AC machine torque and stator flux estimation using a neural network based on the steady-state 2D field model

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

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Stator Flux Linkage Observer Based on RBF Neural Network for IM

2010 Third International Conference on Intelligent Networks and Intelligent Systems ◽

10.1109/icinis.2010.81 ◽

2010 ◽

Author(s):

Sheng-wei Gao

Keyword(s):

Neural Network ◽

Rbf Neural Network ◽

Flux Linkage ◽

Stator Flux

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A strategy for induction motor stator flux estimation using neural networks

10.1109/icems12746.2007.4412126 ◽

2007 ◽

Author(s):

Victor P.B. Aguiar ◽

Ricardo S. The Pontes ◽

Arthur P.S. Braga ◽

Jose Sergio de Aguiar

Keyword(s):

Neural Networks ◽

Induction Motor ◽

Flux Estimation ◽

Stator Flux

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Flame Failures and Recovery in Industrial Furnaces: A Neural Network Steady-State Model for the Firing Rate Setpoint Rearrangement

International Journal of Chemical Engineering ◽

10.1155/2018/3790849 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15

Author(s):

Tahmineh Adili ◽

Zohreh Rostamnezhad ◽

Ali Chaibakhsh ◽

Ali Jamali

Keyword(s):

Neural Network ◽

Steady State ◽

Optimization Problems ◽

Network Models ◽

Economic Loss ◽

Neural Network Models ◽

Firing Rates ◽

State Models ◽

Major Equipment ◽

Artificial Neural Network Models

Burner failures are common abnormal conditions associated with industrial fired heaters. Preventing from economic loss and major equipment damages can be attained by compensating the lost heat due to burners’ failures, which can be possible by defining appropriate setpoints to rearrange the firing rates for healthy burners. In this study, artificial neural network models were developed for estimating the appropriate setpoints for the combustion control system to recover an industrial fired-heater furnace from abnormal conditions. For this purpose, based on an accurate high-order mathematical model, constrained nonlinear optimization problems were solved using the genetic algorithm. For different failure scenarios, the best possible excess firing rates for healthy burners to recover the furnace from abnormal conditions were obtained and data were recorded for training and testing stages. The performances of the developed neural steady-state models were evaluated through simulation experiments. The obtained results indicated the feasibility of the proposed technique to deal with the failures in the combustion system.

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