High Throughput Modeling: A Novel Approach for Process Optimization | AIChE

High Throughput Modeling: A Novel Approach for Process Optimization

Monday, May 24, 2021,
12:00pm to 1:00pm
Virtual / Online

Please join us for a brief announcement of the MMAIChE election results just prior to the MMAIChE seminar!

Reza Panah, Associate Research Scientist at Dow, will be our May speaker for the 2020/2021 Mid-Michigan AIChE Seminar Series on the topic:


High Throughput Modeling: A Novel Approach for Process Optimization


Monday May 24th from 12 noon to 1:00 pm


We are committed to providing a safe and healthy environment for all meeting attendees. In light of the impact of the Coronavirus, we will be holding our meetings virtually. Please reach out to Pranav Karanjkar (pranav.karanjkar@dow.com) for a Webex link you need to login on May 24th, 2021. 


The lecture qualifies for one continuing education hour. CEH certificates are needed for licensed Professional Engineers to maintain their license and certificates will be provided to interested attendees.



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 on 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, the new capability has been developed 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 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 will include background on how this capability was developed. It will also contain information on how it has been utilized so far at Dow to optimize existing processes, develop new process control conditions and save capital on future complex process designs.


Reza's Bio:

Reza Panah is an Associate Research Scientist in the Engineering Sciences group at Dow Performance Silicones Process R&D. He joined Dow in 2014, with a PhD in Chemical Engineering from Nanyang Technological University in Singapore following a post-doctoral assignment at Stanford University. His background is in adsorption processes, mathematical modeling, process optimization, and experimental evaluation of new and complex separation technologies. Recently his focus has been on optimization of existing polymer processes at Dow by combining the power of mathematical modeling with experimental studies. Reza is the recipient of Mid-Michigan AIChE's 2018 Young Chemical Engineer of the Year Award and three Dow's Donald R. Weyenberg Technical Achievement Awards. In 2020, he was selected by the National Academy of Engineering to participate in the US Frontiers of Engineering Symposium.