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Optimization Theory and Algorithms

An effective way of incorporating uncertainty in optimization formulations is to use a two-stage stochastic programming approach, in which multiple scenarios corresponding to uncertainty realizations are embedded within a single optimization formulation. However, the already large- scale dynamic optimization problems typical of complex industrial systems becomes significantly amplified with an increasing number of scenarios. We developed within MACC a novel parallel computing approach for solving large-scale dynamic optimization problems problems of this type. It utilizes a multiple-shooting method for integration of the differential- algebraic equation (DAE) system, in which the time horizon is partitioned into a number of intervals, with the initial states in each interval treated as optimization decision variables. The integration over the intervals for the various scenarios can thus be treated as independent integration tasks, suitable for distribution to multiple processors. Our work includes parallelization of the nonlinear programming solution through sequential quadratic programming with parallel solution of the quadratic programming subproblems.

Dr. Chris L. E. Swartz
Professor and Director, MACC
Dr. Thomas A. Adams II
Associate Professor
Dr. Kamil A. Khan
Assistant Professor
Huiyi Cao
Ph.D. Candidate
Madison Glover
M.A.Sc. Candidate
Chiral Mehta
M.A.Sc. Candidate
Yingkai Song
Ph.D. Candidate
Yingwei Yuan
M.A.Sc. Candidate
Data-Driven Control of Rotational Molding Process
Garg, A., Felipe P.C. Gomes, Mhaskar, P., Michael R. Thompson
American Control Conference, 5117-5122 (2019)  -  [ Publisher Version ]
Model predictive control of uni-axial rotational molding process
Garg, A., Felipe P.C. Gomes, Mhaskar, P., Micheal R. Thompson
Computers & Chemical Engineering, 121 306-316 (2019)  -  [ Publisher Version ]
Data-Driven Advanced in Manufacturing for Batch Polymer Processing Using Multivariate Nondestructive Monitoring
Felipe P.C. Gomes, Garg, A.Mhaskar, P., Michael R. Thompson
Industrial and Engineering Chemistry Research, 23 (58) 9940-9951 (2019)  -  [ Publisher Version ]
Handling Constraints and Raw Material Variability in Rotomolding through Data-Driven Model Predictive Control
Garg, A., Hassan A. Abdulhussain, Mhaskar, P., Michael R. Thompson
Processes, 7 (9) 1-14 (2019)  -  [ Publisher Version ]
Modeling and Control of Batch Processes: Theory and Applications
Springer International Publishing (2019)  -  [ Publisher Version ]
Utilizing Big Data for Batch Process Modeling and Control
Computers and Chemical Engineering (2018)  -  [ Publisher Version ]
Handling multi‐rate and missing data in variable duration economic model predictive control of batch processes
AIChE Journal, 63 (7) 2705-2718 (2017)  -  [ Publisher Version ]
Subspace identification and predictive control of batch particulate processes
American Control Conference, 505-510 (2017)  -  [ Publisher Version ]
Handling multi-rate and missing data in system identification
American Control Conference, 839-844 (2017)  -  [ Publisher Version ]
Development of a high fidelity and subspace identification model of a hydrogen plant startup dynamics
Garg, A.Corbett, B.Mhaskar, P., Hu, G., Flores-Cerrillo, J.
American Control Conference, 2857-2862 (2017)  -  [ Publisher Version ]