Predictive programs

Creating synthesis route prediction programs has been the work of a relatively small number of research groups in the world. There are nearly as many algorithms as there are researchers in the field. However, all these can be roughly classified into three categories. 

The following briefly reviews the parameters that affect erosion and the programs instituted by Elliott to obtain basic erosion data. The data from these studies have been integrated into Elliott s erosion prediction program. Table 4-13 lists some of the more important parameters affecting expander blade erosion from solid particles. Many of these parameters directly relate to the particle itself. 

Elliott s erosion prediction program has also been used to analyze all the blade rows of a two-stage expander. This study confirmed that blade life of a two-stage expander is substantially greater than blade life of a single-stage expander. 

Although the relationship in Fig. 5.6 is somewhat coarse, it is still useful in predictions. Since octanol-water log P prediction programs are omnipresent and adequately reliable, it can now be said that they can predict membrane-water partitioning, by using the equation in Fig. 5.6. Better yet, if one measures the value of loS Ka. one can estimate the membrane partition coefficient with the confidence of the variance expressed in Fig. 5.6. 

As predicted, programming errors have been quite rare and easy to locate and fix. 

The virtual compounds can be input into metabolism prediction programs, such as Metabolexpert [30] or Meteor [31], to identify principal pathways of expected drug metabolism. 

Benlenati, E. and Gini, G. Computational predictive programs (Expert Systems) in toxicology. Toxicology 1997, 119, 213-225. 

Ballard, L. Sloan, E.D., Jr. (2004a). The next generation of hydrate prediction IV. A comparison of available hydrate prediction programs. Fluid phase equilibria, 216... 

The partition coefficient of cortisone was calculated using the predictive program of Advanced Chemistry Development Laboratories, yielding a value of log P = 1.27 0.52 [183]. Similarly, a theoretical log P value of 1.42 0.55 was calculated for cortisone acetate. 

AOPWIN Atmospheric oxidation rate prediction program... 

These models have been incorporated in the PREDICT program for computer simulation (60). 

The low-temperature enhanced diffusion of B can be modeled by calculating an effective diffusivity that is then applied to the calculation of the B profile by using the PREDICT program (59). The duration of enhanced diffusion is related to the damage annealing time. Empirically, the removal... 

Figure 21. Example of two MOSFET channel B implants performed through a poly gate I oxide structure and annealed at 600 °C for 30 min and at 700 °C for 30 min. The substantial enhanced diffusion is shown modeled with calculations from the Predict program. Data are from Mele et al. (61). Abbreviation and symbols S/MS, secondary ion mass spectrometry I, measured after implant A, measured after anneals. (Reproduced with permission from reference 59. Copyright 1988 Institute of Electrical and Electronics Engineers,...

This difftisivity was applied to the mobile fraction of P that is capable of diffusing. In the PREDICT program, equation 57 is used for the first 2 s at peak RTA temperature over the entire P profile with good results (68), provided that the anneal temperature is below 1020 °C. 

A comparison of predictions from CSMGem, the program included in the CD of this work, with the second edition s version (CSMHYD) and three commercial hydrate prediction programs, is given here for all recent hydrate data reported in literature. The five programs (with the last three commercial) compared in this work are... 

The comparisons are broken into two categories for hydrate formation temperatures and pressures (1) uninhibited systems, and (2) thermodynamically inhibited systems. These comparisons show the accuracy that may be expected from readily available hydrate prediction programs. A detailed comparison of the accuracy of these programs is given by Ballard and Sloan (2004b). 

Figure 8.1 shows the pressure and temperature of fluids in a flowline at various points along the ocean floor, predicted by a multiphase flow prediction program. As a unit mass of fluid traverses the pipeline, the pressure drops normally due to friction losses associated with fluid flow. However, the temperature decrease is more interesting. 

Due to these limitations with this prediction program, tracking historical degradation data in terms of functional groups in conjunction with CAMEO prediction data provide a more thorough approach to degradation prediction exercises. A CAMEO prediction example is provided in Figure 142. 

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