Estimating computerized simulations

General Properties of Computerized Physical Property System. Flow-sheeting calculations tend to have voracious appetites for physical property estimations. To model a distillation column one may request estimates for chemical potential (or fugacity) and for enthalpies 10,000 or more times. Depending on the complexity of the property methods used, these calculations could represent 80% or more of the computer time requited to do a simulation. The design of the physical property estimation system must therefore be done with extreme care. 

To use this method, the sample is dissolved in a system containing two phases (e.g., water and octanol) such that the solution is at least about 5 x 10-4 M. The solution is acidified (or basified) and titrated with base (or acid) under controlled conditions. The shape of the ensuing titration curve is compared with the shape of a simulated curve, which is created in silico. The estimated p0Ka values (together with other variables used to construct the simulated curve such as substance concentration factor, CO2 content of the solution and acidity error) are allowed to vary systematically until the simulated curve fits as closely as possible to the experimental curve. The p0Ka values required to achieve the best fit are assumed to be the correct measured p0Ka values. This computerized calculation technique is called refinement , and is described elsewhere [14, 15]. 

For the purpose of estimating conformational parameters of silaiylene carboorganocyclosiloxanes of the structure XV, two approaches were used [117] computerized mathematical simulation using the Monte-Carlo method and experimental estimation of the flexibility parameters in solution under natural conditions. In the first case, mathematical simulation has determined the skeletal flexibility of the molecule in the absence of substituting agents at atoms forming the backbone. In the second case, all fragments of the chain, possible interactions with the solvent and temperature effect have been taken into account in the flexibility estimation. 

Chapter 9 sets forth predictive modeling approaches for estimating Pb exposures in humans. It discusses the various predictive models of human Pb exposure with respect to parameters of complexity, utility, limitations, comparative advantages versus competing approaches, and scope. A number of the predictive, simulation approaches for exposure modeling entail the use of computerized components while others are relatively simple, ad hoc statistical forms. 

North Americans carried 300-fold higher body Pb burdens than prehistoric humans. Flegal and Smith (1992) reported that prehistoric adults would have typically had a PbB value of 0.016 pg/dl, using extrapolation estimates from bone Pb contents of preindustrial and contemporary samples and animal data. Mushak (1993), using a computerized biokinetic Pb exposure simulation model and published estimates of prehistoric Pb levels in human diet, water, ambient air, etc., calculated a PbB for prehistoric children in the narrow range of 0.06—0.12 pg/dl, in good accord with the estimate of Flegal and Smith (1992). 

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