Process Cybernetics, also known as ProCyber, provides consulting services to the Pharmaceutical and Specialty Chemical industries. We assist our clients in designing informative Dynamic Experiments, estimating Dynamic Models from the collected data, and using these models for process insight and optimization. In the case of a complex reaction system, we can identify the underlying reaction stoichiometry and estimate accurate kinetic models.
Process Cybernetics, LLC
Professor Christos Georgakis
Principal
In 2021, Professor Christos Georgakis founded Process Cybernetics LLC to enable the industrial deployment of two innovative methodologies that enable the data-driven modeling and optimization of batch, semi-batch, and continuous processes.
Professor Georgakis has 50 years of experience as an educator, researcher, and industry consultant in Process Systems Engineering. Over his multiyear academic career, he has collaborated widely with many industrial companies. Between 1982 and 2001, he founded and directed at Lehigh University the Industry-University cooperative NSF-funded Research Center in Chemical Process Modeling and Control.
There, more than 15 international corporations funded his research, including companies from the US, France, and Japan. In recent years he has collaborated on research tasks with Sunovion Pharmaceuticals, ExxonMobil, Dow, Pfizer, and Merck. He has consulted with many industrial enterprises.
More info
Biographical Sketch
His academic career started in 1975, as du Pont Assistant Professor and Edgerton Associate Professor of Chemical Engineering at MIT. Subsequently, he was a Professor of Measurement and Control at the University of Thessaloniki in Greece, where he initiated the Chemical Process Engineering Research Institute.
He joined Lehigh University in 1983, where he founded and directed the Chemical Process Modeling and Control Research Center, an NSF-funded Industry-University Cooperative Research Center. Lehigh honored him in 2001 with the Iaccoca Professorship.
After two years as the Othmer Distinguished Professor at the Polytechnic University in New York City, he moved to Tufts in 2004.
His research activities have been recognized by a multitude of awards both nationally and internationally. He was recognized with a Dreyfus Foundation Teacher-Scholar Grant in 1978, and in 2001 he was the recipient of the Computing Award of the CAST Division of the American Institute of Chemical Engineers.
He is a fellow of the American Institute of Chemical Engineers, the American Association for the Advancement of Science, and the International Federation of Automatic Control (IFAC). In 2002-03, he served as the President of the American Automatic Control Council. In 2012, he initiated a new series of conferences called Future Innovation in Process Systems Engineering (FIPSE).
Research Activities
- Data-Driven and Hybrid Models of Batch and Continuous Processes
- Machine Learning Applications in Process Systems Engineering
- Process Optimization and Control
- Nonlinear Multivariable Control
- Process Identification
- Statistical Process Control
- Interaction of Process Design and Control
Publications
DRSM Applications
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). https://doi.org/ARTN e1672610.1002/aic.16726.
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.
Honors And Awards
-
Distinguished Senior Scholar Award, Tufts University, 2017
-
Fellow of the International Federation of Automatic Control, 2007
-
Gordon Senior Faculty Fellow in Systems Engineering, Tufts University, 2007-
-
Fellow of the American Association for the Advancement of Science (AAAS) 2005
-
Othmer Distinguished Professor, Polytechnic University of NY, 2002-2003
-
Iacocca Professorship, Lehigh University, 2001-2002
-
Computing in Chemical Engineering Award; CAST Division of the AIChE, 2001
-
Hugo Schuck Best Paper Award; American Automatic Control Council, 1998
-
Fellow, American Institute of Chemical Engineers (AIChE), 1997
-
Affiliate Fellow, Foundation for Research and Technology-Hellas (FORTH), 1995
-
Dreyfus Foundation Teacher-Scholar, 1979-1983
-
Edgerton Professorship, 1977-1979; Massachusetts Institute of Technology
-
du Pont Professorship, 1975-1976, Massachusetts Institute of Technology
-
Phi Lambda Upsilon, 1970; Sigma Xi, 1988
-
Second Prize, Greek Mathematical Society, 1965
Education
- Ph.D., 1975, Chemical Engineering, U. of Minnesota
- M.S., 1972, Chemical Engineering, U. of Illinois
- Chem.Eng. Diploma, 1970, Chemical engineering, National Technical University, Athens, Greece
Process Cybernetics LLC has been established by Professor Christos Georgakis to promote the rapid deployment of some very significant innovative technological ideas in the estimation of data-driven models for batch, semi-batch, and continuous processes. Professor Georgakis is the Principal of Process Cubernetics LLC or ProCyber, for brief.
The above mentioned technological innovations are DoDE for the design of dynamic experiments and DRSM for the effective modeling of time-resolved data. Details about these technologies are provided throughout the pages of this site. There, substantial information is also given about present and past collaboration with leading companies who have effectively put to practice our ideas.
Professor Georgakis has 50-years of experience as an educator and researcher in the area of Process Systems Engineering. He graduates in 1070 with a Chemical Engineering Diploma from the National Technical University of Athens. In 1972 he received his M.S. degree in Chem. Eng. from the U. of Illinois and in 1975 his Ph.D. from the U. of Minnesota both in Chem. Eng. He started his academic career as a faculty at MIT in 1975. There he served as du Pont Assistant Professor and as Edgerton Associate Professor.
Over his multiyear academic career, he has collaborated widely with many industrial companies. Between 1982 and 2001 he founded and directed the Chemical Proces Modeling and Control Research Center at Lehigh Univesity. There more than 15 international corporations funded his research, including companies from the US, France, and Japan. Is recent years he has collaborated on research tasks with Sunovion Pharmaceuticals, ExxonMobil, Dow, Pfizer, and Merck. He has consulted with many industrial enterprises. More information about Dr. Georgaks is available here.
Process Cybernetics LLC has been established by Professor Christos Georgakis to promote the rapid deployment of some very significant innovative technological ideas in the estimation of data-driven models for batch, semi-batch, and continuous processes. Professor Georgakis is the Principal of Process Cubernetics LLC or ProCyber, for brief.
The above mentioned technological innovations are DoDE for the design of dynamic experiments and DRSM for the effective modeling of time-resolved data. Details about these technologies are provided throughout the pages of this site. There, substantial information is also given about present and past collaboration with leading companies who have effectively put to practice our ideas.
Professor Georgakis has 50-years of experience as an educator and researcher in the area of Process Systems Engineering. He graduates in 1070 with a Chemical Engineering Diploma from the National Technical University of Athens. In 1972 he received his M.S. degree in Chem. Eng. from the U. of Illinois and in 1975 his Ph.D. from the U. of Minnesota both in Chem. Eng. He started his academic career as a faculty at MIT in 1975. There he served as du Pont Assistant Professor and as Edgerton Associate Professor.
Over his multiyear academic career, he has collaborated widely with many industrial companies. Between 1982 and 2001 he founded and directed the Chemical Proces Modeling and Control Research Center at Lehigh Univesity. There more than 15 international corporations funded his research, including companies from the US, France, and Japan. Is recent years he has collaborated on research tasks with Sunovion Pharmaceuticals, ExxonMobil, Dow, Pfizer, and Merck. He has consulted with many industrial enterprises. More information about Dr. Georgaks is available here.
