Power generating stations are under continuous pressure to achieve maximum availability, highest efficiency, and minimum environmental emissions at the lowest possible cost. In recent years, increased fuel flexibility has become more critical financially and operationally than ever before. Colorado Springs Utilities (CSU) has been very progressive in adopting and implementing benchmark technologies and operating strategies to help achieve these goals across their diversified generation portfolio, and in particular at four operating coal units representing 462 megawatts in the system. One key strategy employed at CSU’s Martin Drake Station has been to continuously evaluate and test alternative coal feedstocks which have potential to reduce cost while maintaining capacity, fuel supply security, availability, and efficiency. These tests would not have been possible without the use of Fuel Tech’s Targeted In-Furnace Injection™ (TIFI®) technology to control slagging and fouling, reduce forced outages and load drops, and enhance unit efficiency. The TIFI process involves the use of two different forms of fluid dynamics modeling coupled with a virtual reality engine. Together, these simulation methods create a running duplicate of a given furnace with injection overlays and dosage maps to predict the precise trajectory of an injected chemical, helping to ensure as close to 100% coverage of the targeted zones as possible. With TIFI installed on Units 6 and 7 at Martin Drake Station, the operators were able to blend Powder River Basin coal with design fuel up to double the percentages previously achievable. Using TIFI, the plant was able to maintain full load generation, better control slagging deposits, show improvements in heat absorption, and reduce attemperator spray flows over previous blend trials. Including the cost of the TIFI program, the station has demonstrated a potential annual operating cost reduction approaching $4.9 million. Effective return on TIFI program investment is 4:1.
Currently flood warning in the catchment of the River Cam in Cambridgeshire relies on the issuing of alerts when the river level at the monitoring station at Byron's Pool, just upstream of Cambridge, reaches certain pre-determined levels. Warnings are shown to be fairly accurate, but there is very little lead time between the trigger being exceeded and the commencement of flooding. At present there is no method used that can forecast in advance when the trigger is likely to be reached. Three conceptually different methods of forecasting if and when the trigger at Byron's Pool will be exceeded are presented. The first of these is a simple additive model, in which flows from the three tributaries that are gauged are summed to give a combined flow. The second method involves the derivation and application of two transfer function models capable of transforming river levels on the upstream tributaries to a level at the trigger site. These models are applied both with and without realtime updating techniques. The third method involves the calibration and application of a lumped rainfall-runoff model of the whole catchment to Byron's Pool. Two different calibration periods are used, and the results compared. The results indicate that the simple additive model, while being better than no model at all, is very inaccurate, and fails to replicate the hydrograph shape and timing, most likely because of the influence of an ungauged tributary. The transfer function models perform well, especially when real-time updating is used. The rainfall-runoff model performs less well, struggling to reproduce the hydrograph shape. Ôhe main conclusions are that for this site a hierarchy of models may be appropriate, with rainfallrunoff models providing an early indication of flooding, and transfer function routing models with updating providing a more accurate forecast, with the additive model as a back up. The importance of obtaining more data, including validation of ratings, and the future gauging of the ungauged tributary, is noted throughout this investigation.
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