Performance variability

True Density or Specific Gravity. The average mass per unit volume of the individual particles is called the tme density or specific gravity. This property is most important when volume or mass of the filled composition is a key performance variable. The tme density of fillers composed of relatively large, nonporous, spherical particles is usually determined by a simple Hquid displacement method. Finely divided, porous, or irregular fillers should be measured using a gas pycnometer to assure that all pores, cracks, and crevices are penetrated. 

Quantitative controllable variables are ftequentiy related to the response (or performance) variable by some assumed statistical relationship or model. The minimum number of conditions or levels per variable is determined by the form of the assumed model. For example, if a straight-line relationship can be assumed, two levels (or conditions) may be sufficient for a quadratic relationship a minimum of three levels is required. However, it is often desirable to include some added points, above the minimum needed, so as to allow assessment of the adequacy of the assumed model. 

Figure 9-118. Effect of cooling tower performance variables on plan ground area required. Used by permission of Foster Wheeler Corp., Cooling Tower Dept.

Both situations with categorical and continuous, real-valued performance metrics will be considered and analyzed. Since Taguchi loss functions provide quality cost models that allow the different objectives to be expressed on a commensurate basis, for continuous performance variables only minor modifications in the problem definition of the approach presented in Section V are needed. On the other hand, if categorical variables are chosen to characterize the system s multiple performance metrics, important modifications and additional components have to be incorporated into the basic learning methodology described in Section IV. 

Instead of a single quality-related performance variable, z, as in Section V, let s suppose that one has to consider a total of P distinct objectives and the corresponding continuous performance variables, Zj, i = which are components of a performance vector z =... 

Rather than a single objective, y, as in Section IV, we now have a total of P distinct categorical performance variables, y, i = 1,..., P, associated with an equivalent number of objectives. Consequently, each data record is now composed of a (x, y) pair, where y is a performance vector defined by... 

Besides the identification of the decision variables, x, m = and the performance variables, y, the user is asked to... 

This case study is based on real industrial data collected from a plasma etching plant, as presented and discussed in Reece et al. (1989). The task of the unit is to remove the top layer from wafers, while preserving the bottom one. Four different objectives and performance variables are considered ... 

A quantization of the z, variables resulted in the definition of the following categorical performance variables, y, ... 

When examined at the supremal level, the system s primary goal is the production of pulp with the desired kappa indices. Consequently, as DUq performance variables we will consider the following ... 

In this chapter we revisited an old problem, namely, exploring the information provided by a set of (x, y) operation data records and learn from it how to improve the behavior of the performance variable, y. Although some of the ideas and methodologies presented can be applied to other types of situations, we defined as our primary target an analysis at the supervisory control level of (x, y) data, generated by systems that cannot be described effectively through first-principles models, and whose performance depends to a large extent on quality-related issues and measurements. 

Experimental demonstrations of the increments and decrements in arousal that can be produced by environmental conditions suggest a need to carefully examine this aspect of the literature concerned with the effects of caffeine on psychomotor performance. Variability in the environmental conditions and extraneous stimuli that differentiate experiments may have contributed substantially to the mixed results in that literature. Considerably more research, systematically varying environmental factors, and caffeine dosage levels will be needed before firm conclusions can be drawn in this area. 

X-ray diffraction studies are usually carried out at room temperature under ambient conditions. It is possible, however, to perform variable-temperature XPD, wherein powder patterns are obtained while the sample is heated or cooled. Such studies are invaluable for identifying thermally induced or subambient phase transitions. Variable-temperature XPD was used to study the solid state properties of lactose [20], Fawcett et al. have developed an instrument that permits simultaneous XPD and differential scanning calorimetry on the same sample [21], The instrument was used to characterize a compound that was capable of existing in two polymorphic forms, whose melting points were 146°C (form II) and 150°C (form I). Form II was heated, and x-ray powder patterns were obtained at room temperature, at 145°C (form II had just started to melt), and at 148°C (Fig. 2 one characteristic peak each of form I and form II are identified). The x-ray pattern obtained at 148°C revealed melting of form II but partial recrystallization of form I. When the sample was cooled to 110°C and reheated to 146°C, only crystalline form I was observed. Through these experiments, the authors established that melting of form II was accompanied by recrystallization of form I. 

Markl and coworkers78,79 performed variable-temperature photoelectron spectroscopy (VT PES) experiments on germabenzene derivatives. Precisely, the allylcyclohexadiene 13 yielded by gas-phase pyrolysis (450-550 °C, ca 0.05 mbar) the germabenzene 14 ... 

Simner SP, Anderson MD, Pederson LR, and Stevenson JW. Performance Variability of La(Sr)Fe03 SOFC Cathode with Pt, Ag, and Au Current Collectors. J Electrochem Soc 2005 152 A1851-A1859. 

In this chapter, the mathematical formulation of the variable classification problem is stated and some structural properties are discussed in terms of graphical techniques. Different strategies are available for carrying out process-variable classification. Both graph-oriented approaches and matrix-based techniques are briefly analyzed in the context of their usefulness for performing variable categorization. The use of output set assignment procedures for variable classification is described and illustrated. 

When performing variable-cell AIMD simulations with plane-wave basis sets, problems originate from the fact that the basis set is not complete with respect to the cell vectors.71 This incompleteness can introduce fictitious forces (Pulay forces) into asys and lead to artificial dynamics. To overcome this problem, one must ensure that asys is well converged with respect to the basis set size. In general, it is found that one needs to employ a plane-wave kinetic... 

The crucial point for building a prediction model with PCR (Section 4.6) is to determine the number of PCs to be used for prediction. In principle we could perform variable selection on the PCs, but for simplicity we limit ourselves to finding the appropriate number of PCs with the largest variances that allows the probably best prediction. In other words, the PCs are sorted in decreasing order according to their variance, and the prediction error for a regression model with the first a components will tell us which number of components is optimal. As discussed in Section 4.2... 

The equations describing performance variables, developed in Sections 3 through 8, address changes in cell performance as a function of major operating conditions to allow the reader to perform quantitative parametric analysis. The following discussion establishes the generic equations of performance variables. 

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