Statistical design of experiments

The practice of estabHshing empirical equations has provided useflil information, but also exhibits some deficiencies. Eor example, a single spray parameter, such as may not be the only parameter that characterizes the performance of a spray system. The effect of cross-correlations or interactions between variables has received scant attention. Using the approach of varying one parameter at a time to develop correlations cannot completely reveal the tme physics of compHcated spray phenomena. Hence, methods employing the statistical design of experiments must be utilized to investigate multiple factors simultaneously. 

Figure 4.82 Results of trials according to the statistical design of experiments [134].

The development of on-line sensors is a very costly and time-consuming process. Therefore, if one has available a dynamic model of the reactor which predicts the various polymer (or latex) properties of interest, then this can be used to guide one in the selection and development of sensors. Ideas from the optimal statistical design of experiments together with the present model expressed in the form of a Kalman filter have been successfully used (58) to select those combinations of existing or hypothetical sensors which would maximize the information that could be obtained on the states of the polymerization system. Both the type of sensors and the precisions necessary for them are easily investigated in this way. By changing the choice of the measurement matrix and... 

The topic we will discuss is ANOVA. This is a topic we have discussed previously, in fact, several times. Put into words, ANOVA shows that when several different sources of variation act on a datum, the total variance of the datum equals the sum of the variances introduced by each individual source. We first introduced the mathematics of the underlying concepts behind this in [2], then discussed its relationship to precision and accuracy [3], the connection to Statistical Design of Experiments [4-6], and its relation to calibration results [7, 8],... 

TABLE 2.6. Typical Approach and Results for Statistical Design of Experiments Approach... 

Wemimont, G. (1969), Development and Evaluation of Standard Test Methods, The Role of Statistical Design of Experiments, Materials Research Standards, 9(9), 8-21. 

System design - a prototype of a certain product is made using specialised knowledge, statistical design of experiments is not relevant at this stage. 

Initial process development Early development studies were carried out using a laboratory-scale 24" Accela-Cota and employing a statistical design of experiments technique in which the operational variables were as shown in Table 8. Although several response variables were examined... 

The Use of Statistical Design of Experiments and ArtiLcial Neural Networks. 572... 

Among the strategies used to establish new formulations are univariate analysis (where the effect of only one component is studied at a time), analysis of the effect of a group of nutrients, and statistical design of experiments. 

The strategy of statistical design of experiments (Barros Neto et al., 1995 Rodrigues and lemma, 2005) appears to be a rational and efficient way for a direct and quick determination of the effect and interaction among the parameters analyzed (Montgomery and Runger, 1999). This type of procedure results in the minimization of cost and time. This strategy has proved to be adequate to qualitatively analyze the effects of the use of protein hydrolysates as supplements for insect cell cultivation (Ikonomou et al., 2001 Batista et al., 2005). 

Many catalyst researchers are unaware of the potential benefits of statistical design of experiments. Others have had unfortunate experiences with so-called designed experiments because they underestimated the influence of experimental uncertainties on the reliability of the conclusions. In both cases familiarity with the fundamentals of statistical inference in the experimentation strategy is beneficial (4). Statistically derived strategies can certainly offer many important benefits, although they obviously cannot replace creativity or sound technical judgment. 

Carter, C.W., Baldwin, E.T., Frick, L., Statistical design of experiments for protein crystal-growth and the use of a precrystallization assay. J. Cryst. Growth 1988, 90 (1-3), 60-73. 

The acquisition of kinetic data and parameter estimation can be at quite a sophisticated level, particularly for solid catalytic reactions statistical design of experiments, refined equipment, computer monitoring of data acquisition, and statistical evaluation of the data. Two papers are devoted to this topic by Hofmann (in Chemical Reaction Engineering ACS Advances in Chemistry, 109, 519-534 [1972] in de Lasa, ed.. Chemical Reactor Design and Technology, Martinus Nijhoff 1985, pp. 69-105). 

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