Modelling approach Sample Clauses

Modelling approach. The program structure is represented in Figure 1. Figure 1 – Code structure: 1) Main program, 2) Reactor model and 3) Model Parameters (based on [2]) As observed in Figure 1 (2-3) modelling of fluidized bed reactors involves an accurate coupling of several phenomena. Firstly, we have the model structure composed by a set of consistent differential balances (mass, energy and pressure) from which the state variables profiles are obtained (e.g. gas and solids composition, temperature, pressure). Then, in order to represent the system at the operating conditions under study these balances are coupled with relations with different degrees of complexity that give information regarding the thermo-chemical properties, geometry, physics and hydrodynamics of the system (the model parameters). The present model relies on the following assumptions:
Sample 1
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Modelling approach. The model will be developed in Microsoft Excel. Visual basic functionality will be incorporated to facilitate probabilistic sensitivity analyses. The model will adhere to the principles laid out in the CPHE Methods Manual (Chapter 6) and other best practice guidance for economic evaluation (e.g. Xxxxxxxx, Philips). The long-run model will be a time-dependent probabilistic calculation of the health outcomes and associated costs for specifiable cohorts of individuals. These cohort members will be followed individually over a specifiable number of years over which period their BMI will change according to the base-line rule that they maintain the same percentile for the appropriate, age-dependent, national BMI distribution. Limited sensitivity analysis may be carried out on the BMI-change assumption after discussion with NICE. In addition, outcomes should be reported using an NHS perspective, public sector perspective and broader societal perspective. Cost consequences should be from a Local Government perspective. Weight management interventions on cohorts, or subsets of cohorts, of individuals will be modelled as initial, time-dependent shifts in BMI followed by a probabilistic model of weight recovery or adherence to the weight management programmme. For each modelled individual a state vector will be maintained. The elements of this vector will include: age, gender, BMI, the probability of being alive, the probability of being in a number of BMI- related disease states, QALYs, and other lifestyle-weight management outcomes of interest. The timing of costs and benefits should be reported following receipt of the lifestyle weight management intervention across the lifetime as appropriate, to reflect the findings of the evidence reviews and discussions with NICE and the relevant Programme Development Group.
Sample 1
Modelling approach. The mass and energy balances of the reference cement kiln have been reproduced by Polimi starting from the detailed mass balance provided by VDZ. Mass and energy balances have been estimated by the proprietary code GS [GS] developed by the GECOS group of the Department of Energy of the Politecnico di Milano. The program has the capability of simulating complex energy processes by means of a modular structure. The GS process simulation model does not include any predictive model for the calculation of the cement kiln components. Therefore, parameters such as the efficiency of cyclones, the calcination efficiency in pre-calciner, the heat losses, etc. are provided as inputs to the GS code as they result from external models or from proper assumptions. The sub- processes of the cement kiln are therefore simulated by assembling a combination of elemental components like mixers, splitters, heat exchangers and chemical reactors. A very simple example regards the simulation of the last riser-cyclone stage of the preheating tower, conceptually represented in Figure 3.2. The riser-cyclone stage is simulated by using two mixers (A, D) and two splitters (B, C). The first mixer (A - riser) receives the fresh raw meal fed to the cement kiln (stream #0) and the gaseous stream coming from the previous preheater stage (#1), and releases a gas-solid mixture at the equilibrium temperature. The components B,C and D represents the cyclone: in B, the gaseous flow (#4) is separated from the solid particles (#3), in C a fraction of the solid stream is separated from the main flow to simulate the cyclone efficiency. The uncollected stream (#5) is then mixed in the last component of the stage (D), which releases the final gas-solid mixture (#7). The remaining solid stream is sent to the following preheating stage (components E, F, G, H). In each riser-cyclone stage, the riser (components A, E, I, etc…) can be also used for simulating reactions, as for example the decomposition of MgCO3 into MgO and XX0 (@000°X); in addition, this component is also exploited to set (as an input value) the heat losses associated with the preheating stage. Figure 3.2: Modular structure used for simulating the first three riser-cyclone stages of the preheating tower. Once the input file has been developed by assessing all the streams sequence and the related components layout, GS performs the iterative calculation of each component providing the overall mass and energy balances. Although the current G...
Sample 1
Modelling approach. The mass and energy balances have been estimated by use of Aspen Plus V8.8. An overview of the Aspen Plus set-up used to calculate the mass and energy balances is given in
Sample 1

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