Design Space of Pharmaceutical Processes Using Data-Driven-Based Methods
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The identification and graphical representation of process design space are critical in locating not only feasible but also optimum operating variable ranges and design configurations. In this work, the mapping of the design space of pharmaceutical processes is achieved using the ideas of process operability and flexibility under uncertainty.
For this purpose, three approaches are proposed which are based on different data-driven modeling techniques: response surface methodology, high-dimensional model representation, and kriging methodology. Using these approaches, models that describe the behavior of the process at different design configurations are generated using solely experimental data. The models are utilized in mixed integer non-linear programming formulations, where the optimum designs are identified for different combinations of input parameters within the operating parameter and material property ranges.
Based on this idea, by defining a desirable output range, the corresponding range of input variables that result to acceptable performance can be accurately calculated and graphically represented.
The main advantages of the methodologies used in this work are, firstly, that there is no restriction by the lack of first-principle models that describe the investigated process and, secondly, that the models developed are computationally inexpensive. This work can also be used for the comparative analysis of the use of different surrogate-based methodologies for the identification of pharmaceutical process Design Space.
KeywordsDesign space mapping Data-driven models Kriging High-dimensional model representations Response surface Pharmaceutical processes
This work was supported by the ERC-SOPS (NSF-0504497, NSF-ECC 0540855). Also special thanks to Bill Englisch and Aditya Vanarase for providing the experimental data.
- 7.Halemane KP and Grossmann IE. Optimal process design under uncertainty. 1987 cited. Available from: http://hdl.handle.net/1903/4569
- 18.Lima F, Jia Z, Ierapetritou M, Georgakis C. Similarities and differences between the concepts of operability and flexibility: The steady-state case. AIChe J. 2010;56:702–16.Google Scholar
- 21.Floudas CA. Nonlinear and mixed-integer optimization: fundamentals and applications. New York: Oxford University Press; 1995.Google Scholar
- 22.Jia Z, Davis E, Muzzio FJ, Ierapetritou MG. Predictive modeling for pharmaceutical processes using kriging and response surface. JPI, 2009;4:174.Google Scholar
- 23.Boukouvala F, Muzzio F, Ierapetritou M. Predictive modeling of pharmaceutical processes with missing and noisy data. AIChe J, 2010 cited; Available from: http://onlinelibrary.wiley.com/doi/10.1002/aic.12203/full.
- 25.Genyuan Li S-WW, Herschel Rabitz. High dimensional model representations (HDMR): concepts and applications. cited. Available from: http://www.ima.umn.edu/talks/workshops/3-15-19.2000/li/hdmr.pdf.
- 26.Pistek M. High dimensional model representation. cited. Available from: as.utia.cz/files/phdws06/fullpaper_file_53.pdfGoogle Scholar
- 32.Box GEP, Wilson KB. On the experimental attainment of optimum conditions. J R Stat Soc B Methodol. 1951;13(1):1–45.Google Scholar
- 33.Raymond HM, Douglas CM. Response surface methodology: process and product in optimization using designed experiments. New York: Wiley; 1995. p. 728.Google Scholar
- 34.Cressie N. Statistics for spatial data (Wiley Series in Probability and Statistics). New York: Wiley; 1993. p. 1993.Google Scholar
- 35.Isaaks E. SR, Applied Geostatistics. New York: Oxford University Press; 1989.Google Scholar
- 37.Myers RH, Classical and modern regression with applications (second edn). The Duxbury advanced series in statistics and decision sciences, ed. D. Press. PWS-KENT, Boston, MA, 1990Google Scholar
- 38.Myers RH, Montgomery DC. Response surface methodology: process and product in optimization using designed experiments. New York: Wiley; 1995. p. 728.Google Scholar
- 39.Ferris MC. MATLAB and GAMS: interfacing optimization and visualization software. 2005 cited; Available from: http://pages.cs.wisc.edu/~ferris/matlabgams.pdf.
- 40.Vanarase AU, Muzzio F. Effect of operating conditions and design parameters in a continuous powder mixer. Adv Powder Tech, 2010; (in press).Google Scholar