Examples performance interpretation

When the interpreter computes (or evaluates, in Lisp jargon) a list, it always assumes that the first element of the list is the name of a function and the rest of the elements are the arguments that the function needs for processing. In the above example, the interpreter performs the addition of two numbers the name of the function is the symbol + and the two arguments are the numbers 330 and 336. 

Repeatability. This refers to two aspects of inspection similarity between objects that are inspected and possibility of maintaining constant inspection conditions (settings) for all the inspections performed. Obviously, interpretation of data in repeatable conditions is significantly simplified. Usually, inspection during or after manufacturing process will be repeatable. Another example of repeatable inspection is inspection of heat exchangers in power nuclear plants, inspection of aircrafts as these are well standardised. However, a large part of the NDT inspection done is not repeatable. 

Evidence of the appHcation of computers and expert systems to instmmental data interpretation is found in the new discipline of chemometrics (qv) where the relationship between data and information sought is explored as a problem of mathematics and statistics (7—10). One of the most useful insights provided by chemometrics is the realization that a cluster of measurements of quantities only remotely related to the actual information sought can be used in combination to determine the information desired by inference. Thus, for example, a combination of viscosity, boiling point, and specific gravity data can be used to a characterize the chemical composition of a mixture of solvents (11). The complexity of such a procedure is accommodated by performing a multivariate data analysis. 

The choice of parameter used in the determination of size distribution should include consideration of the information needed in the interpretation of the data. For example, in the case of a manufacturer of paint pigment, the size parameter that best describes the hiding power (performance of the pigment) is the projected area of particles. A powdered catalyst manufacturer is primarily concerned with surface-area equivalence. 

This matrix will contain information regarding loading characteristics such as flooding hmits, exchanger areas, pump curves, reactor volumes, and the like. While this matrix may be adjusted during the course of model development, it is a boundary on any possible interpretation of the measurements. For example, distillation-column performance markedly deteriorates as flood is approached. Flooding represents a boundary. These boundaries and nonlinearities in equipment performance must be accounted for. 

Measurement Selection The identification of which measurements to make is an often overlooked aspect of plant-performance analysis. The end use of the data interpretation must be understood (i.e., the purpose for which the data, the parameters, or the resultant model will be used). For example, building a mathematical model of the process to explore other regions of operation is an end use. Another is to use the data to troubleshoot an operating problem. The level of data accuracy, the amount of data, and the sophistication of the interpretation depends upon the accuracy with which the result of the analysis needs to oe known. Daily measurements to a great extent and special plant measurements to a lesser extent are rarelv planned with the end use in mind. The result is typically too little data of too low accuracy or an inordinate amount with the resultant misuse in resources. 

An example adapted from Verneuil, et al. (Verneuil, V.S., P. Yan, and F. Madron, Banish Bad Plant Data, Chemical Engineeiing Progress, October 1992, 45-51) shows the impact of flow measurement error on misinterpretation of the unit operation. The success in interpreting and ultimately improving unit performance depends upon the uncertainty in the measurements. In Fig. 30-14, the materi balance constraint would indicate that S3 = —7, which is unrealistic. However, accounting for the uncertainties in both Si and S9 shows that the value for S3 is —7 28. Without considering uncertainties in the measurements, analysts might conclude that the flows or model contain bias (systematic) error. 

The majority of centrifugal pumps have performance curves with the aforementioned profiles. Of course, special design pumps have curves with variations. Eor example, positive displacement pumps, multi-stage pumps, regenerative turbine type pumps, and pumps with a high specific speed (Ns) fall outside the norm. But you ll find that the standard pump curve profiles are applicable to about 95% of all pumps in the majority of industrial plants. The important thing is to become familiar with pump curves and know how to interpret the information. 

Interpreting PSA as any risk assessment using accident probabilities and consequences, government and industry have prepared many examples. These are discussed as Public Risk, Specialized Analyses, and Performance Improvement. 

As Tribus, 1969, says, all probabilities are conditional. In the example of the dree, the probabilities are conditioned on the assumption that the dice are perfect and the method of throwing has no effect on the outcome. Some writers (e.g., deMorgan, 1847) say, probability refers to the belief by a mind having uncertain knowledge. This is the interpretation of probability in the Zion-Indian Point (ZIP) and some other PSAs. IVobabiiity in this sense attempts to include all information e.g., QA that could affect the performance of a piece of equipment. Such information may be conveyed as a distribution whose height is proportional to confidence in the belief and who.se width reflects uncertainty (refer to Section 2.6). 

This factor refers to the spatial organization of the information displays. In general, instruments displaying process parameters that are functionally related should also be physically close. In this way, it is likely that a given fault will lead to a symptom pattern that is easier to interpret than a random distribution of information. Although violation of this principle may not induce errors in a direct manner, it may hinder human performance. The following example illustrates this point. 

One example of a particularly hazardous type of consequence in the second category is where, because of misdiagnosis, the operator performs some alternative task other than that required by the system. For example, a rise of pressure in a reactor may be interpreted as being the result of a blockage in an output line, which would lead to attempts to clear the line. If, instead, it... 

Kunesh [126] presents tm overview of the basis for selecting rsuidom packing for a column application. In first deciding between a trayed tower or a packed one, a comparative performance design and its mechanical interpretation should be completed, considering pressure drop, capacity limitations, performance efficiencies (HETP), material/heat balances for each alternate. For one example relating to differences in liquid distribution performance, see Reference 126. 

Dimensional analysis techniques are especially useful for manufacturers that make families of products that vary in size and performance specifications. Often it is not economic to make full-scale prototypes of a final product (e.g., dams, bridges, communication antennas, etc.). Thus, the solution to many of these design problems is to create small scale physical models that can be tested in similar operational environments. The dimensional analysis terms combined with results of physical modeling form the basis for interpreting data and development of full-scale prototype devices or systems. Use of dimensional analysis in fluid mechanics is given in the following example. 

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