Dr. Soorathep Kheawhom is an Associate Professor within the Department of Chemical Engineering at Chulalongkorn University, located in Bangkok, Thailand. Having earned his Bachelor of Engineering degree in Chemical Engineering from the same university in 1997, he subsequently commenced his career at the institution for a span of two years (1997-1999).
Following this, he embarked on his Master's and Ph.D. studies in Japan under the guidance of Prof. Masahiko Hirao, culminating in a Ph.D. from the University of Tokyo in 2004. Post this, he rejoined the Department of Chemical Engineering at Chulalongkorn University.
His initial research endeavored to harness mathematical tools to bolster control, operation, and maintenance activities in chemical processes and probe pivotal phenomena in industrial chemical processes. His current research pivots around the development of next-generation batteries, with a specific emphasis on zinc-air, zinc-ion, and zinc-iodine batteries.
An accomplished author, Dr. Soorathep has contributed numerous articles to peer-reviewed journals and is a frequent keynote speaker at international conferences focused on energy storage technologies. Additionally, he has consistently been a supportive research advisor and mentor to undergraduate students, graduate students, and postdoctoral associates.
Zinc-air batteries have attracted significant attention as promising energy storage, as they exhibit high energy density, cost-effective, safe, and environmentally friendly.
There is an increasing demand for high safety, high energy density, and low-cost rechargeable battery. In this aspect, zinc-ion batteries (ZIBs) have received incremental attention because of their high safety, abundance of zinc source, and environmental friendliness.
Printing technology which is an additive manufacturing technique has been extensively investigated as a promising method for the fabrication of electronic devices. This technique is simple, inexpensive, high throughput and environmental friendly.
Optimization based and model predictive control
Model predictive control (MPC) is recognized as an advanced control algorithm which can effectively handle multiple input multiple output processes with constraints.