An engineer at West Virginia University is developing robust, unconventional AI tools that can reimagine the sustainability of chemical manufacturing.
Yuhe Tian said she believes improvements to current chemical manufacturing processes, including shifts focused on energy sustainability, can be driven by “quantum AI,” or machine learning on a cutting-edge quantum computer that uses subatomic particles to store information and solve problems. With a grant of $240,000 from the National Science Foundation, Tian is launching a two-year project aimed at leveraging quantum intelligence to innovate the design of environmentally friendly chemical plants.
“My team will take a pioneering approach to using quantum machine learning to identify new, optimal, sustainable chemical process designs,” said Tian, assistant professor of chemical and biomedical engineering at the WVU Benjamin M. Statler College of Engineering and Mineral Resources. “We want to determine how the manufacture of chemicals like hydrogen and ammonia can be greener, primarily in terms of reducing carbon dioxide emissions and energy consumption. Instead of heavily relying on human expertise, our framework will systematically generate unconventional process design solutions.”
Austin Braniff, a chemical engineering doctoral student from Mineral Wells, will collaborate with Tian on the fellowship project, which is supported by the EPSCoR RII Track-4 Research Fellows Program and hosted by the Cornell University AI for Science Institute.
According to the U.S. Energy Information Administration, bulk chemical production accounted for 33% of industrial energy consumption in 2020, making it the largest energy user in the domestic industrial sector, with resulting greenhouse gas emissions of 274 million metric tons.
“The global chemical market is highly competitive, so it’s crucial that manufacturers find cleaner but economically viable pathways to chemical production,” Tian said.
“Computer-aided process design” is a tool that screens potential process technologies and set-ups, evaluating trade-offs between different solutions and yielding significant cost savings for manufacturers. Recently, AI has heavily been used with computer-aided process design. However, because chemical manufacturing design is so intricate, involving multiple, different large units working in union—reactors, heat exchangers, separators, etc.—standard AI can take a long time to deliver solutions.
“The issue is how to integrate emerging technologies for chemical manufacturing into existing processes in a way that is more sustainable but ensures the industry remains economically competitive,” Tian said. “That requires both the swift advantage of quantum computing and the intelligent discovery of AI.”