Success factors method

Gamson et a/.t49) have successfully used the. /-factor method to correlate their experimental results for heat and mass transfer between a bed of granular solids and a gas stream. 

The simulation method developed in IMPROVE (cf. Sect. 5.2) is based on correlations between selected influencing factors which have been determined by literature survey and expert discussions in a theoretical rather than empirical way. However, these are just a small excerpt of the success factors relevant for actual design processes. If additional influencing factors are integrated in the simulation for their practical relevance, the models must be validated again. 

How does all this relate to analysis We now understand the factors affecting the shape of 1/E curves when mass transport is diffusion controlled. The key to a successful analytical method will be if the current at some point on the I E curve is reliably dependent upon the concentration of the analyte in the bulk solution. Ideally the relationship between current and concentration should be linear. Dc polarography is such a method. 

In this chapter, I hope to have imparted to the reader a taste of CE, and some practical suggestions that will be useful in developing a method for the analysis of a specific compound(s). By using the basic principles associated with electrophoretic separation as discussed here, and applying a systematic approach to assess the factors that influence the resolution of the analyte(s) under study, it is likely a successful analytical method can be developed for just about any analyte of interest. In many of the core chapters that follow this chapter, specific methods are covered in detail with an analyte-specific focus— however, method development of some kind will be found in the vast majority one of the 55 chapters that constitute this handbook. 

Fig. 3.16 Residual history of the successive substitution method. The convergence depends highly on the relaxation factor D. For D=0.1, oscillations occurred.

AHP has also been applied together with case study methods in different areas like Evaluation of critical success factors of ISO 14001 implementation in a case study in Malaysia (Sambasivan and Fei 2008), Case study to the selection of a multimedia authoring system in software selection (Lai et al. 1999), Case study of design and evaluation of automated cellular manufacturing systems with simulation modeling and AHP approach (Chan and Abhary 1996), just to give some examples available in the literature. 

Theorem The successive overrelaxation method with optimum relaxation factor converges at least twice as fast as the Chebyshev semi-iterative method with respect to the Jacobi method, and therefore at least twice as fast as any semi-iterative method with respect to the Jacobi method. Furthermore, as the number of iterations tends to infinity, the successive overrelaxation method becomes exactly twice as fast as the Chebyshev semi-iterative method. 

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