| Complex processes in physics, chemistry, biology, engineering and finance, often exhibit uncertainties, or fluctuations, or noises, in their structures as a rule rather than as an exception. To interpret, analyze, model and simulate these uncertainties, algorithms have developed to yield not only the structure or pattern of these processes, but also the statistical characteristics of the evolutions of underlined uncertainties that the conventional deterministic models can not offer. These algorithms have significantly optimized process design and operations. Recent development seems to suggest the needs for efficient solution of large-scale problems and the applicability of the proposed methodologies in realistic case studies. Papers are sought that describe: (i) modeling, algorithmic development and solution procedures for the design and operation under uncertainty, (ii) characterization of system behavior under uncertainty, (iii) evaluation of feasible region of design operation for realistic nonconvex cases, (iv) integration of uncertainty analysis within process design and operations in a unified framework, and (v) reconciliations of the stochastic algorithms developed by engineers with those by chemical physicists. |
Steady-State Optimization with Guaranteed Stability under Parametric Uncertainties
Nick Sahinidis and YoungJung Chang, University of Illinois, Department of Chemical and Biomolecular Engineering, 600 South Mathews Avenue, Urbana, IL 61801 |
Hedging Risk through the Flexible Recipe Framework
Sergio Ferrer-Nadal, Chemical Engineering, Universitat Politècnica de Catalunya, Av. Diagonal, 647, ETSEIB, Pav. G2, Barcelona, Spain, Gonzalo Guillén-Gosálbez, Chem. Enginering, Universitat Politécnica de Catalunya, Diagonal 647, Barcelona, 02808, Spain and Luis Puigjaner, Chemical Engineering Department, Universitat Politècnica de Catalunya - ETSEIB, Diagonal, 647, Barcelona, E-08028, Spain |
Design of Uncertain Discrete Time Systems with Constructive Nonlinear Dynamics Methods
Martin Mönnigmann, Johannes Gerhard and Wolfgang Marquardt, Process Systems Engineering, RWTH Aachen University, Templergraben 55, Aachen, 52056, Germany |
Scheduling under Uncertainty Using Parametric Programming
Zukui Li and Marianthi Ierapetritou, Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ 08854 |
Quantifying Risk in Multistage Stochastic Problems Using Approximate Dynamic Programming and Coherent Risk Measures
Nikolaos Pratikakis, Jay H. Lee and Matthew J. Realff, Chemical and Biomolecular Engineering, Georgia Institute of Technology, Ford Environmental Science & Technology Building 311 Ferst Drive, N.W., Atlanta, GA 30332 |
Propagation of Uncertainties in Nonlinear Dynamic Models
Scott Ferson, Applied Biomathematics, 100 North Country Road, Setauket, NY 11733, Youdong Lin, Chemical and Biomolecular Engineering, University of Notre Dame, 181 Fitzpatrick Hall, Notre Dame, IN 46556, George F. Corliss, Electrical and Computer Engineering, Marquette University, 1515 W. Wisconsin Avenue, Milwaukee, WI 53233 and Mark A. Stadtherr, University of Notre Dame, Department of Chemical and Biomolecular Engineering, 182 Fitzpatrick Hall, Notre Dame, IN 46556 |
Analysis and Evaluation of Batch Chemical Processes under Uncertainty Using Process Simulation and Risk Analysis Tools
Evdokia Achilleos and Demetri P. Petrides, INTELLIGEN, INC., 2326 Morse Avenue, Scotch Plains, NJ 07076 |
A Multi-Parametric Programming Approach for Dynamic Programming & Robust Control
Nuno P. Faísca, Konstantinos I. Kouramas and Efstratios N. Pistikopoulos, Centre for Process Systems Engineering, Imperial College London, Prince consort road, London, SW7 2AZ, United Kingdom |
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