High throughput research is a highly leveraged capability used to explore a tremendous range of experimental options without consuming the time and resources of the experimenter. With ever present constraints on time and people, smart utilization of this capability is highly encouraged.

Modeling is a powerful tool for understanding how processes work. Modeling can give insight into the robustness of a process during an upset, how a process could be optimized, and so much more. Historically, exploration of these areas was limited to the time and resources available to the process modeler. Through the inventive combination of modeling platforms, this team has developed the capability to complete high throughput modeling, offering the same benefits of high throughput research to the modeling community.

The high throughput modeling approach integrates Aspen Plus^® with MATLAB^® and uses the more powerful optimization algorithms available in MATLAB. One potential use of this new capability is related to the optimization of existing operations or new designs. Traditional techniques involve a parametric study manipulating one variable at a time on a platform such as Aspen Plus to find the optimum conditions. There are challenges and shortcomings associated with this approach. As processes get more complex, there are more variables to manipulate to arrive at the optimal solution. More variables require more time to optimize, so the greater the chance a “good enough” answer will be accepted rather than finding the true optimum solution. By not reaching the most optimal condition, the company spends additional capital or annual expense; money left on the table that could contribute directly to the bottom line.

This talk includes background on how this capability was developed. It also contains information on how it has been utilized at Dow to optimize existing processes, develop new process control conditions, and save capital on future complex process designs.