The Simulation Model

One way of studying how these various components come together and interact is to build a computer simulation model of a supply chain and to expose it to a dismption. We can also use this same simulation model to assess how alternative policies and actions can affect this chain of events or, if possible, control/eliminate it (and by doing so prevent the SCD from occurring). 

One critical decision facing the researcher is selecting the specific type of simulation approach to use (e.g., static vs dynamic deterministic vs stochastic continuous vs discrete systems dynamic vs discrete event vs complex systems). While some researchers have strongly presented the advantages offered by 

2 Structure of the typical supply chain model - a simulation framework 

When bnilding a simnlation model of a supply chain, two considerations must be noted. First, the system mnst be a dynamic, multi-echelon system that brings together mnltiple tiers of snpphers and multiple tiers of customers (Fig. 7.2). Second, while the overall simnlation model deals with the entire supply chain, aity analysis of this data will tend to focns on the performance of only one node or entity in the model - the focns of interest (as shown in Fig. 7.2). While simulation does allow ns to look at the performance at multiple points, typically, the question that we most often addressed takes the form of How does a supply chain disraption taking place at some upstream point in the supply chain affect the performance of this firm  

Simnlation provides the researcher with a veiy attractive vehicle for generating data. It is attractive in that it reports all the data that you want with continuous regularity and 100 percent accuracy. Driving this vehicle is the experiment. Within the experiment, the researcher must spedly the parameters, and the experimental factors. Parameters are those elements that describe elements exogenous to the simulation model and outside of the control of the researcher. Parameters represent the givens or the system constraints under which the simulation model operates. In contrast, experimental factors are those elements that are under the control of the researcher. Experimental factors incorporate the policies, tools, and procedures that can be used to deal with SCD. In this section, we will focus our attention primarily on the experimental factors. 

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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 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). 

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. 

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. 

First we tuned the simulation model using existing operation conditions. Product properties as well as conversion and temperature profile along the reactor axis closely coincided with the actual data after properly choosing the kinetic constants and other operation parameters. 

This is a further deepened work of what Samsung Total accomplished[12-14] several years ago. Several operation conditions including hardware modification which may enhance the productivity were deduced and simulated using the simulation model. Some ideas wctb alre y applied to commercial plant when they were concluded practically reasonable while some are on the waiting list One of the examples of productivity enhancement is shown in Fig. 1 and Fig. 2 which compare the conversion profiles and MWDs under original and revised operation conditions. As shown in these two figures the productivity was mhanced while MWD docs not change much. 

By using the simulation model developed in Samsung Total we applied the ideas of pFoductivily enhancement successfiiUy to LDPE plant and accomplished considerable productivity incn e. The MWD as well as the melt index and density calculated by the simulation model convinced us of applying the ideas to commercial plant. The end user property prediction capabilities of the model will be refined further by integration of phj icxjchemical and statistical approaches and be one of the next potential research items. 

Klaessens [14-17] developed a laboratory simulator , written in SIMULA, which by a question-answering session assembles the simulation model. SIMULA [18] is a programming environment dedicated to the simulation of queuing systems. KEE [ 19] offers a graphics-driven discrete event simulator, in which the objects are represented by icons which can be connected into a logical network (e.g. a production line for the manufacturing of electronic devices). Although KEE has proven its potential in many areas, no examples are known of analytical laboratories simulated in KEE. 

Calls the simulation model. Following execution of a simulation run, control is returned to the next statement. 

The simulator models the FCCU, generating output from 110 sensors every 20 seconds. In all, 13 different malfunction situations were simulated and are available for analysis. There are two scenarios for each malfunction, slow and fast ramp. Table II provides a list and brief description of each malfunction. A typical training scenario for any fast ramp malfunction simulation had the landmarks listed in Table III. Similarly, a typical training scenario for any slow ramp malfunction simulation is shown in Table IV. For both the fast and slow ramp scenarios, there was data corresponding to 10 min of steady-state behavior prior to onset of the faulty situations. 

Keeping the concentration ratio of H20 and CO in the simulation model constant (according to the Thiele modulus see Equation 12.21) leads to equal concentration profiles of H2, as shown in Figure 12.4, and consequently to equal effectiveness factors for both methods (Thiele modulus and simulation). In fact, the concentrations of H2, CO, and H20 change inside the pore, as considered in the simulation. Therefore, the results obtained by the software used represent reality best. 

If a simulation model is used as part of a MES to evaluate production schedules and support daily operation the presentation of simulation results quite often is integrated in the MES environment. The planner might not even see or know the simulation model itself. There might be a feature such as assess order schedule within the MES, which starts a simulation experiment. Details on this and on the other ways of application will be illustrated by the examples in the next section. 

Due to stochastic demand in China, stochastic production yields in Europe and some stochastic variations in transport times between the two it was decided to support the decision between these alternatives by means of simulation. The structure of the simulation model is shown in Figure 2.2. 

Based on these and other inputs the simulation model provides several outputs. Main results are ... 

Another perspective for production simulation is automatic capacity utilization optimization of multi-product systems. As discussed, this task may be very difficult because of the many different variables and boundary conditions. In an environment integrating optimization and simulation, the optimizer systematically varies the important decision variables in an external loop while the simulation model carries out production planning with the specified variables in the internal loop (see Gunther and Yang [3]). The target function, for example total costs or lead times, can be selected as required. The result of optimization is a detailed proposal for the sequence of the placed orders. 

As plant surface area the layout of the existing production building was used. In addition, three existing bottling plants were incorporated in the simulation model. 

The simulation program CHROMPLATE uses the plate model for the same column conditions as the simulation model CHROMDIFF. The results obtained are very similar in the two approaches, but the stagewise model is much faster to calculate. 

Sikorsky and Romiszowski [172,173] have recently presented a dynamic MC study of a three-arm star chain on a simple cubic lattice. The quadratic displacement of single beads was analyzed in this investigation. It essentially agrees with the predictions of the Rouse theory [21], with an initial t scale, followed by a broad crossover and a subsequent t dependence. The center of masses displacement yields the self-diffusion coefficient, compatible with the Rouse behavior, Eqs. (27) and (36). The time-correlation function of the end-to-end vector follows the expected dependence with chain length in the EV regime without HI consistent with the simulation model, i.e., the relaxation time is proportional to l i+2v The same scaling law is obtained for the correlation of the angle formed by two arms. Therefore, the model seems to reproduce adequately the main features for the dynamics of star chains, as expected from the Rouse theory. A sim-... 

The sensitivity of diffusion-model output to variations in input has been assessed by workers at Systems Applications, Inc., and at the California Department of Transportation. In each case, reports are in preparation and are therefore not yet available. It is important to distinguish between sensitivity and model performance. True physical or chemical sensitivity that is reflected by the simulation-model equations is a valid reflection of reality. But spurious error propagation through improper numerical integration techniques may be r arded as an artificial sensitivity. Such a distinction must be drawn carefully, lest great sensitivity come to be considered synonymous with unacceptable performance. 

To insure that the correction factors are not just empirical numbers that fit only one specific FCC unit at one set of operating conditions, the model should be cross-verified. For this, the same model with these correction factors is used to simulate the second FCC unit. This unit uses a very similar catalyst with almost the same activity and age and a very similar feedstock. Success of the second simulation acts as a cross-verification here and ensures that the corrected model gives a reasonably good general representation of FCC type IV units. However, in practice the catalyst activity should be regularly checked to ensure that the preexponential factors used in the simulation model are still valid. 

The simulation models also correctly predicted the diffusivities of hydronium and methanol in a wide range of temperature (Fig. 19). Methanol is a neutral species and weakly interacts with Nation backbone. It is not surprising that the present MD models that do not consider chemical interaction between the molecules can still correctly evaluate the diffusivity of methanol. Because the present experimental setup is limited for liquid samples, whether or not the permeability of diffusivity is strongly depends on water content has not been examined. In summary, this work provided benchmark for the atomistic simulation of the transport processes in Nation at water content above 3 although at some points, the errors can be 100%. 

This chapter is divided into three main sections, focusing respectively on the simulation of the single cell (Section 6.2), of the tube (Section 6.3) and of the bundle (Section 6.4). In each section, the results obtained from the simulation model are reported, and Sections 6.2 and 6.3 also discuss a comparison to experimental data provided by RRFCS. 

The simulation model depicts the flows of nitrogen between the compartments, and in particular is used to investigate the effect of sedimentation of phytoplankton and zooplankton faeces out of the euphotic zone which is assumed to be 60 m deep however the depth does not affect the conclusions drawn from the results. 

Values assigned to each of the 22 constants in the 19 expressions used in the simulation model, and the relevant sources. The flow pathways are represented as donor -> recipient compartments. N = nitrogen pool P = phytoplankton B = bacteria F = zooflagellates L = large protozoa Z = micro-mesozooplankton (F+U) = faeces and urine (nitrogen pool). All units expressed in terms of mg, m2 and/or d. 

Fig. 8. Estimates of percentage of food consumed by kelp bed filter feeders that is derived from phytoplankton, kelp, faeces and bacteria. From the simulation model of Wulff and Field (1983).

In this section, we evaluate the performance of the dynamic SDR through simulation. We will first describe the simulation model and the performance metrics, then present the results. 

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