Uranium, thorium, and potassium analyses using pXRF spectrometry

The application of portable XRF spectrometry (pXRF) for determining concentrations of uranium (U), thorium (Th) and potassium (K) was evaluated using a combination of 12 Certified Reference Materials, 17 Standard Reference Materials, and 25 rock samples collected from areas of known U occurrences or mineralization. Samples were analysed by pXRF in Soil, Mining Cu/Zn and Mining Ta/Hf modes. Resulting pXRF data were compared to published recommended values, obtained by total or near total digestion methods with ICP-MS and ICP-OES analysis. Results for pXRF show a linear relationship, for thorium, potassium, and uranium (<5000 ppm U) as compared to the recommended concentrations. However, above 5000 ppm U, pXRF results show an exponential relationship with under reporting of pXRF concentrations compared to recommended values. Accuracy of the data can be improved by post-analysis correction using linear regression equations for potassium and thorium, and samples with <5000 ppm uranium; an exponential correction curve is required at >5000 ppm U. In addition, pXRF analyses of samples with high concentrations of uranium (e.g. >1 wt.% U) significantly over-estimated potassium contents as compared to the published values, indicating interference between the two elements not calibrated by the manufacturer software.

Profile Loss Prediction for High Pressure Steam Turbines

A method is presented for predicting the energy loss for a 2D turbine cascade blade operating in subsonic regions where the exit Mach number ≤ 0.8. A prediction method based on entropy creation was used to analyze the cascade profile loss mechanism. The basic profile loss model was introduced from the isentropic Mach number distribution along the blade surface and the trailing edge loss model was introduced from available test data, CFD results and available loss models. In addition, the Reynolds number correction curve was applied from previous research. Linear cascade test datasets which represent hub, mid-span and tip sections were used to validate this loss model.

A Comparative Study of Different Methods of Correcting Combined Cycle Thermal Performance

The power output and heat rate (or efficiency) of a combined cycle power plant are expressed in the Power Industry at a specified set of “reference conditions”. Some of these reference conditions pertain to the test boundary (eg. ambient air temperature, barometric pressure etc.) while some others pertain to the operating condition (eg. baseload, evaporative cooler status, etc.) within the plant boundary. The process of measuring the actual thermal performance of a combined cycle plant involves conducting a test wherein the plant is operated at the pre-determined set of operating conditions that enable minimizing deviations from the “reference conditions”. It is a well-known fact that despite all efforts made during such a test, the actual boundary and operating conditions that prevail at the time of the test will not necessarily be identical to the pre-defined set of “reference conditions”. Hence, in order to evaluate the performance levels of the plant, one of the essential steps in the testing process is to “correct” the measured power output and heat consumption (or heat rate) for differences that persist between the actual test conditions and the corresponding set of “reference conditions”. This “correction” can be performed by using either a correction curve-based approach or a model-based approach. When a correction curve-based approach is used, the effects of the boundary conditions on the relevant performance parameter (output, heat consumption or heat rate) can be depicted as an additive correction term or as a multiplicative correction term. As such, the corrections to the boundary conditions can be applied as either a) additive or b) multiplicative or c) a combination of additive and multiplicative referred to as “hybrid”. The prevailing industry code for testing combined cycle power plants, ASME PTC 46, has adopted the “hybrid” method while the codes for testing individual equipment (such as PTC 22, PTC 6.2, PTC 6) have adopted either the additive philosophy or the multiplicative philosophy or a “hybrid” philosophy similar to PTC 46. The purpose of this paper is to present the outcome of a study that compares the three different correction methods utilizing the correction curve approach for a combined cycle power plant. The studies were based on thermodynamic simulations performed on different plant configurations. A key result will be the quantification of the errors associated with the different methods, which are primarily a function of the ability of the different methods to inherently capture the interactions between the various boundary parameters in the correction process and are a representation of the uncertainty associated with the particular correction method. Furthermore, the paper will introduce a new calculation method and provide recommendations that will help improve the accuracies of test results.

Research on Heat and Power Load Optimizing Distribution for Heat Supply Unit

According to the thermal experiment data and correction curve of heat supply units, the mathematical analysis formula of thermodynamic characteristic has been given accurately in this paper. Moreover, the mathematical model with objective function and the constraint condition for parallel operation of several heat supply units has been built up. By using simplex method and improved SCDD(Synthesized Constrained Dual Descent) method on the basis of the mathematical model, optimal distribution of heat and power load between heat supply units has been realized, and result indicates that this model is reliable and may achieve the goal of energy conservation.

Quantifying Correction Curve Uncertainty Through Empirical Methods

The accuracy of a thermal performance test is typically estimated by performing an uncertainty analysis calculation in accordance with ASME PTC 19.1 or equivalent. The resultant test uncertainty estimate is often used as a key factor in the commercial contract, in that many contracts allow a test tolerance and define the test tolerance to be equal to the test uncertainty. As such, the calculated test uncertainty needs to accurately reflect all of the technical factors that contribute to the uncertainty. The test uncertainty is a measure of the test quality, and, in many circumstances, the test setup must be designed such that the uncertainty remains lower than test code limits and/or commercial tolerances. Traditional uncertainty calculations have included an estimate of the measurement uncertainties and the propagation of those uncertainties to the test result. In addition to addressing measurement uncertainties, ASME PTC 19.1 makes reference to other potential errors of method, such as “the assumptions or constants contained in the calculation routines” and “using an empirically derived correlation”. Experience suggests that these errors of method can in some circumstances dominate the overall test uncertainty. Previous studies (POWER2011-55123 and POWER2012-54609) introduced and quantified a number of operational factors and correction curve factors of this type. To facilitate testing over a range of boundary conditions, the industry norm is for the equipment supplier to provide correction curves, typically created using thermodynamic models of the power plant to predict the response of the system to changes in boundary conditions. As noted in various PTC codes (PTC-22, PTC-46, and PTC-6) it is advisable to run the test at conditions as close to the rated conditions as possible to minimize the influence of the correction curves. Experience suggests that large deviations from rated conditions, and the associated influence of the correction curves, can result in decreased accuracy in the final corrected result. A discussion of these types of situations via case studies is discussed, as well as a means by which to reduce the uncertainty contributions from correction curves considerably.

Comparison of ASME PTC-46 Performance Test Correction Factors as Estimated in a Project Proposal Phase to Those Determined During Project Implementation

Accurate performance correction equations are essential to the successful implementation of an initial performance test of a new unit, and to continually monitor performance in a meaningful way. Developers of these formulations must consider the latest design information of all major equipment in the cycle. Per Section 5.3.5 of ASME PTC 46–1996 [Ref. 1], these corrections are to be developed from a heat balance computer model after it is “finalized following purchase of all major equipment and receipt of performance information from all vendors.” This paper reviews the requirements for the development of accurate correction curve/factor formulations for a typical combined cycle power plant, and demonstrates how significantly skewed the results of a test can be if assumptions on equipment design performance are made prior to manufacturers’ final submittals.