Stages of Data Processing

The data processing has to be done in stages. The data have to be screened for possible mixings of water from different aquifers, and only data from single aquifer samples, or values deduced for end members, should be applied in the following stages. 

With the crude age one can estimate the paleoenvironmental conditions in terms of glacial or interglacial period, sea level, distance of paleorecharge area from the seashore, season of major recharge, altitude of the former water table, former evapotranspiration intensity, or the type of plant community that prevailed at the studied paleorecharge site. These considerations should lead to the selection of the pertinent paleo-input functions. 

One must then recalculate a more precise age for the studied groundwater, applying the deduced paleo-input functions. Final reconstruction of the paleoclimate conditions, based on the recalculated ages can then be done. 

Exercise 15.1 An observation well, Curiosity 1, is located 3 km from the coast in a region of temperate climate. The well head is at an altitude of 15masl, and water was sampled in a marine limestone aquifer from a depth of 105 m. A 14C-based age of 17,000 + 3000 years was deduced, and the S13C value was —8.1%o. Another observation well, Curiosity 2, has been drilled 20 m away to a depth of only 18 m, and it reached water of a recent 14C age and a r)l3C value of —8.3%o. Discuss the past and present hydrological setup of the study area. 

Exercise 15.2 Following the conclusions stated in the answer to Exercise 15.1, is there a need to apply a correction to the deduced water age because the plant community could be different from that during a previous climate  

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Over the years, this concept was refined in several ways. A scale dependency was modeled by the introduction of scale-dependent quenching of combustion. The first stage of the process was simulated by quasi-laminar flame propagation. In addition, three-dimensional versions of the code were developed (Hjertager 1985 Bakke 1986 Bakke and Hjertager 1987). Satisfactory agreement with experimental data was obtained. 

The speed with which the data need to be compressed depends on the stage of data acquisition at which compression is desired. In intelligent sensors it may be necessary to do some preliminary data compression as the data are collected. Often data are collected for several days or weeks without any compression, and then stored into the company data archives. These data may be retrieved at a later stage for studying various aspects ol the process operation. 

Note that the large amount of experimental data makes it possible to assume that processes related to the transfer of the charge to the surface states formed during adsorption of acceptors on oxidated oxides develop much slower than the process of formation of the proper adsorption surface states and, therefore, they are the limiting stage of the process of charging of the surface [18, 20], Thus, in this case one can consider that Nfit) = Nt = const and expression (1.67) can be written as... 

As in example 1, the explained variance (the total variance minus the residual variance) is calculated by comparing the true process data with estimates computed from a reference model. This explained variance can be computed as a function of the batch number, time, or variable number. A large explained variance indicates that the variability in the data is captured by the reference model and that correlations exist among the variables. The explained variance as a function of time can be very useful in differentiating among phenomena that occur in different stages of the process operations. 

Cluster analysis is far from an automatic technique each stage of the process requires many decisions and therefore close supervision by the analyst. It is imperative that the procedure be as interactive as possible. Therefore, for this study, a menu-driven interactive statistical package was written for PDP-11 and VAX (VMS and UNIX) series computers, which includes adequate computer graphics capabilities. The graphical output includes a variety of histograms and scatter plots based on the raw data or on the results of principal-components analysis or canonical-variates analysis (14). Hierarchical cluster trees are also available. All of the methods mentioned in this study were included as an integral part of the package. 

The experimental curves in Fig. 2.9 are compared with the curves calculated according to the second-order kinetic equation for two different initial temperatures. The divergence between the pairs of curves at the final stages of the process reaches 15 %, while the experimental is less than 3 %. This proves that the equation is inadequate. On the other hand, a kinetic equation that takes into account the effect of self-deceleration fits the experimental data along the whole curve. Therefore, we can predict that the reaction in the system under discussion will be incomplete. 

As stated in the regulations,yields must be stated with acceptable ranges specified at appropriate stages of the process. These yield calculations must be based upon observed data, either with similar processes of other products or on data taken during the scale-up process. 

Hydrochemical studies generate large amounts of data of different parameters obtained in the field and reported by various laboratories. The first stage in data processing is to organize the data into tables. This stage is important and warrants some thinking. Have a look at Tables 6.1, 6.2, and 6.3. They contain the same data, but differ in their structure. Which of the three tables is impossible and which is most handy and most informative ... 

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