Integrated PBM-DEM Model Of A Continuous Granulation Process

Transcription

1 Integrated PBM-DEM Model Of A Continuous Granulation Process Rohit Ramachandran March 8, 2016 Department of Chemical and Biochemical Engineering Rutgers University Rutgers, The State University of New Jersey

2 Why model granulation and the integrated process? Model-based approach to improve Quality by Design. Process modeling: relate inputs to outputs, develop knowledge space Scientific representations of phenomena Predictability, reliability, quality, cost Design, control, optimization Preference for first-time right processes (QbD rather than QbT) 1. Boukouvala, et al. (2013) Journal of Pharmaceutical Innovation, 8, p

3 Continuous tablet manufacturing plant (DC and WG) Feeders Blender Tablet press Ref.: Singh, R., Boukouvala, F., Jayjock, E., Ramachandran, R. Ierapetritou, M., Muzzio, F. (2012). GMP news, European Compliance Academic (ECE), August, 2012,

4 Wet granulation process Binder is applied to fine powder particles to create larger granules Applications: Pharmaceuticals Detergents Agricultural Aims: Improve flowability Improve compressibility Avoid segregation of mixture Adjust dissolution rate 4

5 Wet granulation mechanisms 1 Wetting & Nucleation 2 Aggregation & Consolidation 1 Binder droplets Nuclei Granules Dry powder (i) (ii) Consolidated granule 3 Breakage & Attrition 4 Layering Granule Daughter particles Fines Granule 1. Iveson, et al. (2001) Powder Technology, 117 (12), p3-39 5

6 PBM vs. DEM Frequency Population balance modeling: Tracks number of particles in each size class based on rate expressions for subprocesses Typically empirical or semi-empirical Effects of many process parameters and material properties are not accounted for Spatial information is not inherent Compartmental PBMs need transfer rate data/assumptions Many unknown parameters must be estimated with experimental data Discrete element modeling: Tracks each particle in space using laws of physics Can output collision, velocity, force, and spatial data at the particle scale. Does not inherently simulate rate processes, such as aggregation. Computationally intensive Population of granules Individual granule Particle size Particle size 6

7 Multi-scale bi-directional coupling of DEM and PBM Objectives Design an algorithm for bi-directional coupling between the PBM and DEM, using PBM to update the distribution of particles within the granulator, and DEM to feed particle-scale information to the PBM Implement mechanistic rate expressions by creating links between the DEM results and the PBM rate kernels for aggregation, breakage, and consolidation Develop computationally-efficient techniques to transfer data between the models. 7

8 PBM & DEM: A multi-scale approach 8

9 Collision frequency and efficiency R agg ( x, x' ) ( x, x' ) F( x) F( x' ) Collision frequency Previous kernels use constant or size dependent value Can be determined directly from DEM, capturing dynamic effects and effects of process parameters (such as impeller speed) Collision efficiency Frequency ( x, x' ) C( x, x' ) ( x, x' Likelihood that a collision will result in aggregation Efficiency Empirical terms often used, but mechanistic kernels have been developed Related to particle properties (surface wetness, mass, density, other material properties) Depends on collision properties (relative velocity), determined from DEM. ) 9

10 Collision frequency and PSD Pure DEM (no PBM) Using four different PSDs Number of collisions between each particle size class counted PSD affects collision frequency Demonstrates need for bi-directional coupling Distributions simulated in DEM Collision rates vs. sizes of colliding particles 10

11 Software considerations and bi-directional coupling write read Software challenges Computational expense of DEM inhibits solution speed A new approach: Population balance drives the simulation DEM simulations performed intermittently to update key variables Triggered by sufficient changes in PBM Reduces need to simulate entire process time in DEM Each DEM simulation is independent Custom physics vs. custom simulation Text files gproms write read Text files Executed using user code in gproms and STAR-CCM+ STAR-CCM+ 11

12 DEM results: Effect of average particle size, properties Size distribution Larger particles experienced more collisions, per particle, than smaller ones. Effect of size distribution on collision/impact rates demonstrates need for bi-directional coupling. Mild effect on average velocities. Trigger condition: 10% change in d4,3 Impact rate vs. particle size Particle properties Greater LS ratio, fewer collisions (lower coefficient of restitution) Lower porosity, more collisions (greater density, Young s modulus) No clear effects on particle velocities Observations due to contact model and relationship between particle and DEM properties Trigger condition: 5% change in liquid fraction Impact rate vs. particle size 12

13 DEM results: Effect of screw geometry and speed Effects of geometry and process parameters are captured! Fewer collisions in feed screw, especially for larger particles Small effect of screw speed on collision rates of large particles in feed screw Impact rate vs. particle size Average velocity vs. particle size Lower particle velocities in feed screw than in mixing elements Increased screw speeds result in larger particle velocities, particularly in mixing elements 13

14 Coupled PBM-DEM model results Mixing elements Feed screw Time 14

15 PBM-DEM Results: Base case and model settings Widening of PSD Increase in size Decrease in porosity Increase in external liquid volume Liquid addition, consolidation, coalescence of wet and dry particles Reproducibility Particle size distribution Average porosity vs. time Average diameter vs. time External liquid volume vs. time Wider vs. tighter trigger conditions Frequent: 15.4 hr Base: 13.1 hr Infrequent: 8.4 hr 15

16 PBM-DEM Results: Effect of geometry and speed Particle size distribution Average diameter vs. time Larger granules formed in mixing elements than in feed screw. Greater collision rates, wider velocity distribution More aggregation, breakage, consolidation, and external liquid Average porosity vs. time External liquid volume vs. time Increase in screw speed, more consolidation, larger granules. Ability to capture effects of geometry and speed in PBM is solely due to DEM coupling. Quality-by-Design 16

17 PBM-DEM Results: Other effects Lower liquid-to-solid ratio Much smaller granules Less aggregation and consolidation Particle size distribution Average diameter vs. time Decrease in binder viscosity Weaker, more deformable particles More consolidation, external liquid, breakage, and aggregation. Average porosity vs. time External liquid volume vs. time Greater pore saturation fraction Less external liquid, smaller particles 17

18 Continuous simulations: Objectives and approach Objective Demonstrate predictive capabilities of a mechanistic PBM- DEM model for a continuous twin screw granulation process Compartmental approach Steady-state algorithm One-way coupling of compartmental residence times from DEM Fully coupled PBM-DEM model, where DEM simulates individual compartments Qualitative validation Evaluate effect of screw element configuration on CQAs Compare to experimental trends 18

19 Screw element configuration and residence time Eight configurations tested Four conveying elements Three conveying and one kneading element 30, 60, 90 offset 30 reverse offset Two conveying and two kneading 30, 60, 90 offset DEM simulations at steady state Mass in each compartment is evaluated Compared to flow rate to evaluate residence time Residence time by compartment 19

20 Effect of screw configuration on product size Strongly bi-model distributions in conveying elements only Drop nucleation, layering are dominant, minimal breakage Agrees with experimental observations 1 Breakage dominates in kneading elements Less breakage in 30 offset than has conveying characteristics Product size distributions, 6 KEs Increase in length of kneading section results in more breakage, smaller particles. Product size distributions, 12 KEs PBM-DEM model is sensitive to equipment geometry Design tool 1. El Hagrasy and Lister, (2013) AIChE Journal, 59 (11), p

21 Effect of screw configuration on liquid distribution Simulated results match experimental trends 1 Larger particles contain more liquid, regardless of screw configuration Conveying section shows poorest liquid distribution Liquid distribution in 30 KE improves with increase in length of KE section 60 KE has poor liquid distribution, no improvement with increase in length 90 KE shows improvement over 30, but increase in length does not completely eliminate size dependence of liquid content Model currently does not track liquid distribution of fine particles and liquid exchange between wet granules Liquid content vs. particle size Conveying 30 KE 60 KE 90 KE 1. El Hagrasy and Lister, (2013) AIChE Journal, 59 (11), p

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23 Conclusions Modeling granulation is a multi-scale problem Developed a detailed, 2D mechanistic PBM for wet granulation processes in gproms. Established and demonstrated a bi-directional coupling technique with DEM using STAR-CCM+. Demonstrated sensitivities to material properties, process parameters, and equipment design, as a path towards QbD. PBM-DEM for continuous compartmental application Qualitative accuracy Useful when high fidelity results are desired, inherent sensitivities Predictive capabilities, less useful for control or optimization, more for design Next steps: Use hybrid model to evaluate design alternatives Develop new mechanistic kernels and expand understanding of particle properties for PBM simulations 23

24 PPSG Group

25 Acknowledgements Funding 2 NIPTE Young Investigator grants FDA NSF-ERC-SOPs NSF CAREER, EAGER, REU Czech-American S&T Cooperation BMS, Syngenta, J&J, PSE, RU, Evonik, BASF, GSK, Bosch Collaborators Dr. Huiquan Wu, Dr. Mansoor Khan, Dr. Celia Cruz Colleagues from Rutgers, Purdue, NJIT, UPRM Graduate Students from ERC-SOPS Industrial mentors PSE, CD-adapco