We propose a closed loop strategy to implement simultaneous
scheduling and control for chemical processes subject to disturbances.
Evidences from published work in the literature suggest that the integration
of scheduling and control for multiproduct processes results in optimal
production taking into consideration transition stages. On the basis
of that, we build a closed loop implementation for simultaneous scheduling
and control, which mitigates the effects of disturbances by generating
new solutions once the state deviation due to disturbances is detected.
To test the performance of the proposed methodology, we use two case
studies, and compare their performance with that of open loop implementation.
The results of the case studies verify the effectiveness of closed
loop strategy in dealing with process disturbances.
in Wiley Online Library (wileyonlinelibrary.com) Integration of scheduling and control results in Mixed Integer Nonlinear Programming (MINLP) which is computationally expensive. The online implementation of integrated scheduling and control requires repetitively solving the resulting MINLP at each time interval. (Zhuge and Ierapetritou, Ind Eng Chem Res. 2012;51:8550-8565) To address the online computation burden, we incorporare multi-parametric Model Predictive Control (mp-MPC) in the integration of scheduling and control. The proposed methodology involves the development of an integrated model using continuous-time event-point formulation for the scheduling level and the derived constraints from explicit MPC for the control level.Results of case studies of batch processes prove that the proposed approach guarantees efficient computation and thus facilitates the online implementation.
in Wiley Online Library (wileyonlinelibrary.com) Integration of scheduling and control involves extensive information exchange and simultaneous decision making in industrial practice (Engell and Harjunkoski, Comput Chem Eng. 2012;47:121-133; Baldea and Harjunkoski I, Comput Chem Eng. 2014;71:377-390). Modeling the integration of scheduling and dynamic optimization (DO) at control level using mathematical programming results in a Mixed Integer Dynamic Optimization which is computationally expensive (Flores-Tlacuahuac and Grossmann, Ind Eng Chem Res. 2006;45(20):6698-6712). In this study, we propose a framework for the integration of scheduling and control to reduce the model complexity and computation time. We identify a piece-wise affine model from the first principle model and integrate it with the scheduling level leading to a new integration. At the control level, we use fast Model Predictive Control (fast MPC) to track a dynamic reference. Fast MPC also overcomes the increasing dimensionality of multiparametric MPC in our previous study (Zhuge and Ierapetritou, AIChE J. 2014;60(9):3169-3183). Results of CSTR case studies prove that the proposed approach reduces the computing time by at least two orders of magnitude compared to the integrated solution using mp-MPC. V C 2015 American Institute of Chemical Engineers AIChE J, 61: 3304-3319, 2015 Keywords: integration of scheduling and control, piece-wise affine approximation, fast model predictive control, Multiparametric model predictive control, mixed integer nonlinear programming
Scheduling is often obtained without
the consideration of process
dynamics that affect the transition between steady states where production
takes place. In this work we first formulate the scheduling optimization
problem including process dynamics and then propose a decomposition
approach that results in the efficient solution of the integrated
problem. Optimality Analysis is utilized to prove that the production
sequence and transition times are independent of products’
demands. The proof leads to the decomposition of the integrated problem
into two subproblems that can be solved separately without the need
for iterations. Results of case studies verify the feasibility and
effectiveness of the proposed approach in reducing the computational
complexity of the integrated problem but also obtaining the optimal
solution.
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