(369m) An Efficient Stochastic Programming Framework for Studying the Impact of Seasonal Variation On the Water Consumption of Pulverized Coal (PC) Power Plants Conference: AIChE Annual MeetingYear: 2010Proceeding: 2010 AIChE Annual MeetingGroup: Computing and Systems Technology DivisionSession: Poster Session Area 10B Systems and Control Time: Wednesday, November 10, 2010 - 6:00pm-8:00pm Authors: Salazar, J. M., Vishwamitra Research Institute, Center for Uncertain Systems: Tools for Optimization and Management Diwekar, U., Vishwamitra Research Institute, Center for Uncertain Systems: Tools for Optimization and Management Construction and operability of pulverized coal (PC) power plants have started to be constrained by water availability in some regions of the country because these processes are major water consumers. Research efforts have been intensified to reduce the water usage and consumption which are closely related to the water losses associated to the cooling systems and gas purification operations. These parts of the power plant are strongly affected by uncertain variables like atmospheric conditions such as air temperature and humidity which present large seasonal variations. Thus, minimization of water consumption can be formulated as a stochastic programming problem that needs to be solved according to the seasonal variations of the named uncertainties. A complete solution of this problem requires accurate models of both the process and the cooling tower along with a stochastic simulation framework. The outputs of these stochastic simulations are probability distributions of the objective function (water consumption) whose moments (expected value or standard deviation) can be optimized by a non-linear programming (NLP) solver. These are very computationally intensive calculations since the stochastic simulation has to be run several times as the NLP solver modifies each of the policy variables. The employment of better optimization of nonlinear uncertain systems (BONUS) algorithm dramatically decreased the computational requirements of the stochastic optimization and therefore it was applied for different seasons and different ranges of the decision variables. The calculated conditions for a 550 MW PC plant predicted reductions of 6.4%, 3.2%, 3.8% and 15.4% in water consumption for the four different seasons from fall to summer respectively.