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Title: | Subspace Model Identification and Model Predictive Control Based Cost Analysis of a Semicontinuous Distillation Process |
Authors: | Meidanshahi, Vida Corbett, Brandon Adams, Thomas A. II Mhaskar, Prashant |
Department: | Chemical Engineering |
Keywords: | Semicontinuous distillation;Model predictive control (MPC);Cascade MPC with PI;Subspace identification;;Dynamic distillation;gProms |
Publication Date: | 18-Mar-2017 |
Publisher: | Elsevier |
Citation: | Subspace Model Identification and Model Predictive Control Based Cost Analysis of a Semicontinuous Distillation Process Meidanshahi, V., Corbett, B., Adams, T. A. II, Mhaskar, P. Computers & Chemical Engineering, doi:10.1016/j.compchemeng.2017.03.011 (2017) |
Abstract: | Semicontinuous distillation is a process intensification technique for purification of multicomponent mixtures. The system is control-driven and thus the control structure and its tuning parameters have crucial importance in the operation and the economics of the process. In this study, for the first time, a model predictive control (MPC) formulation is implemented on a semicontinuous process to evaluate the associated closed-loop cost. A cascade configuration of MPC and PI controllers is designed in which the setpoints of the PI controllers are determined via a shrinking-horizon MPC. The objective is to reduce the operating cost of a cycle while simultaneously maintaining the required product qualities. A subspace identification method is adopted to identify a linear, state-space model to be used in the MPC. The first-principals model of the process is then simulated in gPROMS. Simulation results demonstrate that the MPC has reduced the operational cost of a semicontinuous process by about 11%. |
URI: | http://hdl.handle.net/11375/21214 |
Identifier: | 10.1016/j.compchemeng.2017.03.011 |
Appears in Collections: | Chemical Engineering Publications |
Files in This Item:
File | Description | Size | Format | |
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Medianshahi 2017 Preprint.pdf | 2.75 MB | Adobe PDF | View/Open |
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