Skip to main content
SpringerLink
Log in

Improving Continuous Powder Blending Performance Using Projection to Latent Structures Regression

  • Research Article
  • Published:
Journal of Pharmaceutical Innovation Aims and scope Submit manuscript

Abstract

Purpose

There has been increasing interest in the last few years especially within the pharmaceutical industry towards continuous powder blending. In this paper, the effects of different design and operating parameters are investigated, which include blade speed, shaft angle, weir height, fill level, blade angle, and blade width.

Method

The projection to latent structures regression is introduced to elucidate the significance of these factors on the two key indices of continuous blending performance, the local blending rate and the mean axial velocity.

Results

Shaft angle and blade speed are the two most influential factors pointing to a blending improvement strategy. The proposed strategy is examined using an experimental setup for the production of pharmaceutical powder mixtures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Williams JC. Continuous mixing of solids. A review. Powder Techn. 1976;15(2):237–43.

    Article  Google Scholar 

  2. Danckwerts PV. Continuous flow systems: distribution of residence times. Chem Eng Sci. 1953;2(1):1–13.

    Article  CAS  Google Scholar 

  3. Gao Y, Muzzio F, Ierapetritou M. Characterization of feeder effects on continuous solid mixing using Fourier series analysis. AICHE J. 2011;57(5):1144–53.

    Article  CAS  Google Scholar 

  4. Weinekötter R, Reh L. Continuous mixing of fine particles. Part. Part. Syst. Char. 1995;12(1):46–53.

    Article  Google Scholar 

  5. Gao Y, Vanarase A, Muzzio F, Ierapetritou M. Characterizing continuous powder mixing using residence time distribution. Chem Eng Sci. 2011;66(3):417–25.

    Article  CAS  Google Scholar 

  6. Williams JC, Richardson R. The continuous mixing of segregating particles. Powder Technol. 1982;33(1):5–16.

    Article  Google Scholar 

  7. Pernenkil L, Cooney CL. A review on the continuous blending of powders. Chem Eng Sci. 2006;61(2):720–42.

    Article  CAS  Google Scholar 

  8. Gao Y, Ierapetritou M, Muzzio F. Periodic section modeling of convective continuous powder mixing processes. AICHE J. 2012;58(1):69–78.

    Article  CAS  Google Scholar 

  9. Laurent BFC, Bridgwater J. Influence of agitator design on powder flow. Chem Eng Sci. 2002;57(18):3781–93.

    Article  CAS  Google Scholar 

  10. Marikh K, Berthiaux H, Gatumel C, Mizonov V, Barantseva E. Influence of stirrer type on mixture homogeneity in continuous powder mixing: a model case and a pharmaceutical case. Chem Eng Res Des. 2008;86(9):1027–37.

    Article  CAS  Google Scholar 

  11. Jones JR, Parker DJ, Bridgwater J. Axial mixing in a ploughshare mixer. Powder Technol. 2007;178(2):73–86.

    Article  CAS  Google Scholar 

  12. Portillo PM, Ierapetritou MG, Muzzio FJ. Effects of rotation rate, mixing angle, and cohesion in two continuous powder mixers—a statistical approach. Powder Technol. 2009;194(3):217–27.

    Article  CAS  Google Scholar 

  13. Vanarase AU, Muzzio FJ. Effect of operating conditions and design parameters in a continuous powder mixer. Powder Technol. 2011;208(1):26–36.

    Article  CAS  Google Scholar 

  14. Portillo PM, Vanarase AU, Ingram A, Seville JK, Ierapetritou MG, Muzzio FJ. Investigation of the effect of impeller rotation rate, powder flow rate, and cohesion on powder flow behavior in a continuous blender using PEPT. Chem Eng Sci. 2010;65(21):5658–68.

    Article  CAS  Google Scholar 

  15. Zhou YC, Yu AB, Stewart RL, Bridgwater J. Microdynamic analysis of the particle flow in a cylindrical bladed mixer. Chem Eng Sci. 2004;59(6):1343–64.

    Article  CAS  Google Scholar 

  16. Arratia PE, N-h D, Muzzio FJ, Godbole P, Reynolds S. A study of the mixing and segregation mechanisms in the Bohle Tote blender via DEM simulations. Powder Technol. 2006;164(1):50–7.

    Article  CAS  Google Scholar 

  17. Brone D, Muzzio FJ. Enhanced mixing in double-cone blenders. Powder Technol. 2000;110(3):179–89.

    Article  CAS  Google Scholar 

  18. Wightman C, Muzzio FJ. Mixing of granular material in a drum mixer undergoing rotational and rocking motions I. Uniform particles. Powder Tech. 1998;98(2):113–24.

    Article  CAS  Google Scholar 

  19. Gao Y, Ierapetritou M, Muzzio F. Investigation on the effect of blade patterns on continuous solid mixing performance. Can J Chem Eng. 2011;89(5):969–84.

    Article  CAS  Google Scholar 

  20. Portillo P, Muzzio FJ, Ierapetritou MG. Hybrid DEM-compartment modeling approach for granular mixing. AICHE J. 2007;53(1):119–28.

    Article  CAS  Google Scholar 

  21. Sarkar A, Wassgren C. Continuous blending of cohesive granular material. Chem Eng Sci. 2010;65(21):5687–98.

    Article  CAS  Google Scholar 

  22. Sarkar A, Wassgren CR. Simulation of a continuous granular mixer: effect of operating conditions on flow and mixing. Chem Eng Sci. 2009;64(11):2672–82.

    Article  CAS  Google Scholar 

  23. Mindlin RD. Compliance of elastic bodies in contact. J Appl Mech. 1949;71:259–68.

    Google Scholar 

  24. Bertrand F, Leclaire LA, Levecque G. DEM-based models for the mixing of granular materials. Chem Eng Sci. 2005;60(8–9):2517–31.

    CAS  Google Scholar 

  25. Dubey A, Sarkar A, Ierapetritou M, Wassgren CR, Muzzio FJ. Computational approaches for studying the granular dynamics of continuous blending processes, 1—DEM based methods. Macromol Mater Eng. 2011;296(3–4):290–307.

    Article  CAS  Google Scholar 

  26. Portillo PM, Ierapetritou MG, Muzzio FJ. Characterization of continuous convective powder mixing processes. Powder Technol. 2008;182(3):368–78.

    Article  CAS  Google Scholar 

  27. Abdi H. Partial least squares regression and projection on latent structures regression (PLS regression). Wiley Interdisciplinary Rev: Computational Statistics. 2010;2(1):97–106.

    Article  Google Scholar 

  28. Stein M. Large sample properties of simulations using Latin hypercube sampling. Technometrics. 1987;29(2):143–51.

    Article  Google Scholar 

  29. Vanarase AU, Alcalà M, Jerez Rozo JI, Muzzio FJ, Romañach RJ. Real-time monitoring of drug concentration in a continuous powder mixing process using NIR spectroscopy. Chem Eng Sci. 2010;65(21):5728–33.

    Article  CAS  Google Scholar 

  30. Wold S, Sjöström M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst. 2001;58(2):109–30.

    Article  CAS  Google Scholar 

  31. Lopes VV, Menezes JC. Inferential sensor design in the presence of missing data: a case study. Chemom Intell Lab Syst. 2005;78(1–2):1–10.

    Article  CAS  Google Scholar 

  32. Gao Y, Muzzio FJ, Ierapetritou MG. Optimizing continuous powder mixing processes using periodic section modeling. Chem Eng Sci. 2012;80:70–80.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems, through grant NSF-ECC 0540855 and by grant NSF-0504497. The authors are thankful to Dr. Salvador G. Muñoz in Pfizer for the PLS Seminar in Purdue University. The authors would also like to thank Juan Osorio, Dr. Patricia M. Portillo, and Dr. Atul Dubey for their help on experimental suggestions and Dr. Douglas B. Hausner for editing this manuscript.

Author information

Authors and Affiliations

  1. Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ, 08854, USA

    Yijie Gao, Fani Boukouvala, William Engisch, Wei Meng, Fernando J. Muzzio & Marianthi G. Ierapetritou

Authors
  1. Yijie Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fani Boukouvala
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. William Engisch
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fernando J. Muzzio
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marianthi G. Ierapetritou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marianthi G. Ierapetritou.

Appendix

Appendix

Details of the 64 simulations. The six factors are normalized with lower bound 0 and upper bound 1. Notice that the negative values of axial velocity indicate net backward flow in the specific conditions.

Table 3 Details of the 64 simulations. The six factors are normalized with lower bound 0 and upper bound 1. Notice that the negative values of axial velocity indicate net backward flow in the specific conditions
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gao, Y., Boukouvala, F., Engisch, W. et al. Improving Continuous Powder Blending Performance Using Projection to Latent Structures Regression. J Pharm Innov 8, 99–110 (2013). https://doi.org/10.1007/s12247-013-9152-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12247-013-9152-3

Keywords

Search

Navigation