Computer-aided system simulation has been increasingly important in industrial environments, particularly for the model based smart manufacturing. System modeling and simulation can contribute insights to engineers with more information about the process and the consequences of the design and control decisions. There are many commercial modeling and simulation packages on the market, such as Aspen Suite for chemical processes simulation, Dymola for computational fluid simulation, etc. In addition, many companies and institutes have custom-built in-house simulation tools tailored toward their particular processes. Currently, solving system simulation problem is still the focus. On the other hand, more and more system design and operation tasks involve solving optimization problems. Although significant efforts are devoted in system modeling and simulation in the simulation platforms, engineers usually find it difficult to extract the system equations to perform equation-oriented optimization tasks. They are often left without choices to run exhaustive search or simulation-based optimization such as genetic algorithms.
In this presentation, an integrated framework for system modeling, simulation and optimization is described. The numerical engine in this framework is based on optimization solvers, which allows naturally handling inequality constraints for the simulation problem. Moreover a variety of optimization formulations will be presented to deal with the feasibility problems and targeting problems. The feasibility problem is to check whether the current system setting with inequality constraint has a feasible solution. The targeting problem is to check whether the current system setting can achieve predefined control set points. Although both feasibility and targeting problems are process simulation problems, the advantage of using optimization solver is the ability to handle inequality constraint easily. Another advantage of using optimization solver is it can be directly extended to solve optimal system design and control set point optimization. A vapor compression cycle is presented as an example to illustrate the benefits.
See more of this Group/Topical: Computing and Systems Technology Division