Artificial neural network-based dynamic equivalents for distribution systems containing active sources

Azmy, Ahmed M.; Erlich, I.; Sowa, P.

doi:10.1049/ip-gtd:20041070

Cited by 67 publications

(33 citation statements)

References 7 publications

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“…ANN is a nonparametric model which utilizes interconnected mathematical nodes or neurons to form a network that can model complex functional relationships (Pai et al, 2008;Sha and Edwards, 2007). So far, different types of neural network architectures and their performances have been studied for the purpose of neuroidentification (Azmy et al, 2004;Park et al, 2005;Singh and Venayagamoorthy, 2002;Venayagamoorthy, 2007). It includes Multi-layer Perceptrons (MLPs), Radial Basis Functions (RBFs), Recurrent Neural Networks (RNNs), and Echo-State Networks (ESNs).…”

Section: Page 6 Of 47mentioning

confidence: 99%

Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach

Bagheri

Mirbagheri

Bagheri

et al. 2015

Process Safety and Environmental Protection

105

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Section: Page 6 Of 47mentioning

confidence: 99%

Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach

Bagheri

Mirbagheri

Bagheri

et al. 2015

Process Safety and Environmental Protection

105

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“…[26] So far, different types of neural network architectures and their performances have been studied. [27][28][29][30][31] This includes multilayer perceptrons (MLPs), radial basis functions (RBFs), recurrent neural networks (RNNs), and echo-state networks (ESNs). In this work, MLP neural network was used.…”

Section: Methods Artificial Neural Networkmentioning

confidence: 99%

Prediction of Chemical Inhibitors Efficiency for Reducing Deposition Thickness Using Artificial Neural Network

Lashkarbolooki

Seyfaee

Esmaeilzadeh

et al. 2014

Journal of Dispersion Science and Technology

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Prediction of efficiency of chemical inhibitors to mitigation of deposition thickness is a key to developing crude oil transportation process. In this work, a feed-forward artificial neural network (ANN) algorithm has been applied to predict the influence of the mitigation effect of ethylene-covinyl acetate (EVA) copolymer and its combination with chloroform (C), acetone (A), P-xylene (PX), and petroleum ether (PE) on the deposition thickness in the pipeline. An optimized three-layer feed-forward ANN model using properties of the oil pipeline such as: inlet oil temperature, environmental (coolant mixture) temperature, oil Reynolds numbers; properties of injected inhibitor such as molecular weight, boiling point, and amount of injection; and time is presented. Different networks are considered and trained using 62661 data sets; the accuracy of the network is validated by 20888 testing data sets. To verify the network generalization, 29 different experiment data sets of four different set of inhibitors have been considered. It is found that the proposed ANN model is an alternative to experimentation and predicts deposition thickness without experimentation, vast information, and tedious and time-consuming calculations.

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“…So far, different types of neural network architectures and their performances have been studied for the purpose of neuroidentification (Azmy et al, 2004;Park et al, 2005;Venayagamoorthy, 2007). It includes MLPs, RBFs, recurrent neural networks (RNNs), and echostate networks (ESNs).…”

Section: Artificial Neural Network Modeling Proceduresmentioning

confidence: 99%

Evaluation and prediction of membrane fouling in a submerged membrane bioreactor with simultaneous upward and downward aeration using artificial neural network-genetic algorithm

Mirbagheri

Bagheri

et al. 2015

Process Safety and Environmental Protection

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Artificial neural network-based dynamic equivalents for distribution systems containing active sources

Cited by 67 publications

References 7 publications

Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach

Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach

Prediction of Chemical Inhibitors Efficiency for Reducing Deposition Thickness Using Artificial Neural Network

Evaluation and prediction of membrane fouling in a submerged membrane bioreactor with simultaneous upward and downward aeration using artificial neural network-genetic algorithm

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