# Chrysanthos E. Gounaris

## Assistant Professor of Chemical Engineering

**Office:**Doherty Hall 3107

**Phone:**412-268-6974

**Fax:**412-268-7139

**Email:**gounaris@cmu.edu

## Bio

Professor Chrysanthos Gounaris received a Dipl. in Chemical Engineering (2002) and an M.Sc. in Automation Systems (2003) from the National Technical University of Athens. He then attended Princeton University, where he earned an M.A. (2005) and a Ph.D. in Chemical Engineering (2008). His doctoral thesis, pursued under the supervision of Professor Chris A. Floudas, explored the use of nonlinear modeling and global optimization techniques to study porous materials. After graduation, Professor Gounaris joined McKinsey & Co. as an Associate, where he provided consultation to petrochemical, pharmaceutical and consumer packaged-goods companies on a variety of projects of operational and strategic nature (2008-2010). He returned to academia to pursue post-doctoral research at Princeton University (2010-2013), after which he joined the Department of Chemical Engineering at Carnegie Mellon University as an Assistant Professor (2013-).## Education

2008, Princeton University, Ph.D. in Chemical Engineering

2005, Princeton University, M.A. in Chemical Engineering

2003, National Technical University of Athens, M.Sc. in Automation Systems

2002, National Technical University of Athens, Dipl. in Chemical Engineering

## Research

Research in the Gounaris group contemplates the development of theory, quantitative methodologies and software implementations to address problems of industrial and scientific interest that involve complex decision-making. The research pertains to a wide spectrum of application areas, ranging from how process industries conduct their operations in a lean and efficient manner to how advanced materials are synthesized for optimal performance to how utility networks are developed in ways that minimize overall energy consumption.

Process Operations

Many decision problems arise in the context of managing a process industrial enterprise. Typically, these problems involve the selection of the best investments (e.g., selecting the site to build a new plant) or the reduction of variable costs (e.g., minimizing energy consumption during production, optimizing supply-chain operations). The group develops mathematical models and associated optimization algorithms to address questions of this nature. Example areas of focus include production scheduling and the distribution of finished goods, interim products and raw materials. From a methodological perspective, the focus is on both exact and metaheuristic algorithms, including innovative hybrid schemes that exploit the strengths of both approaches, while significant effort is spent on addressing the effect of operational uncertainty so as to enable rigorous risk management in these contexts.

Materials Design

The technological explosion in Materials Science has set the stage with a number of interesting decision-making problems. The continuous discovery of new materials as well as the compilation of large computer-generated databases of hypothetical structures has created the need for computational tools that can quickly screen the various options and match materials to processes. At the same time, advances in our ability to fabricate materials with finely-tuned microstructures has created the need for rigorous design tools to select the specific microstructures that maximize material performance. The group focuses on developing accurate correlations between the structure of materials and their functionality and stability in an application setting, as well as on how to effectively use these correlations in designing optimal structures and the processes to fabricate them. Material classes of interest include microporous materials, which are used abundantly in the process industry as adsorbents and membranes, as well as metallic and metal-oxide surfaces and nanoparticles for use in various catalysis, chemical looping, semiconductor and optics applications.

Networks Optimization

Large and complex networks arise in a multitude of fields; from cell metabolism and epidemiology to traffic management and the Internet, to name but a few. Understanding network behavior via detailed mathematical modeling and simulation has traditionally helped us elucidate how a network's structure affects its performance. However, little work has been done in identifying the very network topologies that shall enable the network to behave in a most desirable manner. To further compound the complexity of the task, at the time a network is designed there usually exists significant uncertainty about how the network shall be operated in practice. In this context, the group develops rigorous quantitative techniques to design networks that exhibit satisfactory performance across the envelope of expected operational conditions. Applications of interest include the design of optimal pipeline networks that retain their economic viability and environmental sustainability over a wide range of potential demand profiles and over their operational lifetime, as well as the generation of small "in-silico" model networks that mimic the behavior of large existing networks (e.g., the US electricity grid) in order to aid us in studying the latter.

Research Websites

Center for Advanced Process Decision-Making

Process Systems Engineering

Wilton E. Scott Institute for Energy Innovation

## Highlights

- AIChE Programming Coordinator, Area 10C, 2018
- Associate Editor, Journal of Global Optimization, 2014-present
- Associate Editor, Optimization Letters, 2012-present
- Member of the Organizing Committee, ODYSSEUS-2012, 5th International Workshop on
- Freight Transportation and Logistics, 2012

## Select Awards and Honors

- Glover-Klingman Prize, 2016
- Gordon Wu Prize for Excellence, 2008
- Excellence in Teaching Award, Princeton University, 2005
- Stanley J. Seeger Fellowship, 2003-2008

## Publications

Recent Publications

Selected Publications

Full Publications

### Recent Publications

N.H. Lappas and C.E. Gounaris (2017). Robust Optimization for Decision-making under Endogenous Uncertainty. Under Review. E-print available at: http://www.optimization-online.orgA. Subramanyam, C.E. Gounaris and W. Wiesemann (2017). K-Adaptability in Two-Stage Mixed-Integer Robust Optimization. Under Review. E-print available at: http://www.optimization-online.org

A. Subramanyam, F. Mufalli, J. Pinto and C.E. Gounaris (2017). Robust Multi-Period Vehicle Routing Under Customer Order Uncertainty. Under Review. E-print available at: http://www.optimization-online.org

### Selected Publications

A. Subramanyam and C.E. Gounaris (2017). A Decomposition Algorithm for the Consistent Traveling Salesman Problem with Waiting Times. Transportation Science, Articles In Advance. DOI 10.1287/trsc.2017.0741C.E. Gounaris, K. Rajendran, I.G. Kevrekidis and C.A. Floudas (2016). Designing Networks: A Mixed-Integer Linear Optimization Approach. Networks, 68(6):283-301. Winner of the 2016 Glover-Klingman Prize. DOI 10.1002/net.21699

C.L. Hanselman and C.E. Gounaris (2016). A Mathematical Optimization Framework for the Design of Nanopatterned Surfaces. AIChE Journal, 62(9):3250-3263. DOI 10.1002/aic.15359

N.H. Lappas and C.E. Gounaris (2016). Multi-stage Adjustable Robust Optimization for Process Scheduling Under Uncertainty. AIChE Journal, 62(5):1646-1667. Selected as Editor’s Choice Paper. DOI 10.1002/aic.15183

### Full Publications

30. N.H. Lappas and C.E. Gounaris (2017). Robust Optimization for Decision-making under Endogenous Uncertainty. Under Review. E-print available at: http://www.optimization-online.org

29. A. Subramanyam, C.E. Gounaris and W. Wiesemann (2017). K-Adaptability in Two-Stage Mixed-Integer Robust Optimization. Under Review. E-print available at: http://www.optimization-online.org

28. A. Subramanyam, F. Mufalli, J. Pinto and C.E. Gounaris (2017). Robust Multi-Period Vehicle Routing Under Customer Order Uncertainty. Under Review. E-print available at: http://www.optimization-online.org

27. A. Subramanyam and C.E. Gounaris (2017). A Decomposition Algorithm for the Consistent Traveling Salesman Problem with Waiting Times. Transportation Science, Articles In Advance. DOI 10.1287/trsc.2017.0741

26. C.E. Gounaris, K. Rajendran, I.G. Kevrekidis and C.A. Floudas (2016). Designing Networks: A Mixed-Integer Linear Optimization Approach. Networks, 68(6):283-301. Winner of the 2016 Glover-Klingman Prize. DOI 10.1002/net.21699

25. C.E. Gounaris, P.P. Repoussis, C.D. Tarantilis, W. Wiesemann and C.A. Floudas (2016). An Adaptive Memory Programming Framework for the Robust Capacitated Vehicle Routing Problem. Transportation Science, 50(4):1239-1260. DOI 10.1287/trsc.2014.0559

24. C.L. Hanselman and C.E. Gounaris (2016). A Mathematical Optimization Framework for the Design of Nanopatterned Surfaces. AIChE Journal, 62(9):3250-3263. DOI 10.1002/aic.15359

23. N.H. Lappas and C.E. Gounaris (2016). Multi-stage Adjustable Robust Optimization for Process Scheduling Under Uncertainty. AIChE Journal, 62(5):1646-1667. Selected as Editor’s Choice Paper. DOI 10.1002/aic.15183

22. A. Subramanyam and C.E. Gounaris (2016). A Branch-and-Cut Framework for the Consistent Traveling Salesman Problem. European Journal of Operational Research, 248(2):384-395. DOI 10.1016/j.ejor.2015.07.030

21. M.G. Taylor, N. Austin, C.E. Gounaris and G. Mpourmpakis (2015). Catalyst Design Based on Morphology- and Environment-Dependent Adsorption on Metal Nanoparticles. ACS Catalysis, 5(11):6296-6301. DOI 10.1021/acscatal.5b01696

20. C.E. Gounaris, E.L. First and C.A. Floudas (2013). Estimation of Diffusion Anisotropy in Microporous Crystalline Materials and Optimization of Crystal Orientation in Membranes. The Journal of Chemical Physics, 139(12):124703. DOI 10.1063/1.4821583

19. C.E. Gounaris, W. Wiesemann and C.A. Floudas (2013). The Robust Capacitated Vehicle Routing Problem Under Demand Uncertainty. Operations Research, 61(3):677-693. DOI 10.1287/opre.1120.1136

18. E.L. First, C.E. Gounaris and C.A. Floudas (2013). Predictive Framework for Shape-Selective Separations in Three-Dimensional Zeolites and Metal-Organic Frameworks. Langmuir, 29(18):5599-5608. DOI 10.1021/la400547a

17. E.L. First, C.E. Gounaris and C.A. Floudas (2012). Stereochemically-consistent Reaction Mapping and Identification of Multiple Reaction Mechanisms through Integer Linear Optimization. Journal of Chemical Information and Modeling, 52(1):84-92. DOI 10.1021/ci200351b

16. E.L. First, C.E. Gounaris, J. Wei and C.A. Floudas (2011). Computational Characterization of Zeolite Porous Networks: An Automated Approach. Physical Chemistry-Chemical Physics, 13(38):17339-17358. DOI 10.1039/c1cp21731c

15. C.E. Gounaris, K. Rajendran, I.G. Kevrekidis and C.A. Floudas (2011). Generation of Networks with Prescribed Degree-Dependent Clustering. Optimization Letters, 5(3):435-451. DOI 10.1007/s11590-011-0319-x

14. R. Misener, C.E. Gounaris and C.A. Floudas (2010). Mathematical Modeling and Global Optimization of Large-Scale Extended Pooling Problems with the (EPA) Complex Emissions Constraints. Computers & Chemical Engineering, 34(9):1432-1456. DOI 10.1016/j.compchemeng.2010.02.014

13. C.E. Gounaris, J. Wei, C.A. Floudas, R. Ranjan and M. Tsapatsis (2010). Rational Design of Shape Selective Separation and Catalysis: Lattice Relaxation and Effective Aperture Size. AIChE Journal, 56(3):611-632. DOI 10.1002/aic.12016

12. H.C. Lu, H.L. Li, C.E. Gounaris and C.A. Floudas (2010). Convex Relaxation for Solving Posynomial Programs. Journal of Global Optimization, 46(1):147-154. DOI 10.1007/s10898-009-9414-2

11. J. Wei, C.A. Floudas and C.E. Gounaris (2009). Search Engines for Shape Selectivity. Catalysis Letters, 133(1-2):234-241. DOI 10.1007/s10562-009-0155-0

10. C.A. Floudas and C.E. Gounaris (2009). A Review of Recent Advances in Global Optimization. Journal of Global Optimization, 45(1):3-38. DOI 10.1007/s10898-008-9332-8

9. R. Ranjan, S. Thust, C.E. Gounaris, M. Woo, C.A. Floudas, M.v. Keitz, K.J. Valentas, J. Wei and M. Tsapatsis (2009). Adsorption of Fermentation Inhibitors from Lignocellulosic Biomass Hydrolyzates for Improved Ethanol Yield and Value-Added Product Recovery. Microporous and Mesoporous Materials, 122(1-3):143-148. DOI 10.1016/j.micromeso.2009.02.025

8. R. Misener, C.E. Gounaris and C.A. Floudas (2009). Global Optimization of Gas Lifting Operations: A Comparative Study of Piecewise Linear Formulations. Industrial & Engineering Chemistry Research, 48(13):6098-6104. DOI 10.1021/ie8012117

7. C.E. Gounaris, R. Misener and C.A. Floudas (2009). Computational Comparison of Piecewise-Linear Relaxations for Pooling Problems. Industrial & Engineering Chemistry Research, 48(12):5742-5766. DOI 10.1021/ie8016048

6. C.E. Gounaris and C.A. Floudas (2008). Convexity of Products of Univariate Functions and Convexification Transformations in Geometric Programming. Journal of Optimization Theory and Applications, 138(3):407-427. DOI 10.1007/s10957-008-9402-6

5. C.E. Gounaris and C.A. Floudas (2008). Tight Convex Underestimators for C2-Continuous Problems: II. Multivariate Functions. Journal of Global Optimization, 42(1):69-89. DOI 10.1007/s10898-008-9288-8

4. C.E. Gounaris and C.A. Floudas (2008). Tight Convex Underestimators for C2-Continuous Problems: I. Univariate Functions. Journal of Global Optimization, 42(1):51-67. DOI 10.1007/s10898-008-9287-9

3. C.E. Gounaris, J. Wei and C.A. Floudas (2006). Rational Design of Shape Selective Separation and Catalysis: II. Mathematical Model and Computational Studies. Chemical Engineering Science, 61(24):7949-7962. DOI 10.1016/j.ces.2006.09.011

2. C.E. Gounaris, C.A. Floudas and J. Wei (2006). Rational Design of Shape Selective Separation and Catalysis: I. Concepts and Analysis. Chemical Engineering Science, 61(24):7933-7948. DOI 10.1016/j.ces.2006.09.012

1. G.D. Bellos, L.E. Kallinikos, C.E. Gounaris and N.G. Papayannakos (2005). Modelling of the Performance of Industrial HDS Reactors using a Hybrid Neural Network Approach. Chemical Engineering and Processing, 44(5):505-515. DOI 10.1016/j.cep.2004.06.008