Ensuring consistent process models

A typical structure of a closed-loop RTO system is shown in Fig. 6, which consists of subsystems for data validation, model updating, model-based optimization, and command conditioning. Raw measurements taken from the plant are filtered and checked for reliability in the data validation subsystem. Because, model-based RTO systems rely on model updating to correct for modeling errors and disturbances, an effective model updating system is required to ensure that the RTO system tracks the changing optimal operations closely. Model updating, most commonly effected via on-line estimation of some set of model parameters, uses the validated data. The updated process model is then used by the model-based optimization... 

In a subsequent analysis phase, the work process model is checked for consistency and completeness (.3). Consistency checks ensure that the model does not contain contradictory statements about the work process. Completeness refers to the aspects which are relevant to the intended applications of the model. Completeness does not mean that all details about an aspect of a work process must be included in the model. Rather, completeness requires that a relevant aspect is modeled on a sufficient level of detail, which complies with the target format of the software application using the work process model. 

The behavioral model is the internal model of the process, which indicates how the outputs are determined from the inputs, given the assumptions that have been made. The behavioral model will be developed further by using data flow diagrams. This will clarify the model structure and ensure consistency and completeness of the model. 

The process of specification should always be subjected to verification to ensure accuracy and meaning in the data provided. Even without recourse to full-scale calculation of the solution, internal consistency of the geometry can be checked, as can closure of curves or overlap of distinct components, whereas physical properties can be matched, say, with tables of established values representing material properties, or compared against experience accrued by modellers. In Figure 15.1 each operational component is connected multiply and reversibly with other components, illustrating the practical side of modeling, where one is often required to repeat steps to correct, clarify, or modify actions taken previously. 

A 5% standard deviation from the mean value of market demand for the saleable products in the LP model is assumed to be reasonable based on statistical analyses of the available historical data. To be consistent, the three scenarios assumed for price uncertainty with their corresponding probabilities are similarly applied to describe uncertainty in the product demands, as shown in Table 6.2, alongside the corresponding penalty costs incurred due to the unit production shortfalls or surpluses for these products. To ensure that the original information structure associated with the decision process sequence is respected, three new constraints to model the scenarios generated are added to the stochastic model. Altogether, this adds up to 3 x 5 = 15 new constraints in place of the five constraints in the deterministic model. 

Given the data challenges discussed previously and the increasing use of streamlined methods, it is necessary to continuously improve the consistency and transparency of the information and the assumptions used in such tools to ensure the quality and the validity of the decisions made with the aid of LGA metrics. The inclusion of quality indicators (such as sensitivity and uncertainty analysis) will continue to be an important step to estimate the uncertainties involved in the inventory and impact models. Finally, there is a need to continuously perform peer review assessments by LGA experts, as the current LGA expertise in pharmaceuticals is very limited. When these requirements are fulfilled, LGA metrics are powerful tools to aid the decision making leading to more sustainable pharmaceutical processes. For further examples of FLASG scores and other LGA analyzes being applied, see Section 10.4.1. 

The objectives of this test pattern is to analytically resolve these problems into three manageable segments. The first task will be to define the viscoelastic kinetic properties of a material as a function of various reaction temperatures. These properties (viscosity, viscous modulus, elastic modulus, tan delta) define the rate of change in the polymers overall reaction "character" as it will relate to article flow consolidation, phase separation particle distribution, bond line thickness and gas-liquid transport mechanics. These are the properties primarily responsible for consistent production behavior and structural properties. This test is also utilized as a quality assurance technique for incoming materials. The reaction rates are an excellent screening criteria to ensure the polymer system is "behaviorally" identical to its predecessor. The second objective is to allow modeling for effects of process variables. This will allow the material to undergo environmental... 

As in Example 4, the EXTRACT block in the Aspen Plus process simulation program (version 12.1) is used to model this problem, but any of a number of process simulation programs such as mentioned earlier may be used for this purpose. The first task is to obtain an accurate fit of the liquid-liquid equilibrium (LLE) data with an appropriate model, realizing that liquid-liquid extraction simulations are very sensitive to the quality of the LLE data fit. The NRTL liquid activity-coefficient model [Eq. (15-27)] is utilized for this purpose since it can represent a wide range of LLE systems accurately. The regression of the NRTL binary interaction parameters is performed with the Aspen Plus Data Regression System (DRS) to ensure that the resulting parameters are consistent with the form of the NRTL model equations used within Aspen Plus. 

The actual process flow rates are important in nonequilibrium model simulations, whereas in most equilibrium stage simulations, a simulation with a feed flow rate of 1 unit is as meaningful as a simulation with a feed flow of 10, 100, or 573 units. In real columns the flow rates influence the mass transfer coefficients as well as the tray hydraulics. An inappropriate flow specification may mean the column will flood or, just as likely, dump all the liquid through the holes in the tray. Thus, it is important to ensure that the specified (or calculated) flows and tray or packing characteristics are consistent with the satisfactory operation of the column. 

The full pseudo-homogeneous 2D axi-symmetric model, consisting of (11.19) to (11.23), was used to simulate the synthesis gas process. The model was simulated with a grid 17x257 for 3 seconds until the steady state solution was obtained. The time increment in the simulations was At = 10 s. To ensure mass conservation the convergence criteria was set to an error limit of 10 of the residual error. 

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