Moore, B., C. Georgakis, C. Antoniou, and S. Khattak. “A Two-Phase Approach Optimizing Productivity for a Mab-Producing Cho Cell Culture Process Using Dynamic Response Surface Methodology Models.” Biochemical Engineering Journal 201 (Jan 2024): 109137. https://doi.org/10.1016/j.bej.2023.109137.
Dong, Y. C., C. Georgakis, J. Mustakis, J. M. Hawkins, L. Han, K. Wang, J. P. McMullen, S. T. Grosser, and K. Stone. “Constrained Version of the Dynamic Response Surface Methodology for Modeling Pharmaceutical Reactions.” Industrial & Engineering Chemistry Research 58, no. 30 (Jul 31 2019): 13611-21. https://doi.org/10.1021/acs.iecr.9b00731.
Trentin, G., E. Barbera, A. Bertucco, and C. Georgakis. “Experimental Test of the Design of Dynamic Experiments and Dynamic Response Surface Methodologies: Growth of a Photosynthetic Microorganism.” Industrial & Engineering Chemistry Research (2022 Oct 2022): 12. https://doi.org/10.1021/acs.iecr.2c01851.
Wang, Z. Y., and C. Georgakis. “A Dynamic Response Surface Model for Polymer Grade Transitions in Industrial Plants.” Ind. & Eng. Chem. Res. 58, no. 26 (Jul 2019): 11187-98. https://doi.org/10.1021/acs.iecr.8b04491.
Wang, Z. Y., and C. Georgakis. “An in Silico Evaluation of Data-Driven Optimization of Biopharmaceutical Processes.” AIChE J. 63, no. 7 (Jul 2017): 2796-805. https://doi.org/10.1002/aic.15659.
Wang, Z. Y., N. Klebanov, and C. Georgakis. “DRSM Model for the Optimization and Control of Batch Processes.” IFAC Papers Online 49, no. 7 (2016): 55-60. https://doi.org/10.1016/j.ifacol.2016.07.216.
Pelagagge, F., C. Georgakis, and G. Pannocchia. “Data-Driven Nonlinear MPC Using Dynamic Response Surface Methodology.” Paper presented at the 7th IFAC Conference on Nonlinear Model Predictive Control (NMPC), Bratislava, SLOVAKIA, Jul 11-14 2021.
DRSM Methodology
Wang, Z. Y., and C. Georgakis. “New Dynamic Response Surface Methodology for Modeling Nonlinear Processes over Semi-Infinite Time Horizons.” Ind. & Eng. Chem. Res. 56, no. 38 (Sep 2017): 10770-82. https://doi.org/10.1021/acs.iecr.7b02381.
Klebanov, N., and C. Georgakis. “Dynamic Response Surface Models: A Data-Driven Approach for the Analysis of Time-Varying Process Outputs.” Industrial & Engineering Chemistry Research 55, no. 14 (Apr 13 2016): 4022-34. https://doi.org/10.1021/acs.iecr.5b03572.
Dong, Y. C., C. Georgakis, J. Santos-Marques, and J. Du. “Dynamic Response Surface Methodology Using Lasso Regression for Organic Pharmaceutical Synthesis.” [In English]. Article. Frontiers of Chemical Science and Engineering 16, no. 2 (Feb 2022): 221-36. https://doi.org/10.1007/s11705-021-2061-y.
Dong, Y. C., C. Georgakis, J. Mustakis, and J. P. McMullen. “New Time Sampling Strategy for the Estimation of the Parameters in DRSM Models.” Industrial & Engineering Chemistry Research 59, no. 28 (Jul 2020): 9. https://doi.org/10.1021/acs.iecr.0c00751.
Dong, Y. C., C. Georgakis, J. Mustakis, L. Han, and J. P. McMullen. “Optimization of Pharmaceutical Reactions Using the Dynamic Response Surface Methodology.” Computers & Chemical Engineering 135 (2020): 106778-.
DoDE Applications
Trentin, G., A. Bertucco, C. Georgakis, E. Sforza, and E. Barbera. “Using the Design of Dynamic Experiments to Optimize Photosynthetic Cyanophycin Production by<I> Synechocystis</I> Sp.” [In English]. Article. Journal of Industrial and Engineering Chemistry 117 (Jan 2023): 386-93. https://doi.org/10.1016/j.jiec.2022.10.026.
Georgakis, C, S.-T. Chin, Z Wang, P. Hayot, L.H. Chiang, J. Wassick, and I Castillo. “Data-Driven Optimization of an Industrial Batch Polymerization Process Using the Design of Dynamic Experiments Methodology.” Ind. & Eng. Chem. Res. 59, no. 33 (2020): 14868-80.
Kiparissides, A., C. Georgakis, A. Mantalaris, and E. N. Pistikopoulos. “Design of in Silico Experiments as a Tool for Nonlinear Sensitivity Analysis of Knowledge-Driven Models.” Industrial & Engineering Chemistry Research 53, no. 18 (May 2014): 7517-25. https://doi.org/10.1021/ie4032154.
Fiordalis, A., and C. Georgakis. “Data-Driven, Using Design of Dynamic Experiments, Versus Model-Driven Optimization of Batch Crystallization Processes.” Journal of Process Control 23, no. 2 (Feb 2013): 179-88. https://doi.org/10.1016/j.jprocont.2012.08.011.
DoDE Methodology
Georgakis, C. “Design of Dynamic Experiments: A Data-Driven Methodology for the Optimization of Time-Varying Processes.” Ind. & Eng. Chem. Res. 52, no. 35 (Sep 2013): 12369-82. https://doi.org/10.1021/ie3035114.
Castaldello, C., P. Facco, F. Bezzo, C. Georgakis, and M. Barolo. “Data-Driven Tools for the Optimization of a Pharmaceutical Process through Its Knowledge-Driven Model.” AIChE Journal 69, no. 4 (Apr 2023): 13 e17925. https://doi.org/10.1002/aic.17925.
Bardooli, A., Y. C. Dong, and C. Georgakis. “Mass and Energy Balance-Assisted Data-Driven Modeling and Optimization of Batch Processes: The Case of a Batch Polymerization Process.” [In English]. Article. Computers & Chemical Engineering 160 (Apr 2022): 16 107701. https://doi.org/10.1016/j.compchemeng.2022.107701.
Discovering the Reaction Stoichiometry
Fromer, J., C. Georgakis, and J. Mustakis. “Toward the Identification of Stoichiometric Models for Complex Reaction Mixtures.” Industrial & Engineering Chemistry Research 62, no. 5 (Sep 19 2023): 2225–39. https://doi.org/10.1021/acs.iecr.2c01231.
Dong, Y. C., C. Georgakis, J. Mustakis, J. M. Hawkins, L. Han, K. Wang, J. P. McMullen, S. T. Grosser, and K. Stone. “Stoichiometry Identification of Pharmaceutical Reactions Using the Constrained Dynamic Response Surface Methodology.” AIChE Journal 65, no. 11 (Nov 2019).
Santos-Marques, J., C. Georgakis, J. Mustakis, and J. M. Hawkins. “From Dynamic Response Surface Models to the Identification of the Reaction Stoichiometry in a Complex Pharmaceutical Case Study.” AIChE J 65, no. 4 (Apr 2019): 1173-85. https://doi.org/10.1002/aic.16515.
For a full list of publications, please visit Google Scholar here.
