Mechanistic Model Development & Validation of Particulate Processes
Here, we seek to develop mechanistic and predictive models particulate processes such as granulation, mixing and milling. We are working develop sub-models (kernels) that describe the dynamics as a function of key material properties and process parameters. The models are validated against experimental data at the lab-scale and are tested for their predictive capability. Such validated models are able to reduce the time and cost associated with tedious experimental trials.
Schematic showing the formulation of the compartments within the granulator.
Schematic of the algorithm for solving the coupled heat/mass balance and PBM using a mechanistic kernel.
Agglomeration of Sodium carbonate primary particles. As the HLAS droplets come into contact with Sodium carbonate particles, a product (passivation) layer forms around the particle which aids in the agglomeration by forming strong liquid bridges which bind the particles together.
Schematic draft of the modeled well mixed seeded batch cooling crystallization process.
A comprehensive systems representation of the granulation process
Residence Time Distribution (RTD) versus time for feedrate of 30 kg/h
Three regions of the twin-screw granulator based on the experimental setup
Experimental and simulated steady-state mass hold-up values.
Multi-dimensional PBM Simulation from Graphical User Interface
List of Publications from this Research Area:
A. Chaudhury, A. Tamrakar, M. Schongut, D. Smrcka, F. Stepanek, R. Ramachandran. Multi-dimensional population balance model development and validation of reactive granulation processes.Industrial & Engineering Chemistry Research , 54 (3), 842-857, 2015.
D. Barrasso, A. El Hagrasy, J.D. Litster, R. Ramachandran. Multi-dimensional population balance model development and validation for a twin screw granulation process, Powder Technology, 270 B, 612-621, 2015.
M. O. Besenhard, A. Chaudhury, T. Vetter, R. Ramachandran, J. Khinast. Evaluation of parameter estimation methods for crystallization processes modeled via population balance equations. Chemical Engineering Research & Design, 94, 275-289, 2015.
A. Chaudhury, M. E. Armenante and R. Ramachandran. Compartment based population balance modeling of a high shear wet granulation process using data analytics. Chemical Engineering Research & Design, 95, 211-228, 2015.
M. Sen, A. Chaudhury. R. Singh, R. Ramachandran. Two-dimensional population balance development and validation of a pharmaceutical crystallization process. American Journal of Modern Chemical Engineering, 1, 3-29 , 2014.
A. Chaudhury, D. Barrasso, P. Pandey, H. Wu, R. Ramachandran. Population balance model development, validation and prediction of CQAs of a high-shear wet granulation process: Towards QbD in drug product pharmaceutical manufacturing, Journal of Pharmaceutical Innovation, 9 (1), 53-64, 2014.
A. Chaudhury, H. Wu, M. Khan, R. Ramachandran. A mechanistic population balance model for granulation processes: Effect of process and formulation parameters, Chemical Engineering Science, 107, 76-92, 2013.
D. Barrasso, S. Oka, A. Muliadi, J.D. Litster, C. Wassgren and R. Ramachandran. Population balance model validation and prediction of CQAs for continuous milling processes: toward QbD in pharmaceutical drug product manufacturing. Journal of Pharmaceutical Innovation, 8(3), 147-162, 2013.
D. Barrasso, S. Walia, R. Ramachandran. Multi-component population balance modeling of continuous granulation processes: a parametric study and comparison with experimental trends. Powder Technology, 241, 85-97, 2013.
M. Sen, R. Singh, A. Vanarase, J. John and R. Ramachandran. Multi-dimensional population balance modeling and experimental validation of continuous powder mixing processes. Chemical Engineering Science, 80, 349-360, 2012.
P. Pandey, J. Tao, A. Chaudhury, R. Ramachandran, J.Z. Gao, D. Bindra. A combined experimental and modeling approach to study the effects of high-shear wet granulation process parameters on granule characteristics.Pharmaceutical Development & Technology, 18, 210-224, 2012.
R. Ramachandran, M. Ansari, A. Chaudhury, A. Kapadia, A. V. Prakash and F. Stepanek. A quantitative assessment of the influence of primary particle size distribution on granule inhomogeneity. Chemical Engineering Science, 104-110, 71, 2012.