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The Lawrence K. Cecil Award in Environmental Chemical Engineering recognizes an individual's outstanding chemical engineering contribution and achievement in the preservation or improvement of the environment. The recipient must be a member of AIChE, have 15 years of chemical engineering experience in the environmental field, and demonstrate leadership in research, teaching, engineering, or regulatory activities in either the public or private sector. The award is presented annually by the Environmental Division. This paper was prepared by the 2011 Cecil Award recipient based on work presented at the AIChE national meeting in Minneapolis.

Coal-fired power plants are large water consumers. Water consumption in thermoelectric generation is strongly associated with evaporation losses and makeup streams on cooling and contaminant removal systems. Thus, minimization of water consumption requires optimal operating conditions and parameters, while fulfilling the environmental constraints. Several uncertainties affect the operation of the plants, and this work studies those associated with weather. Air conditions (temperature and humidity) were included as uncertain factors for pulverized coal (PC) power plants. Optimization under uncertainty for these large-scale complex processes with black-box models cannot be solved with conventional stochastic programming algorithms because of the large computational expense. Employment of the novel better optimization of nonlinear uncertain systems (BONUS) algorithm, dramatically decreased the computational requirements of the stochastic optimization. Operating conditions including reactor temperatures and pressures; reactant ratios and conditions; and steam flow rates and conditions were calculated to obtain the minimum water consumption under the above-mentioned uncertainties. Reductions of up to 6.3% in water consumption were obtained for the fall season when process variables were set to optimal values. Additionally, the proposed methodology allowed the analysis of other performance parameters like gas emissions and cycle efficiency which were also improved.

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An increasing population and electricity demand in the U.S. require capacity expansion of power systems. The National Energy Technology Laboratory (NETL), U.S. Department of Energy (DOE), has invested considerable efforts on research and development to improve the design and simulation of these power plants. Incorporation of novel process synthesis techniques and realistic simulation methodologies yield optimal flowsheet configurations and accurate estimation of their performance parameters. To provide a better estimation of such performance indicators, simulation models should predict the process behavior based on not only deterministic values of well-known input parameters but also uncertain variables associated with simulation assumptions. In this work, the stochastic simulation of a load-following pulverized coal (PC) power plant takes into account the variation of three input variables, namely, atmospheric air temperature, atmospheric air humidity, and generation load. These uncertain variables are characterized with probability density functions (pdfs) obtained from available atmospheric and electrical energy generation data. The stochastic simulation is carried out by obtaining a sample of values from the pdfs that generates a set of scenarios under which the model is run. An efficient sampling technique [Hammersley sequence sampling (HSS)] guarantees a set of scenarios uniformly distributed throughout the uncertain variable range. Then, each model run generates results on performance parameters as cycle efficiency, carbon emissions, sulfur emissions, and water consumption that are statistically analyzed after all runs are completed. Among these parameters, water consumption is of importance because an increasing demand has been observed mostly in arid regions of the country and, therefore, constrains the operability of the processes. This water consumption is significantly affected by atmospheric uncertainties. The original deterministic process model simulation was designed in Aspen Plus, and a CAPE-OPEN compliant stochastic simulation capability is employed to run the uncertainty analysis. Initially, the influences of atmospheric conditions and load change on the performance parameters are analyzed separately to understand their individual influences on the process, and then their simultaneous variation is analyzed to generate more realistic estimations of the process performance.