Optimization, of programmed analysi

In this chapter we will take a look at some aspects of programmed analysis, particularly those which bear relation to the chromatographic selectivity. The parameters involved in the optimization of programmed analysis will be divided into primary or program parameters and secondary or selectivity parameters. These parameters will be identified for different chromatographic techniques and procedures will be discussed for the optimization of both kinds of parameters. 

There are two aspects involved in the optimization of programmed analysis. The first one is the optimization of the parameters of the program. These parameters include the initial and final conditions, the shape of the program (see figure 6.2) and the duration of... 

All interpretive optimization methods are by definition required to obtain the retention data of all sample components at each experimental location. If the sample components are known and available they may be injected separately (at the cost of a large increase in the required number of experiments). For unknown samples, for samples of which the individual components are not available, and in those situations in which we are not prepared to perform a very large number of experiments (as will usually be the case in the optimization of programmed analysis) we need to rely on the recognition of all the individual sample components in each chromatogram (see section 5.6). 

The characteristics of the different methods for gradient optimization are summarized in table 6.5. In table 6.5a, the different methods for the optimization of the program parameters are compared. Bearing in mind that a large effort is generally not warranted for the optimization of programmed analysis (see section 6.3.2.4), we should conclude that the Simplex method is not suitable because of the large experimental effort required, and... 

In the preceding chapters we have dealt with the various stages of the process of developing methods for chromatographic analysis. We discussed the selection of the appropriate chromatographic method in chapter 2. Chapters 3,4 and 5 described the parameters, the criteria and the procedures, respectively, that may be used to optimize the retention and the selectivity. In chapter 6 this approach was extended to include the optimization of programmed analysis methods. 

The second aspect of optimization in programmed analysis involves adapting the selectivity by variation of secondary parameters. The various secondary parameters listed in table 3.10 may be used to vary the selectivity of a chromatographic system without affecting retention to a great extent (see the discussion in section 3.6.1). 

Efficient application of XRFA is impossible without use of specialized software. By means of this type of programs in XRFA is realized not only the analysis itself, but also the design of new methods and optimization of spectrometers. 

Vaidyanathan, R. and El-Halwagi, M. M. (1994). Global optimization of nonconvex nonlinear programs via interval analysis. Comput. Chem. Eng., 18(10), 889-897. 

Programmed temperature vaporization (PTV) Most versatile inlet Allows large volume injection Little-no sample degradation Effective trace (to sub-ppb) analysis Expensive Requires optimization of many parameters Not well-known... 

In this chapter, we tackle the integration design and coordination of a multisite refinery network. The main feature of the chapter is the development of a simultaneous analysis strategy for process network integration through a mixed-integer linear program (MILP). The performance of the proposed model in this chapter is tested on several industrial-scale examples to illustrate the economic potential and trade-offs involved in the optimization of the network. 

Here, n, v, and p represent a specific growth rate, a specific substrate consumption rate, and a specific product formation rate, respectively. and are the mean values of data used for regression analysis and a, bp and C are the coefficients in the regression models that are determined based on selected operating data in a database. This model was linked with the dynamic programming method and successfully applied to the simulation and onhne optimization of glutamic acid production and Baker s yeast production. 

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