rejection of the feed-flow disturbances in a multi-component distillation column using a multiple neural network model-predictive controller
Authors
abstract
this article deals with the issues associated with developing a new design methodology for the nonlinear model-predictive control (mpc) of a chemical plant. a combination of multiple neural networks is selected and used to model a nonlinear multi-input multi-output (mimo) process with time delays. an optimization procedure for a neural mpc algorithm based on this model is then developed. the proposed scheme has been tested on a model of an 18-plate multi-component distillation column. the algorithm provides excellent disturbance rejection for this process.
similar resources
Rejection of the Feed-Flow Disturbances in a Multi-Component Distillation Column Using a Multiple Neural Network Model-Predictive Controller
This article deals with the issues associated with developing a new design methodology for the nonlinear model-predictive control (MPC) of a chemical plant. A combination of multiple neural networks is selected and used to model a nonlinear multi-input multi-output (MIMO) process with time delays. An optimization procedure for a neural MPC algorithm based on this model is then developed. T...
full textDistillation Column Identification Using Artificial Neural Network
 Abstract: In this paper, Artificial Neural Network (ANN) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. The actual input-output data of the system were measured in order to be used for system identification based on root mean square error (RMSE) minimization approach. It was shown that the designed recurrent neural network is able to pr...
full textModel Predictive Inferential Control of a Distillation Column
Typical production objectives in distillation process require the delivery of products whose compositions meet certain specifications. The distillation control system, therefore, must hold product compositions as near the set points as possible in faces of upset. In this project, inferential model predictive control, that utilizes an artificial neural network estimator and model predictive cont...
full textNeural Network Controller for a Crude Oil Distillation Column
The development of neural network that could be used for the control of an industrial process is discussed. Field data from a working distillation column or fractionator of crude oil refinery in Nigeria was used for the development and testing the effectiveness of the controller. The developed controller performed optimally when compared with the installed distributed control system based on pr...
full textdistillation column identification using artificial neural network
â abstract: in this paper, artificial neural network (ann) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. the actual input-output data of the system were measured in order to be used for system identification based on root mean square error (rmse) minimization approach. it was shown that the designed recurrent neural network is able to pr...
full textA neural network model predictive controller
A neural network controller is applied to the optimal model predictive control of constrained nonlinear systems. The control law is represented by a neural network function approximator, which is trained to minimize a control-relevant cost function. The proposed procedure can be applied to construct controllers with arbitrary structures, such as optimal reduced-order controllers and decentraliz...
full textMy Resources
Save resource for easier access later
Journal title:
iranian journal of chemistry and chemical engineering (ijcce)Publisher: iranian institute of research and development in chemical industries (irdci)-acecr
ISSN 1021-9986
volume 23
issue 2 2004
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023