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** Parameter Estimation for the Treatment of Reactivity Applications **

Cost estimation for the removal of As from drinking water source was pursued. The assumptions and key cost parameters for such a treatment system are listed in Table 11.4. Two scenarios are selected for the evaluation based on two potential cases [Pg.265]

An important aspect for the treatment of empirical data in the present context is that we are dealing with random variables, i.e. quantities which adopt a certain value with a certain probability. These variables are described by probability distributions whose parameters are estimated on the basis of observed data. Figure 12.1 gives an example. [Pg.613]

Overview Interpretation is the process for using the raw or adjusted unit measurements to troubleshoot, estimate parameters, detect faults, or develop a plant model. The interpretation of plant performance is defined as a discreet step but is often done simultaneously with the identification of hypotheses and suitable measurements and the treatment of those measurements. It is isolated here as a separate process for convenience of discussion. [Pg.2572]

Consistent with experience in other clinical studies, assay affected residual error, and treatment (with or without ritonavir) affected clearance. As stated above, no effect of weight, AAG, and ethnic origin was found. Absolute values of estimates for the effect of the primary covariate, subject population, on all model parameters were larger than 1% and thus retained in the model. [Pg.434]

It can be argued that the main advantage of least-squares analysis is not that it provides the best fit to the data, but rather that it provides estimates of the uncertainties of the parameters. Here we sketch the basis of the method by which variances of the parameters are obtained. This is an abbreviated treatment following Bennett and Franklin.We use the normal equations (2-73) as an example. Equation (2-73a) is solved for <2o- [Pg.46]

Measurements have been made in a static laboratory set-up. A simulation model for generating supplementary data has been developed and verified. A statistical data treatment method has been applied to estimate tracer concentration from detector measurements. Accuracy in parameter estimation in the range of 5-10% has been obtained. [Pg.1057]

It is possible to apply a simultaneous regression estimation of stoichiometric coefficients and stability constants (i,e, ESI) [30,64], in which both stoichiometric coefficients and extraction constants are given as adjustable parameters and the program searches for the best model changing also stoichiometric indices as real numbers. This approach introduced by Havel et al, [30,64] has been implemented in the program POLET [65] and used for the treatment of potentiometric [66], spec-trophotometric [67], and kinetic data [68], The method has also been applied to [Pg.75]

Thus, a list of 1 5 descriptors was calculated for these purposes, as described below. The partition coefficient log P (calculated by a method based on the Gho.sc/Crip-pen approach [11]) (see also Chapter X, Section 1.1 in the Handbook) was calculated because it affects the solubility dramatically [17, 18]. All the other descriptors were calculated with the program PETRA (Parameter Estimation for the Treatment of Reactivity Applications) [28. [Pg.498]

Chapter 9. The fundamental reactor modeling principles covered in Chapters 2-8 provide the framework in which we think about chemical reactors. We understand which phenomena cause which observed reactor behaviors, and which design variables should be changed if We wish to alter the reactor performance. But when we want to make quantitative predictions of reactor performance, we require values for the model parameters. It is a simple fact that most of the parameters needed for the chemistries and reactor configurations of interest are Uot available in the literature. To make these models useful in standard industrial practice, therefore, we must be able to conveniently determine or estimate these parameters from experimental data collected on the system of interest. Chapter 9 covers this important topic iof parameter estimation, which is not usually addressed in a systematic manner in introductory treatments of reactor analysis and design. [Pg.26]

** Parameter Estimation for the Treatment of Reactivity Applications **

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