Digital twin system used to improve distillation by OSU CEAT associate professor

Digital twin system used to improve distillation by OSU CEAT associate professor

Tuesday, September 3, 2024

Media Contact: Desa James | Communications Coordinator | 405-744-2669 | [email protected]

Dr. Yu Feng, associate professor of chemical engineering at Oklahoma State University's College of Engineering, Architecture and Technology, was recently awarded a grant from Fractionation Research Inc. as part of a three-year Oklahoma Center for the Advancement of Science & Technologyproject.  

Feng's project is titled "Distillation Column Efficiency by Optimization of Vapor Distribution using an AI and CFD enabled Digital Twin System." The goal of the project is to create a virtual clone of the distillation column used by FRI. This replica is known as a digital twin, which is a virtual representation of the physical system.  

The real-life column used to separate different components of crude oil can be five stories tall. This scale makes testing various flow rates and separation efficiencies both challenging and expensive. Feng's digital twin model can help expedite optimization of the distillation process and save the company money while also minimizing energy consumption and CO₂emissions.  

Physical model of FRI's distillation column for which Feng's lab will develop the digital twin system .

By implementing the digital twin model, the company can assess numerous variables to optimize the separation process and increase productivity. Prime temperature, location of sensors, velocity, flow rate, packed bed design and geometric dimensions are just a few of the components that Feng's project will be able to assist with. 

Distillation is a physical separation process where crude oil is heated, and its components are separated based on their different boiling points. This process is widely used in various industries to produce products such as gasoline, diesel, kerosene, and even raw materials for cosmetics and other consumer goods.  

"Chemical separations account for approximately half of the U.S.'s industrial energy use and 10-15% of the U.S.'s total energy consumption," Feng said. "Implementing more energy-efficient chemical separation processes could potentially save 100 million metric tons of CO₂emissions annually." 

By providing a way for companies to test different processes virtually using computational fluid dynamics, the distillation process can be improved to help solve the key challenge of reducing energy consumption. 

Feng's future goals for this project include developing an artificial intelligence-empowered, fast-running model that will deliver reliable results for industry companies. Additionally, the project will focus on enhancing the detail of the distillation column simulation and creating more complex packed bed designs. These advancements could lead to a greater return on investment for companies by improving process efficiency and reducing operational costs. 

Distillation columns are filled or "packed" with a material that allows rising vapors to contact descending condensate. The more vapor-liquid contact the better the separation. Feng's research will work to create the best-packed bed that will assistin providing the purest product.  

The Technovate Distillation Built in 1954 for Unit Operation Lab at Oklahoma State University.

OSU's School of Chemical Engineeringhas a unit operations lab that houses two digital twin models that students can further their fundamental knowledge with by having access to different methods to learn. Feng would like to continue to add more digital twin models to the lab providing more real-world applications for OSU students.  

Feng's goal for his lab is to create the first principle-based digital twin systems to help various engineering applications.   

Public link

Please use the above public link if you want to share this noodl on another website.