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• W2734638394 abstract "This paper addresses the problem of subspace identification based modeling and predictive control of batch particulate process with an application to crystal size distribution (CSD) control in a batch crystallizer. To this end, a subspace identification technique is first adapted to identify a linear time invariant model for batch particulate processes. The estimated model is then deployed in a linear model predictive control (MPC) formulation to achieve a particle size distribution with desired characteristics subject to both manipulated input and product quality constraints. The proposed approach is implemented on a seeded batch crystallizer process and compared with an open loop policy as well as a PI controller based trajectory tracking policy. The proposed MPC is shown to achieve 27% and 30% improvements, respectively." @default.
• W2734638394 created "2017-07-21" @default.
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• W2734638394 date "2017-05-01" @default.
• W2734638394 modified "2023-09-26" @default.
• W2734638394 title "Subspace identification and predictive control of batch particulate processes" @default.
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• W2734638394 doi "https://doi.org/10.23919/acc.2017.7963003" @default.
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