Christodoulos A. Floudas
Stephen C. Macaleer '63 Professor in Engineering and Applied Science
Professor of Chemical and Biological Engineering
Diploma, Aristotle University of Thessaloniki, 1982
Ph.D., Carnegie Mellon University, 1986
Room: A325 Engineering Quad
Webpage: Computer-Aided Systems Laboratory
Honors and Awards
- National Award and HELORS Gold Medal, 2013
- AIChE Fellow 2013
- TIAS Fellow and Eminent Scholar, 2013-14
- SIAM Fellow, 2013
- One Thousand Global Experts, China, 2012
- National Academy of Engineering, 2011
- George T. Piercy Distinguished Visiting Professor, University of Minnesota, 2008
- Graduate Mentoring Award, Princeton University, 2007
- Computing in Chemical Engineering Award, American Institute of Chemical Engineers, 2006
- Professional Progress Award, American Institute of Chemical Engineers, 2001
- Bodossaki Academic Award in Applied Sciences, 1997
- Presidential Young Investigator, National Science Foundation, 1988
Concurrent University Appointments
- Associated Faculty, Department of Operations Research and Financial Engineering
- Associated Faculty, Program in Applied and Computational Mathematics
- Faculty, Center for Quantitative Biology
- Applied and Computational Mathematics
- Process Engineering and Science
- Thermodynamics and Statistical Mechanics
Our research interests are in the area of Chemical Process Systems Engineering and lie at the interface of chemical engineering, applied mathematics, operations research, computer science, and molecular biology. The principal emphasis is on addressing fundamental problems in process synthesis and design, interaction of design and control, process operations, discrete-continuous nonlinear optimization, deterministic global optimization, and computational chemistry, structural biology and bioinformatics. The unified thrust of our research relies on mathematical modelling at the microscopic, mesoscopic or macroscopic level, rigorous optimization theory and algorithms, and large-scale computations on high performance clusters of workstations.
Process synthesis and design. In this area, we aim at developing systematically new processes or modifying existing ones that convert the available raw materials into the desired products, and which meet the specified performance criteria of (i) minimum cost or maximum profit, (ii) energy efficiency, and (iii) good operability with respect to flexibility, controllability, reliability, safety, and environmental regulations. Our approach is based on a mixed-integer nonlinear optimization framework where discrete and continuous decisions are modeled explicitly. Current research work focuses on (a) complex and nonideal separations; (b) rigorous phase equilibrium calculations based on equations of state and activity coefficient models; (c) model uncertainty in process synthesis and design; and (d) the optimal location/allocation problems in exploration processes.
Interaction of design and control. In the interaction of design and control, most process synthesis approaches determine economically optimal processes first, which are then studied for the best selection and pairing of controlled and manipulated variables, and the best closed loop performance. As a result, the important concept that changes in the process design may make the system more controllable is ignored. Our aim is to investigate an approach that establishes the trade-off among the various design and control objectives using the noninferior solution set. Current research work focuses on developing a framework based on detailed differential-algebraic models with continuous and discrete decisions and model uncertainty.
Process operations: scheduling and planning. Our primary objective in Scheduling and Planning is to investigate, refine, and apply effective combinatorial optimization models based on our recently proposed continuous-time framework for short term scheduling of batch, semi-continuous and continuous processes. The thrust of our approach is to develop a unified framework that addresses intermediate due dates and demands, establishes the trade-offs in the design, synthesis and scheduling of multipurpose batch plants, and is directly applicable to large-scale manufacturing processes. Current research focuses on (a) novel methods for medium-term scheduling of manufacturing processes, (b) scheduling under uncertainty in the processing times and product demands, (c) supply chain in crude oil lightering, and (d) novel decomposition-based methods for capacity planning of chemical and exploration processes.
Discrete-continuous nonlinear optimization. Modeling process synthesis problems, as well as fundamental problems in metabolic engineering and secondary structure prediction in protein folding results in mixed-integer linear and nonlinear optimization formulations. In the area of nonlinear discrete-continuous optimization, we have studied modeling and algorithmic issues in approaches based on the principles of Generalized Benders Decomposition, developed the framework MINOPT, and applied the resulting methodologies to the synthesis of separation systems, heat exchanger networks, reactor based systems, mass exchange networks, and analysis and synthesis of metabolic networks (see the graduate textbook Nonlinear Mixed-Integer Optimization by Floudas, 1995). Current emphasis is on investigating new methods for MINLP problems that are based on continuous representations.
Deterministic global optimization. A plethora of the most important problems in science and engineering, ranging from the atomistic domain to large-scale, process-level representations, are described mathematically by functions characterized by the existence of multiple minima and maxima, as well as first-, second-, and higher-order saddle points. The area of Global Optimization is concerned with theoretical, algorithmic and computational advances to address the computation and characterization of global minima and maxima, determine valid lower and upper bounds on the global minima and maxima, and address the enclosure of all solutions of nonlinear constrained systems of equations. The recent textbook Deterministic Global Optimization, (Floudas, 2000) provides a unified and insightful treatment of global optimization. It includes major topics such as process design, synthesis, control and operations, phase equilibrium, design under uncertainty, parameter estimation, azeotrope prediction, structure prediction in protein folding and peptide docking. Current research work focuses on theoretical and algorithmic studies of novel deterministic global optimization methods for (a) bilevel and multilevel nonlinear optimization models, (b) continuous approaches for mixed-integer nonlinear optimization problems, (c) differential algebraic systems, (d) parameter estimation problems, (e) black/grey box models, and (f) models under uncertainty.
Computational chemistry, molecular biology and bioinformatics. The search for the global minimum and low free energy conformations of organic molecules, peptides, and proteins faces the multiple-minima problem, making it as fascinating a subject as it is utterly complex. The most stable molecular conformation dictates, in conjunction with low energy conformations, most of the properties of the organic molecules, peptides and proteins. The thrust of our approach is the unique combination of detailed atomistic level modeling including state of the art solvation methods with rigorous deterministic global optimization methods, mixed-integer optimization, molecular dynamics, and free energy calculations. Current research focuses on (a) novel approaches for the ab initio secondary and tertiary structure prediction of polypeptides and proteins, (b) new methods for the tertiary structure refinement of proteins based on experimental data (e.g., NMR), (c) the dynamics of extended structures to beta sheet formation and the dynamics of protein folding, (d) a predictive framework for the protein-protein interactions and peptide docking problems, (e) novel optimization approaches for the de-novo protein design, and (f) the elucidation of the metabolic pathway mechanisms within yeast cells through a combination of micro DNA experiments and optimization theory.