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DC Field | Value | Language |
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dc.contributor.author | Meidanshahi, Vida | - |
dc.contributor.author | Corbett, Brandon | - |
dc.contributor.author | Adams, Thomas A. II | - |
dc.contributor.author | Mhaskar, Prashant | - |
dc.date.accessioned | 2017-03-20T19:39:31Z | - |
dc.date.available | 2017-03-20T19:39:31Z | - |
dc.date.issued | 2017-03-18 | - |
dc.identifier.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) | en_US |
dc.identifier.other | 10.1016/j.compchemeng.2017.03.011 | - |
dc.identifier.uri | http://hdl.handle.net/11375/21214 | - |
dc.description.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%. | en_US |
dc.description.sponsorship | Ontario Trillium Scholarships | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.subject | Semicontinuous distillation | en_US |
dc.subject | Model predictive control (MPC) | en_US |
dc.subject | Cascade MPC with PI | en_US |
dc.subject | Subspace identification; | en_US |
dc.subject | Dynamic distillation | en_US |
dc.subject | gProms | en_US |
dc.title | Subspace Model Identification and Model Predictive Control Based Cost Analysis of a Semicontinuous Distillation Process | en_US |
dc.type | Preprint | en_US |
dc.contributor.department | Chemical Engineering | en_US |
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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