Routine Sample Reduction for Commercial Nuclear Power

The commercial nuclear power industry is threatened by pricing pressure from cheap natural gas. There is an urgency to reduce operational cost in the industry which is dominated by skilled labor. One labor intensive task is the monitoring of the primary reactor coolant chemistry. This effort aims to address that problem in two ways: by reducing the need for personnel to physically acquire and measure samples and by reducing the need for expert analysis to interpret the results. This is accomplished by developing an analysis toolkit to take advantage of the data products from an advanced new detector system from H3D that is now available commercially. In Phase I a thorough feasibility study was performed including analysis on the relevant data that should be recorded by the monitoring system. Most importantly we extracted more than a year of continuous plant data from a customer to be used to evaluate the capabilities that could be expected with fully automated analysis. We were able to show a variety of subtle spectral features that provided valuable plant data to the operators. We also learned about additional features of interest to plant chemists through this interaction and have rolled those lessons learned into a scope for Phase II. In Phase II we intend to focus on the issues that provide the highest economic benefit to the commercial nuclear power industry. One example is the analysis of their resin beds used to trap radioactive contamination in the primary coolant. We intend to provide the operator with a means of knowing the usage fraction of this resin bed in real time to save money on disposal. We also intend to automate the entire analysis process that is currently achieved by taking water samples, bringing them to a laboratory, counting them, and analyzing results. Finally, we will work with the chemistry experts in the field to interpret the meaning of the measured results and present operationally relevant derived data to the user on an as-needed basis rather than a massive dump of raw data that is meaningless to most people. Commercially, this will be most relevant to commercial nuclear power plants and should help their bottom line by providing societal benefits through energy cost reduction. It will also improve the overall safety of these systems and reduce the long-term radioactive waste burden by identifying problems related to waste generation as early as possible. There will be implications in other markets, such as the US Nuclear Navy and nuclear reprocessing facilities.