Nonlinear model predictive distillation control using an extended neural Hammerstein model

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Title: Nonlinear model predictive distillation control using an extended neural Hammerstein model
Author: Rangaratnam, Balachandran
Abstract: Model Predictive Control has been successfully applied in the chemical and petrochemical industries due to its intuitiveness and constraint handling capabilities . However most currently applied techniques use linear models that are valid only in the neighborhood of the operating point . Model predictive control using nonlinear models does have significant potential for efficient control over a wide operating range . This is particularly important for distillation control which is characterized by highly nonlinear , interactive and nonstationary behavior . The main challenge of nonlinear model predictive control is to develop accurate dynamic models . Phenomenological modeling is difficult , and computationally intensive . Hybrid models , that combine conventional identification techniques with alternative modeling approaches like neural networks , are favored because of their flexibility , computational efficiency , and ability to learn complex nonlinear mappings in a reasonable time . The Hammerstein modeling strategy simplifies the identification by separating the steady -state and transient components . In this project , an extended Hammerstein model was developed for use in a nonlinear model predictive control framework . The static nonlinear element of the Hammerstein model was modeled as a feed -forward neural network model , and the nonlinear dynamic element was identified as transfer function models with input -dependent adaptive dynamic parameters . Two distillation columns were modeled : a propylene -propane (C3 ) splitter operating at base case and at high purity and a toluene -xylene column . Steady -state and dynamic data were obtained from rigorous simulators developed previously . A dynamic model of the C3 splitter at base case using internally recurrent neural networks was also developed . Nonlinear model predictive control using the extended Hammerstein model was tested on dynamic simulations of each column . Nonlinear model predictive control using the recurrent dynamic model was tested on the C3 splitter at base case . The control performance was compared with that of PI controllers for each column for setpoint and disturbance rejection .
URI: http : / /hdl .handle .net /2346 /10997
Date: 1998-05

Citation

Rangaratnam, Balachandran Nonlinear model predictive distillation control using an extended neural Hammerstein model. Master's thesis, Texas Tech University. Available electronically from http : / /hdl .handle .net /2346 /10997 .

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