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Predictive Modeling for Pharmaceutical Processes Using Kriging and Response Surface

  • Process Design, Optimization, Automation, and Control
  • Published:
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Abstract

Powder feeding is a fundamental unit operation in the pharmaceutical industry. For the cases in which first-principle process models are unknown, such as when new powder mixture feeding operations are being evaluated, or no longer accurately describe current operating behavior, surrogate model-based approaches can be employed in order to quantify input–output behavior. In this work, two such metamodeling techniques—kriging and response surface methods—are used to predict a loss-in-weight feeder unit’s flow variability in terms of unit flowability and feed rate. Based on a comparison of predicted with experimental values, an iteratively constructed kriging model is found to more accurately capture the feeder system behavior compared with the response surface methodology. Although feeders are used as a case study in this paper, the kriging methodology is general to address other processes where first-principle models are not available.

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Acknowledgments

The authors gratefully acknowledge financial support provided by ERC (NSF EEC-0540855) and experimental data provided by Bill Engisch and Aditya Vanarase.

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Authors and Affiliations

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

    Zhenya Jia, Eddie Davis, Fernando J. Muzzio & Marianthi G. Ierapetritou

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  1. Zhenya Jia
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  2. Eddie Davis
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  3. Fernando J. Muzzio
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  4. Marianthi G. Ierapetritou
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Correspondence to Marianthi G. Ierapetritou.

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Jia, Z., Davis, E., Muzzio, F.J. et al. Predictive Modeling for Pharmaceutical Processes Using Kriging and Response Surface. J Pharm Innov 4, 174–186 (2009). https://doi.org/10.1007/s12247-009-9070-6

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  • DOI: https://doi.org/10.1007/s12247-009-9070-6

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