Simulation, Modeling, and Design Feasibility

Simulation, Modeling, and Design Feasibility Because reaction and separation phenomena are closely coupled in a reactive distillation process, simulation and design are significantly more complex than those of sequential reaction and separation processes. In spite of the complexity, however, most commercial computer process modeling packages offer reliable and flexible routines for simulating steady-state reactive distillation columns, with either equilibrium or kinetically controlled reaction models... 

In recent years, however, there has been a vertical integration of process systems-related engineering services. Computer software firms have purchased or merged with control and automation companies and process control consulting firms. It is now becoming feasible to use the same software, or have relatively transparent links that allow a smooth transition between simulation models for design, data reconciliation, and parameter estimation, online optimization, and control. 

The simulation environments that support these modeling activities are themselves software and they could, with difficulty, be represented within a logic-based assurance framework (this is already feasible for Simulink, whose models can be imported into many verification environments [17]). However, this is not the main obstacle to the use of simulation models within formalized assurance cases. Rather, the problem is that simulation models are designed for that purpose and simultaneously say too much and too little for the purposes of assurance and minimization of epistemic doubt. For example, the Simulink model for a car braking system will provide equations that allow calculation of the exact rate of deceleration in given circumstances (which is more information than we need), but will not provide (other than indirectly) the maximum stopping distance— which is an example of a property that may be needed in an assurance case. The crucial point is that it should be easier to resolve epistemic doubts about a simple constraint, such as maximum stopping distance, than the detailed equations that underlie a full simulation model. 

Realistic predichons of study results based on simulations can be made only with realistic simulation models. Three types of models are necessary to mimic real study observations system (drug-disease) models, covariate distribution models, and study execution models. Often, these models can be developed from previous data sets or obtained from literature on compounds with similar indications or mechanisms of action. To closely mimic the case of intended studies for which simulations are performed, the values of the model parameters (both structural and statistical elements) and the design used in the simulation of a proposed trial may be different from those that were originally derived from an analysis of previous data or other literature. Therefore, before using models, their appropriateness as simulation tools must be evaluated to ensure that they capture observed data reasonably well [19-21]. However, in some circumstances, it is not feasible to develop simulation models from prior data or by extrapolation from similar dmgs. In these circumstances, what-if scenarios or sensitivity analyses can be performed to evaluate the impact of the model uncertainty and the study design on the trial outcome [22, 23]. 

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