Data interpretation operating problems

In the chemical engineering domain, neural nets have been appHed to a variety of problems. Examples include diagnosis (66,67), process modeling (68,69), process control (70,71), and data interpretation (72,73). Industrial appHcation areas include distillation column operation (74), fluidized-bed combustion (75), petroleum refining (76), and composites manufacture (77). 

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. 

The techniques of laboratory- and industrial-scale separations utilizing adsorption and ion exchange have been described comprehensively by Mantell (M3), Cassidy (C2), and Nachod (Nl). Treybal (T4) has recently provided a unified and modern chemical engineering approach to fluid-solid separation operations. The present article will treat the problems of data interpretation and apparatus design more extensively than the authors cited, and will give major emphasis to fixed-bed operations. 

The next fonr chapters are devoted to various aspects of data interpretation, data presentation, and quahty assurance. Chapter 9 considers interpretation of data for radionuchde identification by decay scheme. Chapter 10 reviews the important topics of data calculation, measurement uncertainty, data evaluation, and reporting the results. Chapter 11 describes the quality assurance plan that must govern all laboratory operations. Chapter 12 discusses methods diagnostics to correct the analytical and measurement problems that can be expected to plague every laboratory. 

Some surface characterization techniques, such as Raman and infrared reflectance spectroscopies (see Appendix 3), have been used extensively in corrosion experiments in some laboratories, but the techniques are not considered sufficiently universal to be discussed here. Moreover, instrument operation and data interpretation are, at the moment, sufficiently complex and specialized that production of a series of protocols to suit most corrosion situations would be difficult. Other techniques such as XRD have been used frequently for routine characterization of thick corrosion layers (often after mechanical separation from the substrate). However, XRD has not been used on films much thinner than 2-3 pm, and, where it is used, a major problem has been the inability to determine the precise location of the various phases whose XRD patterns were reflected from the surface. Grazing-incidence XRD may provide some of this depth resolution in the future. Another technique on the horizon is SPM, which is described within the major corrosion application below. 

The first of them to determine the LMA quantitatively and the second - the LF qualitatively Of course, limit of sensitivity of the LF channel depends on the rope type and on its state very close because the LF are detected by signal pulses exceeding over a noise level. The level is less for new ropes (especially for the locked coil ropes) than for multi-strand ropes used (especially for the ropes corroded). Even if a skilled and experienced operator interprets a record, this cannot exclude possible errors completely because of the evaluation subjectivity. Moreover it takes a lot of time for the interpretation. Some of flaw detector producers understand the problem and are intended to develop new instruments using data processing by a computer [6]. 

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. 

Managing Scale and Scope of Large-Scale Process Operations discusses the performance issues associated with data analysis and interpretation that occur as the scale of the problem increases (such as in complex process operations). 

Various comprehensive HPLC systems have been developed and proven to be effective both for the separation of complex sample components and in the resolution of a number of practical problems. In fact, the very different selectivities of the various LC modes enable the analysis of complex mixtures with minimal sample preparation. However, comprehensive HPLC techniques are complicated by the operational aspects of transferring effectively from one operation step to another, by data acquisition and interpretation issues. Therefore, careful method optimization and several related practical aspects should be considered. 

Merely taking water samples, conducting an analysis, and entering the data in a log book is not managing the cooling system. Unfortunately, this scenario is still all too common, and part of the problem is that in many facilities some operators and management team members simply do not have the background or experience to interpret the data. 

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