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Identification and predictive control of a multistage evaporator

By J. C. Atuonwu, Yi Cao, G. P. Rangaiah and M. O. Tade

Abstract

A recurrent neural network-based nonlinear model predictive control (NMPC) scheme in parallel with PI control loops is developed for a simulation model of an industrial-scale five-stage evaporator. Input-output data from system identification experiments are used in training the network using the Levenberg- Marquardt algorithm with automatic differentiation. The same optimization algorithm is used in predictive control of the plant. The scheme is tested with set-point tracking and disturbance rejection problems on the plant while control performance is compared with that of PI controllers, a simplified mechanistic model-based NMPC developed in previous work and a linear model predictive controller (LMPC). Results show significant improvements in control performance by the new parallel NMPC-PI control scheme

Topics: Multiple-effect evaporators Nonlinear model predictive control Nonlinear system identification Recurrent neural networks Automatic differentiation recurrent neural-networks automatic differentiation multivariable processes system-identification models reactor backpropagation temperature inverse time
Publisher: Elsevier Science B.V., Amsterdam.
Year: 2010
DOI identifier: 10.1016/j.conengprac.2010.08.002
OAI identifier: oai:dspace.lib.cranfield.ac.uk:1826/5215
Provided by: Cranfield CERES
Journal:

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