Step numbering terms Links

The main question is whether synthesis of PHA in plants can succeed in bringing the cost of the polymer down to the range of 0.5 -1 US /kg. Bacterial production of PHA typically relies on a carbon source, such as sucrose or glucose, which is produced from photosynthesis and extracted from plants. Synthesis of PHA directly in plants would, therefore, represent a saving in terms of the number of intermediary steps linking C02 fixation to PHA production. Furthermore, starch is one of the cheapest plant commodity product on the market, at about 0.25 US /kg [86]. It is, thus, likely that the production cost of PHA in plants will be substantially cheaper than bacterial fermentation. The final cost of producing PHA in plants will depend on a number of factors. 

It is at this point that we depart from the terminology used by Bockris and Reddy (Ref. 3, p. 1007) in their often-cited and generalized discussion of transfer coefficients [Eqs. (la) and (lb)] (i.e., and y ) and introduce the related terms y. and y p. The difference between these sets of electron-number parameters is that in the latter, an electron transferred in a step that occurs, say, v times (i.e., it has a stoichiometric number v greater than 1) is counted only once and not the v times it actually has to occur for one turnover of the overall reaction. This added complication of the electron accounting has the advantage of showing more clearly how stoichiometric coefficients and numbers enter into experimentally obtainable transfer coefficients and hence can demonstrate one of the links between mechanism and experiment. 

When objective measurement of performance capacities has been incorporated into many clinical trials, concepts and tools from human performance engineering can facilitate the selection of variables and shed some light on issues noted above. In either safety- or efiicacy-oriented studies, study variable selection can be characterized as a two-step process (1) identification of the factors in question (Table 36.1) and (2) selection of the relevant performance capacities to he measured and associated measurement instruments. This link between these two steps often represents a challenge to researchers for a number of reasons. First, duality in terminology must be overcome. Concerns about an intervention are typically initially identified with negative terms such as dizziness and not in terms of performance capacities such as postural stability. Human performance models based on systems engineering concepts (Kondraske, 1995) can be used to facilitate the translation of hoth formal and lay terms used to identify adverse effects to relevant performance capacities to he measured, as shown in Table 36.1. 

We are now in a position to commence our simulation, but as stated earlier we must first convert the simulation to a dimensionless form. We have already taken one step in this direction by introducing the model diffusion coefficient Dm- We now have to fix suitable values for Ax and At, Clearly it would be useful if we could set these values independently, but unfortunately this is not possible. It can be shown that stable solutions to the finite difference expressions are only obtained for values of Dm less than 0.5, and since Dm is equal to D.AtlAx the values of At and Ajc are linked. Specifically, if Ax is decreased, At also has to be reduced, and for any given length of experiment the number of iterations has to be increased. In view of the Ax term this, of course, leads to a sharply increased computation time. One of the major problems in designing a simulation is to choose all the parameters so that they are physically meaningful and so that there is a useful correlation between them and the constants of the real system. 

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