- Post Doctoral
MIT Unit Affiliation:
- Chemical Engineering
Post Doc Sponsor / Advisor:
Date PhD Completed:
Top 3 Areas of Expertise:
Ali Sahlodin received his Bachelor’s degree in Chemical Engineering from the University of Tehran in 2005 and his Master’s degree in Chemical Engineering from Sharif University of Technology in 2007. He then joined McMaster University to pursue for a PhD in Chemical Engineering with focus on dynamic process optimization. He received his PhD in 2012, and shortly after, joined the Process Systems Engineering lab in the Department of Chemical Engineering at MIT for a postdoctoral position.
Sahlodin’s research interests lie in the area of process systems engineering, including advanced process control, real-time optimization, and dynamic process optimization. He also has experience in computational fluid dynamics and its application to air quality modeling. Besides research work, Sahlodin has been enthusiastically involved in supervision and teaching to both academic and industrial audience.
Expected End Date of Post Doctoral Position:
Ali Sahlodin is currently focused on optimal strategies for startup and shutdown of chemical processes. Within a continuous plant operation, the startup/shutdown phases are represented by dynamic models, which often exhibit hybrid discrete/continuous behaviors. Such a behavior poses a major challenge in efficient optimization of startup/shutdown operations.
Prior to joining MIT and after completing his PhD, Sahlodin worked on model predictive control and ways of adapting it for directly maximizing economic performance.
Also, in his PhD, Sahlodin developed a method for efficient optimization of transient processes with a guarantee of finding the most economical solution.
Other major research work carried out by Sahlodin includes plantwide real-time optimization of the Tennessee Eastman Benchmark and modeling of roadside vehicular pollutions using computational fluid dynamics.
Efficient global dynamic optimization (GDO) using spatial branch-and-bound (SBB) requires the ability to construct tight bounds for the dynamic model. This thesis works toward efficient GDO by developing effective convex relaxation techniques for models with ordinary differential equations (ODEs). In particular, a novel algorithm, based upon a verified interval ODE method and the McCormick relaxation technique, is developed for constructing convex and concave relaxations of solutions of nonlinear parametric ODEs. In addition to better convergence properties, the relaxations so obtained are guaranteed to be no looser than their underlying interval bounds, and are typically tighter in practice. Moreover, they are rigorous in the sense of accounting for truncation errors. Nonetheless, the tightness of the relaxations is affected by the overestimation from the dependency problem of interval arithmetic that is not addressed systematically in the underlying interval ODE method. To handle this issue, the relaxation algorithm is extended to a Taylor model ODE method, which can provide generally tighter enclosures with better convergence properties than the interval ODE method. This way, an improved version of the algorithm is achieved where the relaxations are generally tighter than those computed with the interval ODE method, and offer better convergence. Moreover, they are guaranteed to be no looser than the interval bounds obtained from Taylor models, and are usually tighter in practice. However, the nonlinearity and (potentially) nonsmoothness of the relaxations impedes their fast and reliable solution. Therefore, the algorithm is finally modified by incorporating polyhedral relaxations in order to generate relatively tight and computationally cheap linear relaxations for the dynamic model. The resulting relaxation algorithm along with a SBB procedure is implemented in the MC++ software package. GDO utilizing the proposed relaxation algorithm is demonstrated to have significantly reduced computational expense, up to orders of magnitude, compared to existing GDO methods.
Top 5 Awards and honors (name of award, date received):
5 Recent Papers:
Ali M. Sahlodin, Benoît Chachuat, (2011). Discretize-then-relax approach for convex/concave relaxations of the solutions of parametric ODEs. Applied Numerical Mathematics, 61 (7): 803-820.
A.M. Sahlodin, B. Chachuat, (2011). Convex/concave relaxations of parametric ODEs using Taylor models. Computers & Chemical Engineering, 35 (5): 844-857.
Ali M. Sahlodin, Benoît Chachuat (2011). Tight Convex and Concave Relaxations via Taylor Models for Global Dynamic Optimization. Computer Aided Chemical Engineering, 29: 537-541.
M. Golshan, R.B. Boozarjomehry, A.M. Sahlodin, M.R. Pishvaie (2011). Fuzzy real-time optimization of the Tennessee Eastman challenge process. Iranian Journal of Chemistry and Chemical Engineering (English edition), 30 (3): 31-44.
Ali M. Sahlodin, Rahmat Sotudeh-Gharebagh, Yifang Zhu (2007). Modeling of dispersion near roadways based on the vehicle-induced turbulence concept. Atmospheric Environment, 41 (1): 92-102.