Sample‐based approaches to decision making problems under uncertainty

Abstract

Decision making under uncertainty is becoming more important in process industries as optimisation is applied to novel applications as well as plant‐wide and enterprise optimisation. Among the standard stochastic optimisation techniques are stochastic programming and dynamic programming. It is difficult to use these techniques for practical applications due to unwieldy computational requirements, arising from a large number of uncertain parameters and state variables, respectively. In this paper, we present sample‐based techniques for ameliorating the computational difficulties. Application studies involving catalyst design and real‐time optimisation point to the promising potentials of the sample‐based techniques. © 2011 Canadian Society for Chemical Engineering

Computational design of heterogeneous catalysts and gas separation materials for advanced chemical processing

“…Based on the microkinetic model, a new Cu/Ag alloy catalyst that is more selective than Ag was identified. Considering the possible uncertainty in the microkinetic model, Lee et al [6] proposed an efficient method to design catalysts where the uncertainties associated with experimental data are represented as exogenous variables with assumed probability distributions. The method has been successfully applied to the ammonia decomposition reaction where the binding energies of nitrogen and hydrogen, as the key model parameters, were optimized to maximize the reaction conversion.…”