Process trends data compression

None of the practiced compression techniques satisfies all of these requirements. In addition, it should be remembered that compression of process data is not a task in isolation, but it is intimately related to the other two subjects of this chapter (1) description of process trends and (2) recognition of temporal patterns in process trends. Consequently, we need to develop a common theoretical framework, which will provide a uniformly consistent basis for all three needs. This is the aim of the present chapter. 

The ideas presented in Section III are used to develop a concise and efficient methodology for the compression of process data, which is presented in Section IV. Of particular importance here is the conceptual foundation of the data compression algorithm instead of seeking noninterpretable, numerical compaction of data, it strives for an explicit retention of distinguished features in a signal. It is shown that this approach is both numerically efficient and amenable to explicit interpretations of historical process trends. 

Computational Fluid Dynamics (CFD) Results The data trend of the above presented analytical study has been verified by computational fluid dynamics (CFD) analysis. Moreover, some microscale-specific effects could be seen for the initial part of the process, the pressure drop is confined over a short distance (between stations 2 and 3 in Fig. 5a) as the shockwave travels further firom the left to the right, the pressure gradient dissipates more and more continuously over a longer range. Instead of a well-defined shockwave, a set of compression waves can... 

The empirical model illustrated in Table 5.2 for density is compared with the experimental data and the density factor model derived previously as shown in Fig. 5.10. The trend lines between the experimental data and empirical model show very good correlation whereas the density factor analysis tend to underestimate the values. The density empirical model gives reasonable estimation of the composites density in terms of the amount of constituent material for both the gelatin and SDS in the ranges between 0 and 50 % for gelatin and 0-0.66 % for SDS respectively. Similarly, the models developed for compressive strength and modulus also showed close proximity between the experimental and empirical values of as shown in Figs. 5.11 and 5.12, respectively. Thus, it can be concluded that the empirical models developed via ANOVA in terms of process variables offer close estimation of the GSA and GSA-SDS composites experimental values. 

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