Simulated modeling

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. 

The amount of detail input, and the type of simulation model depend upon the issues to be investigated, and the amount of data available. At the exploration and appraisal stage it would be unusual to create a simulation model, since the lack of data make simpler methods cheaper and as reliable. Simulation models are typically constructed at the field development planning stage of a field life, and are continually updated and increased in detail as more information becomes available. 

Once production commences, data such as reservoir pressure, cumulative production, GOR, water cut and fluid contact movement are collected, and may be used to history match the simulation model. This entails adjusting the reservoir model to fit the observed data. The updated model may then be used for a more accurate prediction of future performance. This procedure is cyclic, and a full field reservoir simulation model will be updated whenever a significant amount of new data becomes available (say, every two to five years). 

Analytical models using classical reservoir engineering techniques such as material balance, aquifer modelling and displacement calculations can be used in combination with field and laboratory data to estimate recovery factors for specific situations. These methods are most applicable when there is limited data, time and resources, and would be sufficient for most exploration and early appraisal decisions. However, when the development planning stage is reached, it is becoming common practice to build a reservoir simulation model, which allows more sensitivities to be considered in a shorter time frame. The typical sorts of questions addressed by reservoir simulations are listed in Section 8.5. 

It Is important to know how much each well produces or injects in order to identify productivity or injectivity changes in the wells, the cause of which may then be investigated. Also, for reservoir management purposes (Section 14.0) it is necessary to understand the distribution of volumes of fluids produced from and injected into the field. This data is input to the reservoir simulation model, and is used to check whether the actual performance agrees with the prediction, and to update the historical data in the model. Where actual and predicted results do not agree, an explanation is sought, and may lead to an adjustment of the model (e.g. re-defining pressure boundaries, or volumes of fluid in place). 

The reservoir model will usually be a computer based simulation model, such as the 3D model described in Section 8. As production continues, the monitoring programme generates a data base containing information on the performance of the field. The reservoir model is used to check whether the initial assumptions and description of the reservoir were correct. Where inconsistencies between the predicted and observed behaviour occur, the model is reviewed and adjusted until a new match (a so-called history match ) is achieved. The updated model is then used to predict future performance of the field, and as such is a very useful tool for generating production forecasts. In addition, the model is used to predict the outcome of alternative future development plans. The criterion used for selection is typically profitability (or any other stated objective of the operating company). 

Reservoir pressure is measured in selected wells using either permanent or nonpermanent bottom hole pressure gauges or wireline tools in new wells (RFT, MDT, see Section 5.3.5) to determine the profile of the pressure depletion in the reservoir. The pressures indicate the continuity of the reservoir, and the connectivity of sand layers and are used in material balance calculations and in the reservoir simulation model to confirm the volume of the fluids in the reservoir and the natural influx of water from the aquifer. The following example shows an RFT pressure plot from a development well in a field which has been producing for some time. 

Measurements have been made in a static laboratory set-up. A simulation model for generating supplementary data has been developed and verified. A statistical data treatment method has been applied to estimate tracer concentration from detector measurements. Accuracy in parameter estimation in the range of 5-10% has been obtained. 

Iterative solution methods are more effective for problems arising in solid mechanics and are not a common feature of the finite element modelling of polymer processes. However, under certain conditions they may provide better computer economy than direct methods. In particular, these methods have an inherent compatibility with algorithms used for parallel processing and hence are potentially more suitable for three-dimensional flow modelling. In this chapter we focus on the direct methods commonly used in flow simulation models. 

Mesoscale simulations model a material as a collection of units, called beads. Each bead might represent a substructure, molecule, monomer, micelle, micro-crystalline domain, solid particle, or an arbitrary region of a fluid. Multiple beads might be connected, typically by a harmonic potential, in order to model a polymer. A simulation is then conducted in which there is an interaction potential between beads and sometimes dynamical equations of motion. This is very hard to do with extremely large molecular dynamics calculations because they would have to be very accurate to correctly reflect the small free energy differences between microstates. There are algorithms for determining an appropriate bead size from molecular dynamics and Monte Carlo simulations. 

Molecular Dynamics and Monte Carlo Simulations. At the heart of the method of molecular dynamics is a simulation model consisting of potential energy functions, or force fields. Molecular dynamics calculations represent a deterministic method, ie, one based on the assumption that atoms move according to laws of Newtonian mechanics. Molecular dynamics simulations can be performed for short time-periods, eg, 50—100 picoseconds, to examine localized very high frequency motions, such as bond length distortions, or, over much longer periods of time, eg, 500—2000 ps, in order to derive equiUbrium properties. It is worthwhile to summarize what properties researchers can expect to evaluate by performing molecular simulations ... 

Pesticide Runoff Modeling. Obtaining the field data necessary to understand the potential mnoff of pesticides under a variety of conditions and sods would be an expensive and time-consuming process. As a result, a variety of simulation models that vary in their conceptual approach and degree of complexity have been developed. Models are influenced by their intended purpose, the biases of the developer, and the scale at which they are used. 

One of the first complete, continuous simulation models was the pesticide mnoff transport model (PRT) (56). Improvements in the PRT modelled to the hydrologic simulation program—FORTRAN model (57). A number of other models have been developed (58,59). These models represent a compromise between the avadable data and the abiHty to encompass a wide range in soils, climates, and pesticides. These models have had mixed success when extended beyond the data with which they were caHbrated. No model has yet been developed that can be proven to give accurate predictions of... 

A. C. Lloyd and co-workers. Development of the EESTAR Photochemical Air Quality Simulation Model and Its Evaluation Relative to the EARPP Data Base, Environmental Research and Technology Report, No. P-5287-500, West Lake Village, Calif., 1979. 

P. W. Lumiann, W. P. L. Carter, and L. A. Coyner, M Surrogate Species Chemical Reaction Mechanism for Urban-Scale Mir Quality Simulation Models, Report No. EPA/600/3-87-014, U.S. Environmental Protection Agency, Research Triangle Park, N.C., 1987. 

The simulation models of the flow-sheeting system must make frequent requests for properties at specific temperatures, pressures, and compositions. Computer-program calls for such data are usually made in a rigorously defined manner, which is independent of both the point data generation models and the particular components. These point generation routines provide the property values, using selected methods that base their calculations on a set of parameters for each component. 

Mathematically speaking, a process simulation model consists of a set of variables (stream flows, stream conditions and compositions, conditions of process equipment, etc) that can be equalities and inequalities. Simulation of steady-state processes assume that the values of all the variables are independent of time a mathematical model results in a set of algebraic equations. If, on the other hand, many of the variables were to be time dependent (m the case of simulation of batch processes, shutdowns and startups of plants, dynamic response to disturbances in a plant, etc), then the mathematical model would consist of a set of differential equations or a mixed set of differential and algebraic equations. 

With these kinetic data and a knowledge of the reactor configuration, the development of a computer simulation model of the esterification reaction is iavaluable for optimising esterification reaction operation (25—28). However, all esterification reactions do not necessarily permit straightforward mathematical treatment. In a study of the esterification of 2,3-butanediol and acetic acid usiag sulfuric acid catalyst, it was found that the reaction occurs through two pairs of consecutive reversible reactions of approximately equal speeds. These reactions do not conform to any simple first-, second-, or third-order equation, even ia the early stages (29). 

Zannetti, Paolo, Numerical Simulation Modeling of Air Pollution An Oveiview, Ecological Physical Chemistiy, 2d International Workshop, May 1992. 

While the statistical weighting is elegant and rigorous if the uncertainties are known, its applicability is hmited because the uncertainties are seldom known. Commercial simulator models are yet unable to iterate on the parameter estimates against the unit measurements. And, the focus should be on a limited subset of the complete measurements set. 

As with troubleshooting, parameter estimation is not an exact science. The facade of statistical and mathematical routines coupled with sophisticated simulation models masks the underlying uncertainties in the measurements and the models. It must be understood that the resultant parameter values embody all of the uncertainties in the measurements, underlying database, and the model. The impact of these uncertainties can be minimized by exercising sound engineering judgment founded upon a famiharity with unit operation and engineering fundamentals. 

The idea of a finite simulation model subsequently transferred into bulk solvent can be applied to a macromolecule, as shown in Figure 5a. The alchemical transformation is introduced with a molecular dynamics or Monte Carlo simulation for the macromolecule, which is solvated by a limited number of explicit water molecules and otherwise surrounded by vacuum. Then the finite model is transferred into a bulk solvent continuum... 

Having made the comparison with experiment one may then make an assessment as to whether the simulation agrees sufficiently well to be useful in interpreting the experiment in detail. In cases where the agreement is not good, the detennination of the cause of the discrepancy is often instructive. The errors may arise from the simulation model or from the assumptions used in the experimental data reduction or both. In cases where the quantities examined agree, the simulation can be decomposed so as to isolate the principal components responsible for the observed intensities. Sometimes, then, the dynamics involved can be described by a simplified concept derived from the simulation. 

In 1993, eomparisons between the results of the simulation and the measurement data from the test bed revealed an exeellent level of agreement. Sueh a dynamie simulation model makes it possible to examine the dynamie behavior of the entire system even before the maehines and eomponents have been manufaetured. It allows the system behavior to be investigated under operating eonditions that are... 

In die simulation model, die FCC system was subdivided into discrete elements and suitable subsystems. This model provided all die process parameters such as pressures, flowrates, and temperatures. Figure 6-44 shows die corresponding block diagram. (The model for die expander, piping systems, and vessels is based on a gas turbine model described by GHH Borsig in a paper by W. Blotenberg.)... 

Having previously introduced the key methods to determine the important variables with respect to stress and strength distributions, the most acceptable way to predict mechanical component reliability is by applying SSI theory (Dhillon, 1980). SSI analysis is one of the oldest methods to assess structural reliability, and is the most commonly used method because of its simplicity, ease and economy (Murty and Naikan, 1997 Sundararajan and Witt, 1995). It is a practical engineering tool used for quantitatively predicting the reliability of mechanical components subjected to mechanical loading (Sadlon, 1993) and has been described as a simulative model of failure (Dasgupta and Pecht, 1991). 

In order to build new facilities or expand existing ones without harming the environment, it is desirable to assess the air pollution impact of a facility prior to its construction, rather than construct and monitor to determine the impact and whether it is necessary to retrofit additional controls. Potential air pollution impact is usually estimated through the use of air quality simulation models. A wide variety of models is available. They are usually distinguished by type of source, pollutant, transformations and removal, distance of transport, and averaging time. No attempt will be made here to list aU the models in existence at the time of this writing. 

Shreffler, J. H., and Schere, K. L., "Evaluation of Four Urban-Scale Photochemical Air Quality Simulation Models." U.S. Environmental Protection Agency Pub. EPA-600/3-82-043. Research Triangle Park, NC, 1982. 

Killus, J. P., Meyer, J. P., Durran, D. R., Anderson, G. E., Jerskey, T. N., and Whitten, G. Z., "Continued Research in Mesoscale Air Pollution Simulation Modeling," Vol. V, "Refinements in Numerical Analysis, Transport, Chemistry, and Pollutant Removal," Report No. ES77-142. Systems Applications, Inc., San Rafael, CA, 1977. 

Szepesi, D. J., "Compendium of Regulatory Air Quality Simulation Models." Akademiai lOadd es Nyomda Vdllalat, Budapest, 1989. 

Simulation models describe the various conditions occurring during a press cycle (gradients of the temperature, the moisture content, the steam pressure and the formed bond strengths) which lead both to microbuckling of the wood cell walls by their moisture and temperature-induced densification (Fig. 6) [215-218]. 

Dynamic simulation models include fluid inertia and compressibility and exchanger shell expansion to determine the pressure spikes associated with... 

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