(577h) Minimization Under Uncertainty of Fresh Water Consumption for Pulverized Carbon (PC) and Integrated Gasification Combined-Cycle (IGCC) Power Plants | AIChE

(577h) Minimization Under Uncertainty of Fresh Water Consumption for Pulverized Carbon (PC) and Integrated Gasification Combined-Cycle (IGCC) Power Plants



Conventional coal-fired power plants are significant consumers of fresh water for cooling and production purposes. However, recent power demand increments and environmental concerns have constrained the fresh water usage and consumption. These constraints affect the efficiency and productivity of the power plants due to the close relationship between power generation and water consumption. Additionally, new technologies (including carbon sequestration units) entail considerable augmentation of the already high water consumption of thermoelectric processes. Water loss is strongly affected by uncertain variables like atmospheric conditions and novel technological configurations of the cooling systems. Thus, optimal operating conditions and parameters, subjected to uncertain variations, are necessary to minimize the water consumption while fulfilling the required generation levels and the environmental regulations. Pulverized Carbon (PC) and Integrated Gasification Combined-Cycle (IGCC) plants including and excluding carbon sequestration units are studied. A conventional stochastic nonlinear programming approach for this optimization problem is computationally expensive due to the complexity of the required models for the processes. Comprehensive Aspen Plus simulation models are necessary to ensure appropriate closure of the mass and energy balances and to consider all the cooling system losses (makeups and blowdowns) and process consumption losses (humidification and reactions) to be minimized. Uncertainties are determined, characterized, and included in the Aspen Plus model generating the stochastic simulation of the processes. Employment of the better optimization of nonlinear uncertain systems (BONUS) algorithm decreases the computational requirements of the stochastic optimization.