Design parameters system costs, calculation

The design parameters allow the calculation of the system costs and of the total power and/or capital investments that are necessary for a specific application. Thus, the Te/Ac concept represents a valuable tool for the scahng-up of AOTs (Bolton et al., 2001 a) and it provides the basis for a cost analysis (cf. Chemviron Carbon, 1997). 

The ICARUS software uses a combination of mathematical models and expert systems to develop cost estimates. Costs are based on the materials and labor required (following the practice used for detailed estimates) rather than installation factors. If design parameters are not specified by the user, then they are calculated or set to default values by the program. The user should always review the design details carefully to make sure that the default values make sense for the application. If any values are not acceptable, they can be manually adjusted and a more realistic estimate can be generated. 

The main variable of design for a CSTR is the hydraulic retention time (HRT), which represents the ratio between volume and flow rate, and it is a measure of the average length of time that a soluble compound remains in the reactor. Capital costs are related to HRT, as this variable directly influences reactor volume [83]. HRT can be calculated by means of a mass balance of the system in that case, kinetic parameters are required. Some authors obtained kinetic models from batch assays operating at the same reaction conditions, and applied them to obtain the HRT in continuous operation [10, 83, 84]. When no kinetic parameters are available, HRT can be estimated from the time required to complete the reaction in a discontinuous process. One must take into account that the reaction rate in a continuous operation is slower than in batch systems, due to the low substrate concentration in the reactor. Therefore, HRT is usually longer than the total time needed in batch operation [76]. 

Within the framework of component development, CFD is used for scientific modeling and model validation in addition to the classical engineering parameter studies and optimization processes. Both approaches are based on the use of HPC calculation capacity. Within the framework of modeling and vahdation, HPC facilitates a complex representation of the physical phenomena with fine space and time discretization. With the aid of such submodels and appropriate laboratory experiments, models for nozzles, heat transfer phenomena, two-phase flow, and so on can be derived and vahdated. CFD models thus selected and validated form the basis for the CFD-based design and optimization of flow systems. The classical engineering problem of parameter variation and optimization requires a large number of simulation calculations and therefore leads to an extremely high cost of computation. HPC allows the parallelization of individual simulations, which in turn makes it possible to calculate several simulations simultaneously and thus enables comprehensive parameter studies and flow optimizations to be completed in an acceptable time frame. In the ATR 10 development process, CFD simulations were conducted on up to 16 cores of the JuRoPA supercomputer simultaneously. This meant that when two simulation... 

Even with the computational facilities available today, such analyses are associated with a major effort, both for modeling the soil-structure system and carrying out the calculation. In particular during the early design stage, parametric studies are necessary in order to assess the influence of the various parameters and optimize the system for purposes of cost estimation. This necessitates the application of simplified methods that capture the essential features of the system response. The next sections provide a brief overview with emphasis on such simplified methods. 

The ADC ran the demonstration equipment over a range of parameters seeking the optimum energy point. This information was then used as part of a parametric study of a 50-mgd SWRO desalination system. Capital and operating costs were estimated at the optimum design point to calculate the lowest water cost (Seacord et al., 2006). 

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