Periodic Reporting for period 1 - MODSIM (Modelling of Twin Screw Granulation on Micro-Macro Scales) Reporting period: 2019-05-01 to 2021-04-30 Summary of the context and overall objectives of the project Objective 1 was to develop a method for PBM that is computationally efficient and compute the results with higher precision. This allows the researcher to have an advanced prediction of how the system behaves when the process parameters are varied, hence further leads to less powder wastage which was the main target of the project. The mathematical model developed for continuous manufacturing unit (twin-screw granulator) using population balance model (PBM) approach by incorporating the process parameters in PBM. For solving these complex models, an accurate and efficient numerical technique is required. Therefore, we developed two finite volume methods to solve a simultaneous aggregation-breakage PBM. The second objective was to extract the mean residence time (MRT) so that during the granulation we have more control over the process and desired quality granules can be manufactured. This highly depends on the knowledge of MRT which describes the stay of powder in a specific part of the screw. Different properties granules can be prepared by correlating the MRT with process parameters such as liquid to solid ratio (L/S), powder feed rate, and screw speed. This gives a better understanding of the behavior of granules prepared during the granulation process. In addition, the most important parameter while using the wet TSG is the MRT of powder inside the barrel. Process parameters including feed flow rate, screw speed, and L/S are correlated with the obtained values of MRT to build a predictive tool. Artificial neural network (ANN) modeling is implemented to predict the MRT of pharmaceutical formulation in a wet TSG. Next, we develop a model that has the ability to predict mechanistically the behaviour of particles inside the TSG. Experimental data is collected for Microcrystalline cellulose (MCC-101, Avicel pH 101) was granulated with water in a TSG. A five compartmental population balance model (CPBM) is developed and 10 parameters related to aggregation and breakage PBM is optimized. In addition, kriging interpolation is used to interpolate for new values of empirical parameters at different L/S and screw speeds. This model has the tendency to extract new data and further assists in reducing the waste of the powder in the pharmaceutical industry. Five CPBM is developed for the TSG. Moreover, Kriging interpolation is used to interpolate for new values of empirical parameters at different L/S and screw speeds. Finally, the CPBM model is calibrated and validated using the experimental data. Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far WP 1An inverse problem approach is developed to extract the structure of the aggregation/breakage frequencies and size-dependent kernels. These kernels are further implemented in the PBM. For solving the PBM, it is necessary to have a numerical method that will be highly accurate and efficient as the optimization of parameters requires a fast algorithm. Therefore, we developed two finite volume methods to solve a simultaneous aggregation-breakage PBM. This work has been published in the top journal “Journal of Computational Physics” (http://hdl.handle.net/10344/10422). In addition, an MC method is developed to solve the multidimensional PBM, and the manuscript is under preparation.WP 2The most important parameter while using the wet twin-screw granulator is the mean residence time of powder inside the barrel. Process parameters including feed flow rate, screw speed, and liquid to solid ratio are correlated with the obtained values of mean residence time (MRT) to build a predictive tool. ANN modeling is implemented to predict the MRT of pharmaceutical formulation in a wet twin-screw granulator. This MRT has been later used in defining the time limits in the integrals of the PBM. This work has been published in the top journal of chemical engineering “Chemical Engineering Research and Design” (http://hdl.handle.net/10344/10403). WP 3WP3 concerns the development of a 5 CPBM as a predictive tool of the particle size distribution (PSD) for wet granulation in co-rotating twin-screw granulator (TSG). This model is derived in terms of L/S and screw speed representing the main process parameters of the TSG. Moreover, Kriging interpolation is used to interpolate for new values of empirical parameters at different L/S and screw speeds. Finally, the CPBM model is calibrated and validated using the experimental data. This work is published in “International Journal of Pharmaceutics” (http://hdl.handle.net/10344/10421).WP 4The project and research findings have been presented in various seminars and consortia, internally and externally including at the 9th International Granulation Workshop, Lausanne, Switzerland 26th - 28th June 2019 (https://www.sheffield.ac.uk/agglom/2019) this research work was published related to WP-2, AICHE 2019, Pharmaceutical Manufacturing Technology Centre PMTC day (2019-2021), Process Engineering Cluster-Bernal Institute (2019-2020), and various international online conferences. The researcher also participated in Education, public engagement (EPE) program “Science Week Workshops” at the University of Limerick from 2019 to 2021. This engagement includes workshops “Lego Robotics Challenge with Primary Schools” and “SMART Manufacturing Workshop with Secondary Schools”. The total participation during this event is 100 students from different schools. I have been invited as a visiting researcher for a period of 2 weeks by Dr. Sukhjit Singh, NIT Jalandhar to develop research collaboration. Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far) For the pharma and chemical industries, the predictive tools developed using the PBM concept provide an advanced prediction for the granulation processes by varying process parameters such as L/S ratio, powder feed rate, and screw speed. Hence a technique is required which computes the results by consuming lesser computational time. This further helps the industry to fasten its production after obtaining the desired prediction for the granulation process. Hence the algorithms developed for solving the PBM are useful for the pharma industry and directly impact society. It is also important to know that how much time is spent by the powder in which part of the screw that further helps in understanding the mechanistic behavior leads to more detailed knowledge of the granulation process. This is very important as different attributes have been predicted with different values of process parameters. For example: for low solid to liquid ratio, particle size distribution leads to bimodal behavior. The mechanistic understanding further helps in control granulation and leads to more efficient granulation. Alsow we n developed a new mechanistic and deterministic CPBM as a predictive tool of the particle size distribution (PSD) for wet twin-screw granulators. The mathematical model accounts for aggregation and breakage of the particles occurring in five compartments of the TSG with inhomogeneous screw configurations. This provides a better understanding of the granulation process and provides the particle behavior inside the barrel after each zone/compartment. The compartment model is built in terms of process parameters which helps in predicting the nature of granules prepared during the wet granulation. In addition, the advanced prediction also reduces powder wastage and in turn reduces the cost of drugs which is very important to society irrespective of gender. http://hdl.handle.net/10344/10422 http://hdl.handle.net/10344/10421 http://hdl.handle.net/10344/10403
