|
Education
-
B.Eng. in Industrial Automation, 2000, Zhejiang University, China
-
M.Eng. in Systems Engineering, 2003, Zhejiang University, China
-
Ph.D. in Chemical Engineering, 2009, McMaster University, Canada
-
Post-Doctoral Associate, 2009-2011, Process Systems Engineering Laboratory, MIT, USA
Current Research Interests
- My research goal is to develop new theory, technology and software tools to design, operate and control large-scale nonlinear engineering systems under uncertainty. The research will focus on the applications to energy and environment related problems for efficiency and sustainability.Three research topics are of particular interest at the current stage.
1. Global Optimization for Process Design Under Uncertainty
The importance of addressing uncertainty in the design and analysis of energy or environmental engineering systems has been widely recognized. Although such design tasks can be systematically carried out with mathematical programs, the global optimization of the resulting problems, which are usually nonlinear and nonconvex, are very difficult. Therefore, novel global optimization techniques will be developed for efficient solution of stochastic nonconvex programs arising from process design under uncertainty. These techniques include a hybrid branch/decomposition global optimization framework, a decomposition scheme for mluti-stage problems, and effective scenario generation and reduction techniques to model uncertainty.
2. Stochastic Model Predictive Control
Model predictive control (MPC) is the dominant algorithm for centralized multivariable control in the process industries. Explicitly addressing uncertainty has been deemed an important feature of the next-generation industrial MPC technology. This research topic is concerned with the development of a unified MPC framework that allows flexibile uncertainty representation and renders efficient real-tme solution. The new MPC framework is termed stochastic MPC because of its connection to stochastic programming. This framwork will cover both the dynamic optimization and the steady-state optimization in MPC, integrating stability constraints and performance metrics that address explicit uncertainty. The resulting real-time optimization problems will be convex and tratable, and these problems can be further decomposed for parallel computation.
3. Supply Chain Optimization Under Uncertainty
Supply chain optimization (SCO) is a set of approaches utilized to efficiently integrate suppliers, manufacturers, warehouses and stores, so that merchandise is produced and distributed at the right quantities, to the right locations, and at the right time, in order to minimize system-wide costs while satisfying service level requirements. Due to unavoidable and
significant uncertainties in supply chains, SCO methods must take into account the effects of the uncertainties systematically for practical applications, and stochastic programming has become an attractive tool for SCO under uncertainty. However, the relevant stochstic programming problems are usually very challenging, because of their nonconvexity and large dimensionality. This research topic is concerned with the development of efficient solution approaches to these chanllenging problems and the application to supply chain systems in Canada's major prcoess industries, such as petroleum, pulp and paper.
|
