Jay H. Lee (이재형)Professor
Tel : +82-42-350-3926
Fax : +82-42-350-3910
E-mail : email@example.com
Homepage : http://lense.kaist.ac.kr
- 1991 : California Institute of Technology, Pasadena, California (Ph.D. in Chemical Engineering)
- 1986 : University of Washington, Seattle, Washington (B.S. in Chemical Engineering - Magna Cum Laude)
Employment and Professional Experience
- 2000 ~ 2010 : Professor, Georgia Institute of Technology, Atlanta, GA, USA.
- 1998 ~ 2000 : Associate Professor, Purdue University, West Lafayette, IN, USA.
- 1991 ~ 1998 : Assistant / Associate Professor, Auburn University, Auburn, AL, USA.
- 1997 : Visiting Professor, Seoul National University
- 1993 : Visiting Scientist, DuPont Experimental Station, Wilmington, DE, USA.
Awards and Honors
- U.S. National Science Foundation Young Investigator Award (1993).
- Georgia Tech Ziegler Award for Outstanding Faculty Member (2002).
- ASMC 2006 ISMI Best Paper Award (2006).
- SAIC Georgia Tech Student Paper Competition, Winner (2009).
- IEEE Fellow (2011).
- IFAC Fellow (2011).
- Korean Academy of Science and Technology, Fellow (2013).
1. Hall, M., P. Bansal, J. H. Lee, M. J. Realff, and A. S. Bommarius, “Biological Pretreatment of Cellulose: Enhancing Enzymatic Hydrolysis Rate Using Cellulose-Binding Domains from Cellulases,” Bioresource Technology, in press, 2010.
2. Kim, J. K., M. J. Realff, J. H. Lee, C. Whittaker, and L. Furtner, “Design of Biomass Processing Network for Biofuel Production using an MILP Model,” Biomass and Bioenergy, in press, 2010.
3. Hall, Mélanie, Prabuddha Bansal, Matthew J. Realff, Jay H. Lee, Andreas S. Bommarius, “Substrate-based Limitations in the Enzymatic Hydrolysis of Cellulose: Crystallinity is a Key Determinant Conversion Rate Factor but Remains Constant over Hydrolysis,” the FEBS Journal, 277, pp. 1571-1582, 2010.
4. Bansal, P., Mélanie Hall, Matthew J. Realff, Jay H. Lee, Andreas S. Bommarius, “Multivariate statistical analysis of X-ray data from cellulose: A new method to determine degree of crystallinity and predict hydrolysis rates,” Bioresource Technology., 101(12): pp. 4461-4471, 2010.
5. Pratikakis, N., M. J. Realff, and J. H. Lee, “Strategic Capacity Decisions In Manufacturing Using Real-Time Adaptive Dynamic Programming,” Naval Research Logistics, 57(3), pp. 211-224, 2010.
6. Lee, J. H., K. S. Lee and W. C. Kim “Model-Based Iterative Learning Control with a Quadratic Criterion for Time- Varying Linear Systems,” Automatica, 36, pp. 641-657, 2000.
7. Morari, M. and J. H. Lee, “Model Predictive Control : Past, Present and Future,” Computers and Chemical Engineering, 23, pp. 667-682 1999.
8. Lee, J. H. and Z. Yu, “Worst-Case Formulation of Model Predictive Control for Systems with Bounded Parameters,” Automatica, 33, pp. 763-781, 1997.
9. Robertson, D. G., J. H. Lee and J. B. Rawlings, “A Moving Horizon Based Approach for Least Squares Estimation,” AIChE Journal, 42, pp. 2209-2224, 1996.
10. Lee, J. H. and N. L. Ricker, “Extended Kalman Filter Based Model Predictive Control,” Ind. Eng. Chem. Res., pp.1530-1541, 1994.
Laboratory for Energy Systems Engineering (LENSE)
Energy System Modeling and Optimization, Carbon Footprinting and Life Cycle Assessment, Biorefinery Supply Chain, Machine Learning for Protein Engineering, Predictive Control
The main research activities of our laboratory are as follows.
■ New energy technologies:
Bioenergy / Biofuel: With oil prices so volatile, energy generated from biomass for both vehicle fuel and power generation is becoming a major technology focus for the world. The primary rationale for this has been replacement of oil. A second and equally important factor is that biomass-derived fuels are viewed as having an environmentally benign (i.e., carbon-neutral) character, with some even believing their use could be environmentally beneficial. Biomass considered for fuel production include agricultural products and by-products (such as sugar cane or corn stover), wood, grasses (such as switchgrass), and algae. The challenges are the development and improvement of processing techniques, so that biofuels are economically competitive to the traditional fossil-based fuels.
■ Efficiency Improvement:
In addition to developing new energy technologies, the research laboratory addresses the challenges of reducing energy consumption and achieving sustainable energy structures for society's future. Korea recently unveiled an ambitious plan to improve energy efficiency by 28% by Year 2020. We are exploring new technologies to enhance energy efficiency for chemical and other process industries. Other research issues that fall under this challenge include carbon management, combustions, gasification, liquefaction, lighting technologies, power distribution / grid control, sustainability, system design / optimization, and low-energy computing.
■ Systems and Policies:
It is important to provide a holistic picture by assessing energy technologies along with energy / environment policies and economics. In today's society, energy, policy, and economics are inextricably intertwined. Understanding the tradeoffs between energy use and effect on economic prosperity, and how governments regulate the balance is a key factor in determining which technology innovations will succeed and become integrated into the future energy portfolio. Interdisciplinary efforts are needed to analyze and model innovative ways to meet energy demands without negative economic or environmental consequences. Assessments of current and potential energy technologies with an emphasis on innovative adaptation for future use are also critical to sustained economic success. New and renewable energy technologies can be incorporated into the energy mix to enhance and transform current systems as we transition to enabling technologies for a self-sustaining energy economy.
1. IGCC plant modeling, optimization, and control
2. Real-time monitoring and optimization of energy and carbon footprints
3. Biofuel process modeling and control – pretreatment, hydrolysis, and fermentation
4. Biofuel supply chain design and operation
5. Reinforcement Learning based multi-stage decision making under uncertainty
• Real-time Optimization
• Process Scheduling
• Supply Chain Operation
• R & D Portfolio Management
In Reinforcement Learnig, The decisionmaker learns a superior (and eventually optimal) decision policy by interacting with the environment and receiving feedback about its performance.