- Post Doctoral
MIT Unit Affiliation:
- Chemical Engineering
Post Doc Sponsor / Advisor:
Date PhD Completed:
Top 3 Areas of Expertise:
My previous research spans a broad range of the process system engineering, such as process simulation, advanced process control, large-scale optimization techniques and their applications for energy problems. Research topics include i) maximization of the expected economic returns of plant-wide system based on the statistical modeling; ii) robust control of nonlinear reactor; iii) advanced process control of distributed parameter system for fluid mechanics and artificial lift; iv) high-performance optimization for the scenario-based stochastic programming and its application in the crude oil procurement for British Petroleum (BP).
My future research theme will be the Modeling, Control and Optimization for Multi-Scale Process Network, which consists of the i) partial differential equation (PDE) based control and estimation for small-scale energy systems; ii) probabilistic modeling and uncertainty characterization for plant-wide process; iii) global/parallel/stochastic optimization approaches for large-scale system.
Expected End Date of Post Doctoral Position:
- Big Data Modeling based Real Time Optimization and Risk Management
- Stabilization and Performance Improvement for Nonlinear Process with Uncertainties
- Optimal Control and Estimation for Partial Differential Equation (PDE) Governed System
- Global Optimization for Refinery Scheduling under Uncertainties
Motivated by the soaring production cost, intensive competitions and public attentions on environmental issues, how to reduce the operational cost, raise the profit and enhance the operational safety attracts tremendous interests in the chemical and petroleum industry. Since the regulatory control strategy may not achieve such rigorous requirements, higher level process control activities, such as production planning, real time optimization (RTO) and multi-variable control are more frequently taken into account. Moreover, to attain the better performance, process control engineers often consider plant-wide operations rather than unit-based actions. As a result, both dynamic and discrete optimization techniques for the large-scale problem nowadays play a more important role in the industry than before.
Even the classical optimization based techniques, such as model predictive control (MPC), have seen considerable successes in many practical applications. However, they are still suffering from computational issues in the circumstances of a large-scale plant, complex dynamic system or the short sampling time period. Furthermore, these traditional optimization techniques usually employ the deterministic formulations, but often become unsuitable for uncertain dynamics. Hence, this thesis
is mainly concerned with developing computationally effective algorithms to solve practical problems arising from those high level process control activities and highly affected by the disturbances.
Approximate dynamic programming (ADP) is one of the most efficient computational frameworks to handle large-scale, stochastic dynamic optimization problems. While a large number of successful cases based on ADP have been reported, several critical issues, including risk management, continuous state space representation and the stability of the control policy, prohibit its application in process control. To overcome these shortcomings,
1. We developed a systematic approach to extract the probabilistic model from the operational data of a plant-wide system and proposed a risk-sensitive RTO approach based on ADP.
2. An innovative procedure for designing control Lyapunov function (CLF) and robust control Lyapunov function (RCLF) is presented for a nonlinear control affine system under the input and state constraints.
3. Based on the well-designed RCLF, a mixed control strategy, combining the advantages of MPC and ADP, is proposed to handle the stability issue of the ADP control scheme.
Apart from the dynamic optimization, another focus of this research is the discrete optimization. Considering mixed integer linear programming (MILP) becomes increasingly common in the planning and scheduling of the chemical production, it is worthwhile to explore a more efficient algorithm for solving this NP hard problem. A modified Benders decomposition approach, featured by its tighter cutting plane, is presented to accelerate the solution procedure.
All the proposed approaches are demonstrated and evaluated by several benchmark examples. The comparisons with previous works also show the superiority of the suggested methods.
5 Recent Papers:
Yang, Y. and P. I. Barton (2015), “Integrated crude selection and refinery optimization under uncertainty”, AIChE Journal, accepted.
Yang, Y. and S. Dubljevic (2014), “Linear matrix inequalities (LMIs) observer and controller design synthesis for parabolic PDE”, European Journal of Control, 20(5), pp. 227–236.
Yang, Y. and S. Dubljevic (2013), “Boundary model predictive control of thin film thickness
modeled by Kuramoto-Sivashinsky equation with input and state constraints”, Journal
of Process Control, 23(9), pp. 1362–1379.
Yang, Y. and J. M. Lee (2013), “A value function-based switching robust control scheme for nonlinear systems”, Journal of Process Control, 23(6), pp. 852–869, 2013.
Yang, Y. and J. M. Lee (2012), “A tighter cut generation strategy for acceleration of Benders decomposition”, Computers & Chemical Engineering, 44, pp. 84–93.