演講快訊
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03.29
Physics-Inspired State Space Model Applications of Chemical Engineering: Process Control
康嘉麟 教授
摘要 :
Traditionally, dynamic prediction in chemical processes has required the development of first-principle models followed by parameter adjustment to fit the data from actual operations as closely as possible. However, developing first-principle models is extremely cost-intensive because of the difficulty in gathering critical domain knowledge. The application of state-space models in the field of chemical engineering has sparked significant interest, especially in the area of chemical process control. Recent advancements in state-space models have been notable, propelled by integrating machine learning techniques, particularly Autoencoders, sequence-to-sequence neural networks, and Transformers.
State-space models have extensive applications in chemical engineering, especially in process control and monitoring. These models, capable of representing complex systems with inputs, outputs, and state variables, offer a robust framework for understanding the dynamic behavior of chemical processes. The fusion of physics-inspired and data-driven approaches within state-space models has further amplified their ability to portray and predict the operational states of chemical processes accurately. This study reviewed developments in applying state-space models focusing on chemical process control and highlighted our recent efforts that employ these models for improved process understanding and control.
The research discusses the methods for constructing soft instruments using state-space models at different sampling frequencies and how to integrate the gain directionality of the physical interpretability into the models. It also discusses developing data-driven digital twin models using state-space models, allowing them to replace traditional first-principle modeling and achieve interactive digital twin models.
學經歷 :
EDUCATION
NATIONAL TSING HUA UNIVERSITY
Ph.D. in Chemical Engineering 09/2011-09/2016
Academic Advisor: Dr. Shi-Shang Jang
Dissertation: Modeling Rotating Packed Bed on Absorption of CO2 by Aqueous Amines and Ammonia solutions
TUNG HAI UNIVERSITY
M.S. in Chemical and Materials Engineering 09/2008-06/2010
Academic Advisors: Dr. Hong-Wei Yen
Thesis: Lactic acid production directly from starch in a starch-controlled fed-batch operation using Lactobacillus amylophilus
TUNG HAI UNIVERSITY
B.S. in Chemical Engineering 09/2004-06/2008
PROFESSIONAL EXPERIENCE
NATIONAL YUNLIN UNIVERSITY OF SICENCE AND TECHNOLOGY
Associate Professor 08/2021-Present
Main research:
* Application of Artificial Intelligence (AI) on Chemical process
* CFD Simulation and Industrial Scale-Up Modeling
* Process System Engineering
NATIONAL YUNLIN UNIVERSITY OF SICENCE AND TECHNOLOGY
Assistant Professor 08/2019-07/2021
TAMKANG UNIVERSITY
Assistant Professor 08/2017-07/2019
NATIONAL TSING HUA UNIVERSITY
Postdoctoral Research Fellow 09/2016-07/2017
THE UNIVERSITY OF TEXAS AT AUSTIN
Visiting scholar(Graduate Student Study Abroad Program) 07/2015-07/2016