimization and process design space identification with respect to operating and design variables is illustrated.