An expert system design for a crude oil distillation column with the neural networks model and the process optimization using genetic algorithm framework

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.

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Data reconciliation for crude oil distillation units based on self-adaptive probability of mutation in genetic algorithm

Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788) ◽

10.1109/wcica.2004.1343209 ◽

2004 ◽

Author(s):

Feng Li ◽

Xuefeng Yan ◽

Feng Qian

Keyword(s):

Genetic Algorithm ◽

Crude Oil ◽

Data Reconciliation ◽

Crude Oil Distillation ◽

Self Adaptive

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Investigations into Optimization Models of Crude Oil Distillation Column in the Context of Feed Stock and Market Value

Advances in Chemical Engineering and Science ◽

10.4236/aces.2012.24058 ◽

2012 ◽

Vol 02 (04) ◽

pp. 474-480 ◽

Cited By ~ 3

Author(s):

Lekan T. Popoola ◽

Jamiu A. Adeniran ◽

Solomon O. Akinola

Keyword(s):

Crude Oil ◽

Distillation Column ◽

Market Value ◽

Optimization Models ◽

Feed Stock ◽

Crude Oil Distillation

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Development of a System for Monitoring Tool Wear Using Artificial Intelligence Techniques

10.1115/imece2001/dsc-24507 ◽

2001 ◽

Author(s):

Rui. G. Silva ◽

Steven J. Wilcox ◽

Robert L. Reuben

Keyword(s):

Neural Networks ◽

Expert System ◽

Tool Wear ◽

Knowledge Base ◽

Tool Life ◽

Intelligent System ◽

Process Data ◽

Line System ◽

Life Model ◽

The Neural Networks

Abstract The main objective of the work reported here was to develop an intelligent condition monitoring system able to detect when a cutting tool was worn out. To accomplish this objective the use of a hybrid intelligent system, based on an expert system and two neural networks was investigated. The neural networks were employed to process data from sensors and the classifications made by the neural networks were combined with information from the knowledge base to obtain an estimate of the wear state of the tool by the expert system. The novelty of this work is mainly associated with the configuration of the developed system. The combination of sensor-based information and inference rules, results in an on-line system that can learn from experience and update the knowledge base pertaining to information associated with different cutting conditions. The neural networks resolved the problem of interpreting the complex sensor inputs while the expert system, by keeping track of previous success, estimated which of the two neural networks was more reliable. Mis-classifications were filtered out through the use of a rough but approximate estimator, Taylor’s tool life model. The system’s modular structure would make it easy to update as required for different machines and/or processes. The use of Taylor’s tool life model, although weak as a tool life estimator, proved to be crucial in achieving higher performance levels. The application of the Self Organizing Map to tool wear monitoring proved to be slightly more reliable then the Adaptive Resonance Theory neural network although overall the system made reliable, accurate estimates of the tool wear.

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