Simulation model design

The Automation Ontology captures knowledge about industrial plants and their automation systems to support engineering of simulation models (AutomOnto in Table 5.1). It has been presented in (Novak et al. 2015). The automation ontology has a mechatronic nature and provides support for simulation model design and integration. 

The remainder of this chapter is structured as follows. Methods, standards, and other works related to this chapter are described in Sect. 10.2. Section 10.3 defines the basic view of the simulation task and specifies the important terms and concepts of the simulation domain as well as the simulation framework architecture. Sect. 10.4 presents the architecture of the simulation domain, which is the one of the most important parts of the knowledge base (KB). The structure of the knowledge base is then described in Sect. 10.5. Section 10.6 shows the usage of the knowledge base for the configuration of the simulation framework. Sect. 10.7 presents an example utilizing our approach for simulation model design. Conclusions in Sect. 10.8 summarize the experience with the prototypical implementation. 

From the statistical point of view, the automation ontology consists of 100-h classes and 80-l- properties. It uses basic axioms only and it is implemented in OWL-DL. The ontology is focused on description of objects, structures as well as physical quantities and units needed for proper simulation model design and integration. Technically, the automation ontology is implemented in OWL-DL, which was selected as an optimal compromise between expressive power and efficiency to perform reasoning. In the technical infrastructure, the ontology is embodied within the... 

In the area of the simulation design, we have compared two scenarios for developing simulation models. If the simulation library with simulation components is available, the simulation model can be semiautomatically assembled from these components. As future work in the simulation model design area, we will tackle the transformation of energy between various physical disciplines, such as between electrical and mechanical systems. The common problem of simulation design and integration that we will address in the future work is the validation of designed simulation models, especially in the relationship to the used components, from which the simulation is assembled. 

Once the flowsheet structure has been defined, a simulation of the process can be carried out. A simulation is a mathematical model of the process which attempts to predict how the process would behave if it was constructed (see Fig. 1.1b). Having created a model of the process, we assume the flow rates, compositions, temperatures, and pressures of the feeds. The simulation model then predicts the flow rates, compositions, temperatures, and pressures of the products. It also allows the individual items of equipment in the process to be sized and predicts how much raw material is being used, how much energy is being consumed, etc. The performance of the design can then be evaluated. 

Once a PES has been computed, it is often fitted to an analytic function. This is done because there are many ways to analyze analytic functions that require much less computation time than working directly with ah initio calculations. For example, the reaction can be modeled as a molecular dynamics simulation showing the vibrational motion and reaction trajectories as described in Chapter 19. Another technique is to fit ah initio results to a semiempirical model designed for the purpose of describing PES s. 

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. 

A study of industrial apphcatious by Taylor, Kooijmau, and Woodman [IChemE. Symp. Ser. Distillation and Absorption 1992, A415-A427 (1992)] concluded that rate-based models are particularly desirable when simulating or designing (1) packed columns, (2) systems with strongly uonide liquid solutions, (3) systems with trace compo-... 

To answer the above-mentioned questions, one can envision so many alternatives they cannot be enumerated. Typically, an engineer charged with the responsibility of answering these questions examines few process options based on experience and corporate preference. Consequently, the designer develops a simulation model, performs an economic analysis and selects the least expensive alternative from the limited number of examined options. This solution is inappropriately designated as the optimum. Normally it is not Indeed, the true optimum may be an order of magnitude less expensive. 

Duffal SB, Kimko HC (eds). Simulation for designing clinical trials a pharmacokinetic-pharmacodynamic modeling perspective (Drugs and the pharmaceutical sciences, Vol 127). New York Marcel Dekker, 2003... 

Simulation models are essential tools for reactor design and optimization. A general simulation model consists of a reactor and a reaction model [1]. The reactor model accounts for the reactor type and for the flow pattern in the reactor, while the reaction or kinetic model describes the kinetics of the chemical reactions occurring. 

Eulerian two-fluid model coupled with dispersed itequations was applied to predict gas-liquid two-phase flow in cyclohexane oxidation airlift loop reactor. Simulation results have presented typical hydrodynamic characteristics, distribution of liquid velocity and gas hold-up in the riser and downcomer were presented. The draft-tube geometry not only affects the magnitude of liquid superficial velocity and gas hold-up, but also the detailed liquid velocity and gas hold-up distribution in the reactor, the final construction of the reactor lies on the industrial technical requirement. The investigation indicates that CFD of airlift reactors can be used to model, design and scale up airlift loop reactors efficiently. 

The ORVR system is an important subsystem which reduces the contamination of evaporative fuel gas at gas station during the fueling. In this paper, a simulation model of adsoiption and desorption of evaporative fuel gas in canister of ORVR system is developed. From the comparison between the simulations and experiments, the validity of the developed model is verified and the dynamics can be predicted. This PDE model can be used to design the canister of ORVR system effectively for diverse climate and operating conditions. 

The final outcome will be a geological model that is more or less accurate and that can be used for the final design using simulation models. 

A simulation model needs to be developed for each reactor compartment within each time interval. An ideal-batch reactor has neither inflow nor outflow of reactants or products while the reaction is carried out. Assuming the reaction mixture is perfectly mixed within each reactor compartment, there is no variation in the rate of reaction throughout the reactor volume. The design equation for a batch reactor in differential form is from Chapter 5 ... 

Thus, the design of a batch reactor can be based on the optimization of a temporal superstructure. Given a simulation model with a mathematical formulation, the next step is to determine the optimal values for the control variables of a batch reaction system. 

Simulation for Designing Clinical Trials A Pharmacokinetic-Pharmacodynamic Modeling Perspective, edited by Hui C. Kimko and Stephen B. Duffull... 

Both classes of Operating Module usually need one or more input time series and produce one or more output time series (eg. outflow of water and constituents). From experience, the designers of HSPF knew that much of the effort in using continuous simulation models is associated with time series manipulations. Thus, a sophisticated Time Series Management System was included. It centers around the Time Series Store (TSS) (Figure 10), which is a disk-based file on which any input or output time series can be stored indefinitely. 

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