Pressure signals measured in gas pipelines provide useful information for monitoring the status of pipeline network operations. This study numerically investigates leakage detection and location in a simple gas pipeline branch using transient signals from an array of pressure sensors. A pipeline network simulation model was developed and used to predict one-dimensional compressible flow in gas pipelines. Two monitoring methods were tested for leakage detection and location; these include cross-correlation monitoring and pressure differential monitoring. The performance and reliability of the two monitoring methods were numerically assessed based on simulation results for a simple pipeline branch operation with a prescribed leakage rate (5% of total flow rate in the gas pipeline).
The nuclear dismantling assessment is analyzed with the fuzzy set and deep‐learning algorithm, where the project preference time search (PPTS) has been constructed. Artificial intelligence (AI) management is applied to nuclear decommissioning for the best estimation. The basic data are from three factors, Licensee, NRC, and Public. In the analysis, the highest OUTPUT value is 0.0513151 in the 72nd year, which is the best time for decommissioning, because the confidence value in the 37th year is 78.76% compared to that of the 72nd year. In the other case of membership functions with right‐shift values, the change rate is higher in the 72nd year as being similar to the value in the 37th year near 0.09 in final confidence value. The trend is a new function that shows two peaks compared to the previous one. The other cases could be made by comparing in the interested time. Finally, the list of reactor decommissions processes with numbering is used to find out the very confident time using the final confidence value as the PPTS method.
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