Running Scenarios

On the other hand, doubling the effective porosity leads to exactly inverse effects. The initial pyrite turnover rises to 150 % of the reference turnover. 

If the relation between effective porosity and tortuosity factor is taken into consideration and the two parameters are modified accordingly, the resulting total change in turnover is the product of the two single changes in turnover. Halving the porosity coupled with duplication of tortuosity factor results in a decrease of initial flux to approximately 50 % (Fig. 4.7) of the reference flux. 

For the examined systems, an approximated factor results that describes the total change in flux according to the change in tortuosity factor and effective porosity  

Since porosity and tortuosity factor are coupled according to Eq. (4), the correct effect of change in porosity regarding the associated change in tortuosity factor may be calculated by combining Eqs. (4) and (8). The resulting equation reads  

A halving of the specific pyrite surface or the pyrite oxidation rate is associated with little effects on the turnover of pyrite and the discharge of reaction products. Both parameters have the same influence, because they both determine the pyrite turnover in moles per time and mass. The effect of halving the parameters (compared to the reference calculation) on the oxygen time-depth-distribution and the discharge is shown in Fig. 4.8. 

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Originally ASTRA was developed on the base of existing models that have been converted into a dynamic formulation feasible for implementation in system dynamics and allowing for closure of the feedbacks between the models. Among these models have been the macroeconomic model, ESCOT (Schade et al., 2002) and the classical four-stage transport model, SCENES (ME P, 2000). The ASTRA model then runs scenarios for the period 1990 until 2030 using the first 12 years for calibration of the model. Data for calibration stem from various sources, with the bulk of data coming from the EUROSTAT (2005) and the OECD online databases (OECD, 2005). A detailed description of ASTRA is provided by Schade (2005). 

Process-based modelling to explore mechanisms and run scenarios based on catchment-scale projections of climate, land use and pollution change. 

If one is less restrained in setting specification limits, a balance can be struck between customer expectations and the risk and cost of failure a review of available data from production and validation runs will allow confidence limits to be calculated for a variety of scenarios (limits, analytical procedures, associated costs see Fig. 2.15 for an example). 

The algorithm CREATE-PURGE-ROUTE described earlier is run under the worst-case scenario of the original EDTA plant with the added... 

Similarly, the number and types of tests completed will influence the cost in ways that can be very complex. Recently, the author was involved in designing a proof-of-principle study intended to assess the ability of a dietary supplement to enhance weight loss among subjects instructed to follow a reduced energy diet. Sample size calculations were run for two scenarios, the first using change in body weight as the primary outcome variable, the... 

In a worst-case scenario, the run-off from agricultural land may contain levels of fertilizers or animal waste that might seriously contaminate the water supply. 

Fig. 3.10 DDT volatilisation flux from the ocean, soil and ocean burdens from MPI-MBM run assuming low (0.0034 mg/1) (blue) or high (0.1 mg/1) (red) water solubility under two climate scenarios represented by the surface temperatures Imefm 287.7 K (dashed) and Tmean=297.7 K (solid).

The main objective of the PP/DS optimizer run in the scenario at hand is to minimize setup times and costs on campaign and noncampaign resources without incurring too much delay against the order due dates. For some resource groups, mode costs are also used to ensure that priority is given to the best (i.e., fastest, cheapest, etc.) resources. 

The main objective of PP/DS optimization is the building of campaigns taking into account inventories and setup costs. Additional objectives in this scenario are the minimization of delays with respect to the due dates given by the SNP optimization run as well as the selection of suitable resources. The PP/DS optimizer is based on a genetic algorithm. As setup carry-overs have been modeled already in the upper planning level, PP/DS receives input that will yield a consistent plan. 

The complexity of the method in terms of number of steps and solvents needed depends on the sorbent chemistry. The development in a simplified scenario involves running an analyte in several concentrations in multiple replicates and assaying for recovery and performance. This procedure is described in detail for several silica and polymeric sorbents by Wells.42 However, if a number of sorbents are to be evaluated, the process becomes time-consuming if multiple 96-well plates (each with one sorbent packed in all the wells) must be screened separately. This process may take a week or more and consume an analyst s precious time as well. The most plausible solution is to pack different sorbents in the same well plate and use a universal procedure that applies to all of them. An example of such a multisorbent method development plate is the four-sorbent plate recently introduced by Phenomenex demonstrated124 to require only 1 to 2 hr to determine optimal sorbent and SPE conditions. 

We run Monte Carlo simulations to examine the performance of the sensor selection algorithm based on the maximization of mutual information for the distributed data fusion architecture. We examine two scenarios first is the sparser one, which consists of 50 sensors which are randomly deployed in the 200 m x 200 m area. The second is a denser scenario in which 100 sensors are deployed in the same area. All data points in the graphs represent the means of ten runs. A target moves in the area according to the process model described in Section 4. We utilize the Neyman-Pearson detector [20, 30] with a = 0.05, L = 100, r) = 2, 2-dB antenna gain, -30-dB sensor transmission power and -6-dB noise power. 

As expected, major environmental indicators are affected positively by the introduction of hydrogen cars. Demand for gasoline drops by more than 13% until 2030, compared with BAU, and demand for diesel by about 2%. The difference is significant, as in this scenario only passenger cars are equipped with fuel cells and H2-ICE engines, but neither buses nor light-duty vehicles are expected to be equipped with fuel cells. This means only a small share of diesel fuel consumers is affected, i.e., diesel cars, while buses, light- and heavy-duty vehicles (LDV, HDV) continue to run on diesel. 

The procedure used to determine whether a given result is unacceptable involves running a series of identical tests on the same sample, using the same instrument or other piece of equipment, over and over. In such a scenario, the indeterminate errors manifest themselves in values that deviate, positively and negatively, from the mean (average) of all the values obtained. Given this brief background, let us proceed to define some terms related to elementary statistics. 

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