- Carnegie Mellon University
- Department of Chemical Engineering
- 5000 Forbes Avenue
- Doherty Hall, Room 3100
- Pittsburgh, PA 15213
|2014 — Present||Ph.D. (In progress), Chemical Engineering, Carnegie Mellon University|
|2012 — 2013||M.S., Chemical Engineering, Carnegie Mellon University|
|2008 — 2012||B.S., Polymer Material and Science, Jilin University|
|Spring, 2015||Chemical Engineering Process Control (06-464)|
Adaptive and learning controller and observer for batch chemical reactor.
Chemical batch processes, which by nature have no steady states and many dynamic transitions, raise problems for operation and control. While, one of the traits of batch processes, that they involve repetitive runs, may offer a solution to those problems. In this project, we propose to develop an adaptive control method and a state estimation observer for batch processes based on iterative learning idea. In practice, the online measurements, especially concentrations of species are not always available. Conventionally, the estimations of those unmeasured concentrations are made through first-principle models. While, because of the inaccuracy of kinetic parameters and the dynamic changing in batch processes, those estimation are doubtable. A state estimation observer using concepts of reaction invariants is proposed to recover the unmeasured concentrations without kinetic error. The iterative learning on previous batch runs are used in conjunction to cancel out other estimation errors and disturbances.