Model Predictive Control of a Crude Oil Distillation Column

1997 ◽  
Vol 21 (1-2) ◽  
pp. S893-S897
Author(s):  
M Hovd
Keyword(s):  
Model Predictive Control ◽  
Crude Oil ◽  
Predictive Control ◽  
Distillation Column ◽  
Crude Oil Distillation

Model Predictive Control of High‐purity Internal Thermal Coupled Distillation Column

2021 ◽  
Author(s):  
Ja'afar Sulaiman Zangina ◽  
Wenhai Wang ◽  
Weizhong Qin ◽  
Weihua Gui ◽  
Zeyin Zhang ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Purity ◽  
Distillation Column

Development of Neural Network Models for a Crude Oil Distillation Column

2012 ◽  
Author(s):  
Khairiyah Mohd. Yusof ◽  
Fakhri Karray ◽  
Peter L. Douglas
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Crude Oil ◽  
Distillation Column ◽  
Network Models ◽  
Ann Model ◽  
Neural Network Models ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Crude Oil Distillation

This paper discusses the development of artificial neural network (ANN) models for a crude oil distillation column. Since the model is developed for real time optimisation (RTO) applications they are steady state, multivariable models. Training and testing data used to develop the models were generated from a reconciled steady-state model simulated in a process simulator. The radial basis function networks (RBFN), a type of feedforward ANN model, were able to model the crude tower very well, with the root mean square error for the prediction of each variable less than 1%. Grouping related output variables in a network model was found to give better predictions than lumping all the variables in a single model; this also allowed the overall complex, multivariable model to be simplified into smaller models that are more manageable. In addition, the RBFN models were also able to satisfactorily perform range and dimensional extrapolation, which is necessary for models that are used in RTO.

Cooperation-Based Explicit Distributed Model Predictive Control

2013 ◽  
Vol 380-384 ◽  
pp. 707-711 ◽  
Author(s):  
Guo Qi Zhong ◽  
Zhi Yuan Liu
Keyword(s):  
Linear System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Distillation Column ◽  
Control Method ◽  
Parametric Programming ◽  
Distributed Model ◽  
Control Information ◽  
Distributed Model Predictive Control ◽  
Global Cost

in this paper, an explicit distributed model predictive control method for a class of linear system with control information coupling by means of multi-parametric programming is established. In order to get close to the optimal performance of centralized MPC, the method is based on cooperation, which solving weighted global cost instead of local ones. The method is employed on distillation column control problem to verify the efficiency.

Theory and Application of the Lang-Gilles Observer for Distillation Processes

2016 ◽  
Author(s):  
Maxim Stuckert
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Distillation Column ◽  
Simulation Studies ◽  
Multicomponent Mixtures ◽  
Plant Model ◽  
Model Mismatch ◽  
Tray Efficiency ◽  
Full State ◽  
On Line

This thesis deals with the nonlinear full state observer for distillation processes first introduced by Lang and Gilles in 1990. The observer is very attractive in practice as it requires only few temperature measurements in each section of a distillation column and only few observer parameters need to be tuned. We provide conditions under which this observer converges and derive a simple rule for the tuning of the observer parameters. We also give a method for the on-line estimation of the Murphree tray efficiency. Such on-line methods are rarely found in ­literature. In a sequence of simulation studies, we investigate the capabilities of the observer for the estimation of the tray efficiency and for model-predictive control. The simulation studies are based on distillation processes for separation of multicomponent mixtures and one of the studies introduces a plant-model mismatch. ...

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