Optimization under Uncertainty for Water Consumption in a Pulverized Coal Power Plant [electronic resource].
- Washington, D.C. : United States. Dept. of Energy, 2009.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
- Additional Creators:
- National Energy Technology Laboratory (U.S.)
United States. Department of Energy
United States. Department of Energy. Office of Scientific and Technical Information
- Pulverized coal (PC) power plants are widely recognized as major water consumers whose operability has started to be affected by drought conditions across some regions of the country. Water availability will further restrict the retrofitting of existing PC plants with water-expensive carbon capture technologies. Therefore, national efforts to reduce water withdrawal and consumption have been intensified. Water consumption in PC plants is strongly associated to losses from the cooling water cycle, particularly water evaporation from cooling towers. Accurate estimation of these water losses requires realistic cooling tower models, as well as the inclusion of uncertainties arising from atmospheric conditions. In this work, the cooling tower for a supercritical PC power plant was modeled as a humidification operation and used for optimization under uncertainty. Characterization of the uncertainty (air temperature and humidity) was based on available weather data. Process characteristics including boiler conditions, reactant ratios, and pressure ratios in turbines were calculated to obtain the minimum water consumption under the above mentioned uncertainties. In this study, the calculated conditions predicted up to 12% in reduction in the average water consumption for a 548 MW supercritical PC power plant simulated using Aspen Plus. Optimization under uncertainty for these large-scale PC plants cannot be solved with conventional stochastic programming algorithms because of the computational expenses involved. In this work, we discuss the use of a novel better optimization of nonlinear uncertain systems (BONUS) algorithm which dramatically decreases the computational requirements of the stochastic optimization.
- Published through SciTech Connect.
AIChE Tuesday, November 10, 2009: 3:40 PM Bayou E (Gaylord Opryland Hotel).
Stephen E. Zitney; Juan M. Salazar; Urmila Diwekar.
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