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Home2020July 2020How AI can better serve the chemical process industry

July 2020

Special Focus: The Digital Plant

How AI can better serve the chemical process industry

Years of production have accumulated rich data assets throughout the chemical process industry (CPI).

Lamar University: Lou, H. H.  |  Gai, H.
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The Authors

Lou, H. H. - Lamar University, Beaumont, Texas

Helen H. Lou is a Professor in the Dan F. Smith Department of Chemical Engineering at Lamar University in Beaumont, Texas. Her areas of expertise include big data analytics, machine learning and process systems engineering. She has been applying these technologies for the sustainable development of the chemical processing industry. Dr. Lou has successfully implemented more than 50 projects sponsored by federal, state and private sectors, and has published extensively. She earned her MA degree in computer science and her PhD in chemical engineering, and has trained 16 master’s-level and 9 doctoral-level chemical engineers, as well as influenced many undergraduate chemical engineers. Due to her outstanding achievements, Dr. Lou was elected as a Fellow of the American Institute of Chemical Engineers (AIChE) in 2018.

Gai, H. - Lamar University, Beaumont, Texas

Huilong Gai is a research scientist at Lamar University. He received his PhD in chemical engineering from Lamar University in 2019. His work focuses on the modeling and control of petrochemical processes and combustion kinetic modeling and control of industrial flares. Dr. Gai specializes in various machine-learning algorithms and first-principles models.

From the Archive

Comments

Comments

Ammar AL Khani • 7/26/20 12:09 PM
The black box model is most suitable for a well defined simple process in a deterministic system, that can be extrapolated by a regression model. However, for complex processes that have multiple implicit correlated parameters and in many cases its imperative to describe this complex process through combined Analytical-Mathematical models with the help of AI algorithms and Finite Elements Extrapolation to overcome nonidentified information in the process information matrix. Therefore AI methods are in fact very helpful, when ones being able to determine and describe the process through relevant extracted knowledge from on-line sampled physical data.
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