Evaluation criteria importance

Criteria have also been developed for evaluating the importance of intraphase and interphase heat transfer on a catalytic reaction. The Anderson criterion for estimating the significance of intraphase temperature gradients is [J. B. Anderson, Chem. Eng. Sci., 18 (1963) 147] ... 

Does a soil-fluid-chemical system behave as an active electrochemical system or a passive electrical conductor under the influence of a DC electric field This is a fundamental question of significant implications. The evaluation criterion that can be used to differentiate the two systems of completely different nature is vested in Faraday s laws of electrolysis, as the transfer of electrons from the electrodes to the system and vice versa in an ideal electrochemical system is invariably associated with chemical reactions obeying Faraday s laws of electrolysis (Antropov, 1972). The two important fundamental laws of electrolysis can be simply expressed as follows (a) the amount of chemical deposition is proportional to the quantity of electric charges flowing through the system in an electrolytic process, and (b) the masses of different species deposited at or dissolved from electrodes by the same quantity of electric charges are directly proportional to their equivalent weights (Crow, 1979). 

Impact evaluation has been considered to have the highest technical content, to be the most difficult, and also to be the most imperfect link of the four stages in LCA, whose main aim is classification, characteristic and quantitative. Currently, there is no clearly unified evaluation criterion between inventory dates and health damages(Gao, E, 2008). Therefore, it is important to establish a set of comprehensive and quantitative methods to transfer inventory dates into health damages. 

One important technical evaluation criterion for electrolytic processes is the efficiency, i.e. the cost-benefit ratio for an industrial electrolysis system. When determining the efficiency, it is expedient to utilize the heating value (3.54 kWh Nm ) or the thermoneutral voltage Vth = 1.48 V because in commercial electrolysis systems for alkaline and PEM electrolysis, water is added in its liquid state. As such, the efficiency referring to the heating value of hydrogen specifies how efficiently the electrolyzer or the entire electrolysis system with all auxiliary components can be operated. 

Elastic Properties. The abiUty of a fiber to deform under below-mpture loads and to return to its original configuration or dimension upon load removal is an important performance criterion. Permanent deformation may be as detrimental as actual breakage, rendering a product inadequate for further use. Thus, the repeated stress or strain characteristics are of significance in predicting or evaluating functional properties. 

Optical Properties. Haze is the most common optical property problem that depends on colorants. Because dyes ate dissolved into the resin system, they contribute Htde or no practical haze to the system. Pigments can have significant haze, which is a combination of the pigment itself and the quahty of dispersion of the pigment. In an opaque appHcation haze is not a concern, but in transparent or translucent appHcations haze development becomes an important criterion in colorant evaluation. 

The standard state chosen for the calculation of controls its magnitude and even its sign. The standard state is established when the concentration scale is selected. For most solution kinetic work the molar concentration scale is used, so A values reported by different workers are usually comparable. Nevertheless, an important chemical question is implied Because the sign of AS may depend upon the concentration scale used for the evaluation of the rate constant, which concentration scale should be used when A is to serve as a mechanistic criterion The same question appears in studies of equilibria. The answer (if there is a single answer) is not known, though some analyses of the problem have been made. Further discussion of this issue is given in Section 6.1. 

All predictions must be taken for what they are, namely, generalizations based on current knowledge and understanding. There is a temptation for a user to assume that a computer-generated answer must be correct. To determine whether this is in fact the case, a number of factors concerning the model must be addressed. The statistical evaluation of a model was addressed above. Another very important criterion is to ensure that a prediction is an interpolation within the model space, and not an extrapolation outside of it. To determine this, the concept of the applicability domain of a model has been introduced [106]. 

An important criterion allows for the evaluation of the importance of the energy dissipation field in the reaction zone. This characteristic time constant has been formulated by Pohorecki and Baldyga (1993, 1995) ... 

The design methods de.scribed above rely on correlations of the overall reactor average quantities obtained from experimental tanks of different scales. The most important deficiency of these methods is that local effects are not taken into consideration, while these might be responsible for the overall reactor performance. Accordingly, if none of the above scale-up criteria is found satisfactory (see e.g. data of Middleton et ai, 1986) a more fundamental approach must be applied, although not necessarily as complex as the one presented in Section 5.4.S.2. Such an approach was presented by Paul et al. (1971) who found that the yield of the desired intermediate in a system of consecutive reactions (iodination of L-tjrosine) correlates reasonably with fluctuations of the velocity, So, these fluctuations could be chosen as a criterion for scale-up of the reactor. The average value for u in the upper part of the tank was evaluated from ... 

Sensitivity by itself is not sufficient to completely evaluate an LCEC system for analytical purposes. The minimum detectable quantity (detection limit) is of more practical importance. The detection limit takes into consideration the amount of baseline noise as well as the response to the analyte. The detection limit is then defined as the quantity of analyte which gives a signal-to-noise ratio of three (a S/N of 3 is the generally accepted criterion although other values have been used). For a complete description of an LCEC application, both the sensitivity and detection limit, along with the S/N criteria used, should be provided. 

The life time of a catalyst is an important criterion for its commercial application. The reforming reactions in most of the cases have been studied only for hours of time on-stream, but not for days or weeks. Superior catalysts need to be evaluated by operating continuously for thousands of hours. Simple accelerated aging test that allows assessment of catalyst life in hours or days rather than the usual priod of months will be helpful. 

An important point is the evaluation of the models. While most methods select the best model at the basis of a criterion like adjusted R2, AIC, BIC, or Mallow s Cp (see Section 4.2.4), the resulting optimal model must not necessarily be optimal for prediction. These criteria take into consideration the residual sum of squared errors (RSS), and they penalize for a larger number of variables in the model. However, selection of the final best model has to be based on an appropriate evaluation scheme and on an appropriate performance measure for the prediction of new cases. A final model selection based on fit-criteria (as mostly used in variable selection) is not acceptable. 

An important aspect of variable selection that is often overlooked is the hazard brought about through the use of cross-validation for two quite different purposes namely (1) as an optimization criterion for variable selection and other model optimization tasks (including selection of the optimal number of PLS LVs or PCR PCs) and (2) as an assessment of the quality of the final model built using all samples. In this case, one can get highly optimistic estimates of a model s performance, because the same criterion is used to both optimize and evaluate the model. As a result, when doing variable selection, especially with a limited number of calibration samples, it is advisable to do an additional outer loop cross-validation across the entire model... 

In classical statistics, the most important type of criterion for judging estimators is a high probability that a parameter estimate will be close to the actual value of the parameter estimated. To implement the classical approach, it is necessary to quantify the closeness of an estimate to a parameter. One may rely on indices of absolute, relative, or squared error. Mean squared error (MSB) has often been used by statisticians, perhaps usually because of mathematical convenience. However, if estimators are evaluated using Monte Carlo simulation it is easy to use whatever criterion seems most reasonable in a given situation. 

At this point, it is also important to check the plug-flow assumption, as the models assume plug flow for liquid. The correlation of Michell-Furzer (eq. (3.417)) for the liquid is used and the results are shown in Table 5.17. The minimum values of Z/Jp are evaluated by using the Mears criterion (eq. (3.421)). 

The next parameter of importance is the Peclet number of the liquid and the gas phase. For the specified Reynolds number, the Peclet number for the liquid phase using the Michell-Furzer correlation (eq. (3.417)) is 0.74. The minimum value of Z/dp for ethanol conversion between 0.1 and 0.9, evaluated using the Mears criterion (eq. (3.421)), is 2.84 and 62.11 respectively, much lower than the value used in the example, which is about 2500. Thus, the operation can be assumed to follow the plug-flow model. 

The evaluation of dose-response relationships is a critical component of hazard characterization (OECD, 1989 ECETOC, 1992 US , 1997a IPCS, 1999). Evidence for a dose-response relationship is an important criterion in establishing a toxic reproductive effect. It includes the evaluation of data from both human and laboratory animal studies. Because quantitative data on human dose-response relationships are infrequently available, the dose-response evaluation is usually based on the assessment of data from tests performed using laboratory animals. However, if data are available in humans with a sufficient range of doses, dose-response relationships in humans can also be evaluated. 

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