Probabilistic modeling and optimization of clean coal technologies: case studies of the externally-fired combined cycle
In previous work, quantitative probabilistic analysis techniques have been applied to the evaluation of advanced clean coal technologies. Probabilistic evaluations enable the consequences of uncertainties in the input parameters to detailed engineering-economic models to be displayed and evaluated. Risk is the probability of an adverse outcome. Probabilistic analysis provides a systematic framework for the evaluation of technological risks. These types of risks include the possibility of poor performance, high emission, and high cost compared to more conventional technologies. By identifying the sources of high risk outcomes, it is possible to prioritize research on new technologies so as to minimize such risks. Furthermore, probabilistic analysis has been applied to compare competing technologies under uncertainty. The existence of uncertainty poses challenges to the optimization of advanced power generation and environmental control technologies. Probabilistic modeling and mathematical programming techniques for optimization are combined to optimize process flowsheets under uncertainty. A stochastic optimization capability enables to optimization of a flowsheet so as to maximize favorable outcomes and to minimize risks. Probabilistic analysis and optimization techniques are applied to case studies using an engineering-economic model of the Externally-Fired Combined Cycle (EFCC) technology. The case studies illustrate the key insights obtained from a probabilistic approach to analysis and optimization.
Proceedings of the Air &Amp; Waste Management Association'S Annual Meeting &Amp; Exhibition