(5al) Using Simulation-Based Optimization Approach for Process Synthesis and Design under Uncertainty: Applications for Space Missions

A computational architecture, SIMulation-based OPTimization (SIMOPT) that combines combinatorial optimization within a simulation environment, is used to solve the process synthesis and design problems under uncertainty. Given the products that one wants to produce and the raw materials to start with, process synthesis methods try to determine the best combination of processes to obtain the products in the most cost effective way. Once the process list is determined, the second problem faced is determining how the integrated system will perform under the uncertain conditions, what the inventory levels should be and whether or not the system would be able to deliver the predetermined amounts of products under abnormal conditions.

The developed framework for the process synthesis and design includes a superstructure optimization module, a simulation module and a data analysis module. Given the amount of raw materials available and product demands, and available technologies and their properties, the superstructure optimization module determines the optimum technology list (and its retrofitting schedule, if applicable) for a specified objective, such as minimizing cost or maximizing profit. The simulation module is used to predict the behavior of the integrated system (technologies selected by the optimization module) under uncertainties such as technology performance variation (in recovery rates or process efficiencies) and random events e.g. technology malfunction. Trigger events are the technology malfunctions and significant performance changes. In case of a trigger event, the simulation is stopped and the state of the system and the information gathered up to that time is fed to the optimization to revise the technology list (and its retrofitting schedule). The simulation is resumed with the new list (and schedule). This loop is continued till the end of the simulation time, resulting in one timeline. In the outer loop, data analysis module uses the information gathered from statistically significant number of timelines to update the technology parameters, such as the reliability, recovery rates or process efficiencies, in the optimization module. At the termination of SIMOPT framework, a technology list, (its retrofitting schedule where applicable) and inventory levels are determined for the predetermined objective function with associated level of certainty in the product levels (such as being able to meet the product demand with 99 % confidence).

Several interesting space mission applications (with and without retrofitting options) will be given to demonstrate the use of the proposed SIMOPT framework. The results of these two cases will be presented to demonstrate the differences in design decisions for a stationary versus an evolving system.