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Twin screw granulation (TSG) is a continuous wet granulation technique that is used widely across different solid manufacturing industries. The TSG has been recognized to have numerous advantages due to its modular design and continuous manufacturing capabilities, including processing a wide range of formulations. However, it is still not widely employed at the commercial scale because of the lack of holistic understanding of the process. This study addresses that problem via. the mechanistic development of a regime map that considers the complex interactions between process, material, and design parameters, which together affect the final granule quality. The advantage of this regime map is that it describes a more widely applicable quantitative technique that can predict the granule growth behavior in a TSG. To develop a robust regime map, a database of various input parameters along with the resultant final granule quality attributes was created using previously published literature experiments. Missing data for several quality attributes was imputed using various data completion techniques while maintaining physical significance. Mechanistically relevant non-dimensional X and Y axis that quantify the physical phenomena occurring during the granulation were developed to improve the applicability and predictability of the regime map. The developed regime map was studied based on process outcomes and granule quality attributes to identify and create regime boundaries for different granule growth regimes. In doing so breakage-dominant growth was incorporated into the regime map, which is very important for TSG. The developed regime map was able to accurately explain the granule growth regimes for more than 90 % of the studied experimental points. These experimental were generated at vastly different material, design, and process parameters across various studies in the literature, this further increases the confidence in the developed regime map. [ABSTRACT FROM AUTHOR]

[Display omitted] • Variation of mass transfer in glass and PTFE capillary due to adhesion of continuous phase was studied. • Wetting of the wall by the continuous phase film affects the mass transfer significantly. • The pressure drop in the glass capillary was always higher than the PTFE capillary. • Slug velocities & internal circulation rate is strongly governed by the wetting properties. • Model to quantify the effect of wettability and physical properties on mass transfer. The wettability of the reactor wall has a significant effect on the interfacial liquid–liquid mass transfer rates in segmented flow. This work quantitatively demonstrates the importance of choosing the right material of construction of flow reactors to achieve the desired mass transfer performance. Glass and PTFE capillaries of identical diameter were used to study the effect of hydrophilic and hydrophobic surfaces on the hydrodynamics and mass transfer of the system. It was observed that for the overall mass transfer coefficient (k L a) changed by two orders of magnitude depending on the wettability of the continuous phase. The observations indicated that it is essential to achieve complete wetting of the capillary walls by the continuous phase for significant mass transfer enhancement. The observations are discussed on the basis of film thickness and slip velocity at the wall as well as the slip velocity at liquid–liquid interface. Predictions of the mass transfer coefficient using a model based on the interfacial and fluid properties showed excellent match with the experiments thereby allowing us to explore the effects of wettability on the overall mass transfer coefficient in greater detail. [ABSTRACT FROM AUTHOR]

Highlights • A new framework for a unidirectional multi-scale model of wet granulation. • A multi-dimensional Population Balance Model is developed using mechanistic kernel. • Population Balance Model is parallelized to address the computational complexity requirements. • Results demonstrate scaling of DEM and PBM in multiple cores. • The methodology is implemented to high shear wet granulation process. Abstract The accurate modeling of the physics underlying particulate processes is complicated and requires significant computational capabilities to solve using particle-based models. In this work, a unidirectional multi-scale approach was used to model the high shear wet granulation process. A multi-dimensional population balance model (PBM) was developed with a mechanistic kernel, which in turn obtained collision data from the discrete element modeling (DEM) simulation. The PBM was parallelized using a hybrid OpenMP+MPI approach. The DEM simulations were performed using LIGGGHTS, which was parallelized using MPI. Speedups of approximately 14 were obtained for the PBM simulations and approximately 12 for the DEM simulations. The uni-directional coupling of DEM to PBM was performed using middle-ware components (RADICAL-Pilot) that did not require modifications of the DEM or PBM codes, yet supported flexible execution on high-performance platforms. Results demonstrate scaling from 1 to 128 cores for the PBM and up to 256 cores for the DEM. The proposed method, implementations and middle-ware enable the modeling of high shear wet granulation process faster than existing approaches in literature. [ABSTRACT FROM AUTHOR]

The need to produce Hydrogen in an environmentally friendly manner has increased as the transportation industry has started to employ cleaner sources of energy. One of the cleaner ways to produce H 2, is to use the Bunsen S-I cycle. This includes the separation of the H 2 from an ionic mixture of Hydrogen Iodide and water using distillation. In this work, the Pitzer’s model for electrolyte solutions has been employed to represent the vapour-liquid equilibrium (VLE) of the binary system of HI and H 2 O. The activity coefficient (γ) is calculated from the equation which indicated the non-ideal nature of the system and aided the calculation of the vapour phase mole fraction. The experimental VLE data was obtained from a recirculating still at 101.3 kPa pressure for the binary system. This experimental data and data from literature was used to estimate the parameters of the Pitzer’s model to represent the activity coefficient of HI in this mixture. These optimized parameters could explain all the variations in the data with more that 90% accuracy for most of the data, with an average standard deviation of about 0.04. Furthermore, the temperature dependent form of the Pitzer’s equation was used to predict the VLE data at higher pressures of 3 and 5.7 bars and higher temperature conditions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Data integration framework is applied to two continuous pharmaceutical manufacturing pilot plants. • Complex data streams and simulation models are integrated on cloud. • Approaches to integrate legacy equipment and to incorporate data with modeling software are demonstrated. • Cyber-physical security framework is established to mitigate risks of cyberattacks. • Critical challenges and opportunities for practical implementation are discussed. The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. In this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted. [ABSTRACT FROM AUTHOR]

Quality risk management is an important task when it pertains to the pharmaceutical industry, as this is directly related to product performance. With the ICH Q9 guidelines, several regulatory bodies have encouraged the pharmaceutical industry to implement risk management plans using scientific and systemic approaches such as quality-by-design to asses product quality. However, the implementation of such methods has been challenging as assessment of risks requires accurate quantitative models to predict changes in quality when variations occur. This study describes a framework that quantitatively assesses risk for a twin screw wet granulation process. This framework consists of a physics-constrained autoencoder system, whose outputs are constrained using physics-based boundary conditions. The latent variables obtained from the auto-encoder are used in a support vector machine-based classifier to understand the granule growth behavior occurring within the system. This framework is able to predict the process outcomes with 86% accuracy and classify the granule growth regimes with a true positive rate of 0.73. Based on the classification the risk associated with the process can be estimated. [ABSTRACT FROM AUTHOR]

The solution of high-dimensional PBMs using CPUs are often computationally intractable. This study focuses on the development of a scalable algorithm to parallelize the nested loops inside the PBM via a GPU framework. The developed PBM is unique since it adapts to the size of the problem and uses the GPU cores accordingly. This algorithm was parallelized for NVIDIA® GPUs as it was written in CUDA® and C/C++. The major bottleneck of such algorithms is the communication time between the CPU and the GPU. In our studies, communication time contributed to less than 1% of the total run time and a maximum speedup of about 12 over the serial CPU code was achieved. The GPU PBM achieved a speedup of about two times compared to the PBM's multi-core configuration on a desktop computer. The speed improvements are also reported for various CPU and GPU architectures and configurations. [ABSTRACT FROM AUTHOR]

[Display omitted] • Sensitivity analysis and optimization are applied for energy metrics and yield. • Case studies are developed for batch and continuous wet granulation processes. • A maximum energy saving of 83.3% is achieved while maintaining quality. • Experiments confirm quality and process performance at optimal conditions. During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors (Sampat et al., 2022), this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. The results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%. [ABSTRACT FROM AUTHOR]