This paper presents a planning tool where rigorous plant performance models are used to produce accurate plant performance data reflecting real plant operation under the loads specified by load demand curves. Cumulative figures such as total fuel consumption and production efficiency serve as the basis for a subsequent detailed economic analysis. Basic inputs to the model are the energy demand curves for a specific site on a daily, hourly, or even per minute basis. The plant performance model takes into account the varying load levels, ambient conditions, fuel price, and revenues from electric power delivered off-site. The load/performance simulator calculates plant performance and basic economic parameters at each point in time of the load demand curve to exactly match the given energy demand (Fig. 1). The results are critical economic data like cumulated fuel consumption and expenses, total energy production, and overall averaged plant efficiency. The performance model delivers detailed plant performance data on an overall plant as well as component level. The results reflect real plant operation under all varying conditions, hence produce highly significant performance and economic parameters.

The determination of the particle size distribution of a powder may last up to 14 hours with standard sedimentation techniques such as an Andreasen pipette. If, in a photosedimentometer, not only the one sensor close to the bottom is incorporated but a number of sensors positioned at different heights, the time required for determining the particle size distribution of a powder may be reduced to 15 minutes. For each sensor there is an optimum location, which is determined both by the distance from the surface to the lowest and the highest sensor and by the number of sensors. The instrument, equipped with three measuring sensors and a reference sensor is connected to a personal computer. The measured particle size distributions are reproducable with a standard deviation of less than 2%. Agreement with a Sedigraph or a sedimentation balance is about the same as the agreement of these two instruments with each other. The time required for the determination of a particle size distribution of a quartz powder suspended in water with particle diameters from 1.5 to 60 μ is 15 minutes.

Accurate on-line plant and equipment performance evaluation is becoming critical in the power generation industry as operators seek to optimize their plants, particularly in competitive power markets. The analysis accuracy of an on-line performance monitoring system is directly dependent on the quality of the input data and usually suffers because installed plant sensors are not high-precision instruments. The inherent measurement uncertainties can be overcome by using a readily available heat balance program in combination with a least square solver. This data reconciliation system will provide the performance evaluation system with data that better reflects the plant’s current operating point, thus improving the performance evaluation system’s output and allowing for better plant optimization. Additionally, the reconciliation system can identify broken, biased or highly noisy sensors. These improvements can be obtained without installing additional precision sensors or putting unreasonable efforts into sensor calibration.

Method 1 (which is described in the literature, 1'2) yielded MAGSORB pellets with the highest reactivity, but uneven distribution of potassium iii resulted in Mg:K atomic ratios that varied widely among individual pellets. This introduced a large potential error into thermobalance test results. Method 2 yielded denser MAGSORB pellets with acceptable Mg:K intradlstribution, but sharply reduced reactivity. These pellets were approximately 2.4 times as dense as the Method 1 pellets, and had 73% lower pore volume. Method 3 yielded pellets with good homogeneity and reactivity close to that: of the Method 1 formulation. These pellets were 1.5 times as dense as Method 1 pellets and had 40t less total pore volume. MAGSORB pellets made by method 3 were used in most of the parametric thermobalance tests and all of the packed-bed cycle tests. MAGSORB pellets were tested for reaction with COz in a high-pressure, high-temperature thel_obalance for temperature ranges from 650"F to 950"F and system pressures from atmospheric to 600 psig. Thermobalance and differential scanning calorimeter testing of Method 1 pellets showed that COz absorption and desorption rates were negligible below a minimum temperature of about 750"F, but increased rapidly as temperature approached 800°F. Various temperature and pressure combinations for desorption of the CO2 were studied. A sweep gas was required to enact complete desorption at 800°F and atmospheric pressure, lt was found that desorption could occur without a sweep gas if reduction of CO2 partial pressure was accompanied by a temperature increase. For example, CO2 was desorbed by reducing CO2 partial pressure from 139 to 46 psi while increasing temperature from 800°to 900°F. A series of thermobalance tests was performed to show reproducibility in a standard gas mixture averaging 42 volt CO2, 42 volt He, and 16 volt H20. These tests were conducted at 820°to 830"F and 300 psig system pressure, with a CO2 partial pressure of 131 to 134 psi. These tests gave typical asymptotic weight gain curves with a mean CO2 absorption of 70.1% ± 12.2% of the stoichlometric limit after 30 minutes. The stoichiometrlc weight gain limit, based on the Mg, K, and CO 2 analyses of the MAGSORB, represented an absolute weight gain of 76.9%. The error limits given above are for 95% confidence. Thermobalance tests under similar conditions as above, but containing syngas components (H2, CO, N2), steam, H2S, and NH 3 proved that these components did not react with the sorbent or reduce its react _ity. In fact, iv sv the sorbent prepared by Method 3 showed higher CO 2 absorption rates and capacities in the presence of syngas, H2S, or NH 3, with 30-minute weight gains ranging from 86.4% to 96.7% of the stolchlometric limit. Desorption was essentially complete in less than 12 minutes with standard gas with or without HzS or NH 3, but took about 30 minutes in the syngas mixture. A packed-bed reactor was constructed to conduct cycle testing of a selected MAGSORB formulation with a 50 volt CO2/He mixture. The conditions sele...

The evaluation of power plant uprates has traditionally been based on the definition of several ‘typical’ operating modes based on historical data and a — more or less detailed — model of the plant that is compared in current configuration against the same base model including the modifications under consideration. For the economic assessment of the uprate, annual operating hours are allocated to the operating points, and fuel savings and/or additional output predicted by the model due to the modifications are evaluated against the expected investment cost. In this study, the authors demonstrate that this classic approach contains risks in several aspects, in particular: • the representativeness of the ‘typical’ operating modes, • the accuracy of the model, and • the correctness of the assumptions in the allocation of operating hours. Utilizing the example of an actual uprate of a heat recovery steam generator (HRSG) in a large utility plant of an Austrian steel company, a new approach for an evaluation based on ‘big data’ is presented that uses a full year of operational data in hourly granularity for both, the verification of the accuracy of the plant model, and the evaluation of the effect of the uprate. The authors also provide details of the underlying technologies that allow for both, excellent match of operational data with a fully-fledged heat balance software and fast evaluation of tens of thousands of calculation cases.