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. (FRI) as part of a three-year Oklahoma Center for the Advancement of Science & Technology project.
Feng’s project, “Distillation Column Efficiency by Optimization of Vapor Distribution using an AI and CFD-enabled Digital Twin System,” aims to create a virtual clone, or digital twin, of FRI’s distillation column.
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 costs while also minimizing energy consumption and CO2 emissions.
By implementing the digital twin model, FRI can assess and optimize numerous variables, including prime temperature, sensor location, velocity, flow rate, packed bed design and geometric dimensions, to improve chemical separation and increase productivity.
“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 in an OSU press statement. “Implementing more energy-efficient chemical separation processes could potentially save 100 million metric tons of CO2 emissions annually.”
Feng's future goals for this project include developing an artificial intelligence-empowered, fast-running model that will deliver reliable results. 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 help create the best-packed bed, which will assist in providing the purest product.
