Additives factors which affect performance

But for power station applications, the thermal efficiency is not the only measure of the performance of a plant. While a new type of plant may involve some reduction in running costs due to improved thermal efficiency, it may also involve additional capital costs. The cost of electricity produced is the crucial criterion within the overall economics, and this depends not only on the thermal efficiency and capital costs, but also on the price of fuel, operational and maintenance costs, and the taxes imposed. Yet another factor, which has recently become important, is the production by gas turbine plants of greenhouse gases (mainly carbon dioxide) which contribute to global warming. Many countries are now considering the imposition of a special tax on the amount of CO2 produced by a power plant, and this may adversely affect the economics. So consideration of a new plant in future will involve not only the factors listed above but also the amount of CO2 produced per unit of electricity together with the extra taxes that may have to be paid. 

The crane simulation is also performed within a few seconds. The size and the complexity depend on the problem instance. Since the number of batches is always fixed, the main factor affecting the number of binary variables is the number of maintenance jobs. It should be mentioned that each schedule optimization run also considers two previous batches that are already in production from a resource availability perspective. The initial situation based on the previous batches, defines the complexity. If the production is far from the ideal production cycle, the flexibility may be very low and the main task of the optimization is to increase the total throughput as fast as possible. At this point, the schedule is very sensitive to additional disturbances, which may directly affect the throughput. However, when an optimal production cycle has been reached, a rescheduling optimization may use the existing flexibility (for instance time buffers between the most critical steps) to minimize or to eliminate the throughput decrease caused by disturbances. 

After each of the components has been evaluated and corrected wherever possible, the closed-loop system should be checked. From an overall point of view, there are three general factors that affect the closed-loop performance of a control loop (1) the type and magnitude of disturbances, (2) the lag associated with the components that compose the control loop, and (3) the precision to which each component of the control loop performs. Actuator deadband affects the variability in the controlled variable. The addition of lag to a control loop (e.g., sensor filtering) results in slower disturbance rejection, which can increase the variability in the controlled variable. Disturbance magnitude directly affects variability. 

It can be concluded that there is agreement across the DT models that closeness in time, similarity of cues, similarity of crew/performers, and similarity of location are important factors affecting the dependence level. There is less agreement as to which factors should be additionally included to characterize dependence. Potential additional factors are stress, similarity of procedures, and complexity. Whether or not an intervening success between two HFEs decouples the HFEs is also not agreed on. 

Slip can be described as the ability of a film to slide over itself or another film. It is one of the factors which helps describe how easily films can separate (or bags can be opened). The use of slip additives may affect the performance of other agents (such as anti-static agents). The presence of fillers and pigments may also affect the performance of the slip additive. 

Human reliability analysis (HRA) With technological development and incorporation of redundancy it is possible to reduce equipment failure to a great extent. However, human behavior is not that predictable. So, there are chances that failure could occur because of human factors. This is a method by which probability is measured. It is also used in PFLA. This could be quantitative as well as qualitative. Although the exact value is not certain it is estimated that error committed by a human could be as high as 60—80% (even 90%). Human performance is affected by several factors, referred to as the performance shaping factor (PSF). By this method, PSF is identified and tries to improve it. In addition to PSF, normal human error probability (HEP) is also calculated on the basis of human activity. There are so many factors that affect this analysis accuracy, reproducibility, bias, etc. There have been several methods and each needs to be understood before application. An HRA event tree is often used. It may be informative to refer to Table V/1.0-1 (Chapter V). 

The determination of metals in water samples by neutron activation analysis (NAA) shows different sensitivities for different samples, including several cases where NAA sensitivity is better than all the analytical techniques. Several of the factors which can affect the sensitivity of the method are sample composition, neutron flux, irradiation time, decay time, coxmting time, and detector efficiency [328,329]. Different preconcentration methods have also been applied to NAA protocols for metal analysis. For instance, the use of coprecipitation method [330,331], chelating adsorbents [332], etc. One of the additional advantages of this methodology is that both the irradiation and neutral activation can be directly performed on the resin, without eluting the metals from the column. 

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