S
Sabah Saadi Fayaed
Researcher at Al Maaref University College
Publications - 15
Citations - 500
Sabah Saadi Fayaed is an academic researcher from Al Maaref University College. The author has contributed to research in topics: Reservoir simulation & Artificial neural network. The author has an hindex of 7, co-authored 14 publications receiving 311 citations. Previous affiliations of Sabah Saadi Fayaed include University of Malaya & National University of Malaysia.
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Journal ArticleDOI
Review on heavy metal adsorption processes by carbon nanotubes
Seef Saadi Fiyadh,Mohammed Abdulhakim Alsaadi,Wan Zurina Wan Jaafar,Mohamed Khalid AlOmar,Sabah Saadi Fayaed,Nuruol Syuhadaa Mohd,Lai Sai Hin,Ahmed El-Shafie +7 more
TL;DR: In this article, the authors highlight up-to-date methods for the removal of heavy metals from water using the technique of adsorption, focusing on one particular technique, involving carbon nanotubes (CNTs).
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Reservoir-system simulation and optimization techniques
TL;DR: In this article, an overview of simulation and optimization modeling methods utilized in resolving critical issues with regard to reservoir systems is presented. But, the nonlinearity of natural physical processes causes a major problem in determining the simulation of the reservoir's parameters (elevation, surface-area, storage).
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Integrated Artificial Neural Network (ANN) and Stochastic Dynamic Programming (SDP) Model for Optimal Release Policy
TL;DR: In this article, a Comprehensive Stochastic Dynamic Programming with Artificial Neural Network (SDP-ANN) model was developed and tested at Sg. Langat Reservoir in Malaysia.
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The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach
Seef Saadi Fiyadh,Mohammed Abdulhakim Alsaadi,Mohamed Khalid AlOmar,Sabah Saadi Fayaed,Ako R. Hama,Sharifah Bee,Ahmed El-Shafie +6 more
TL;DR: The ANN model of lead removal was subjected to accuracy determination and the results showed R2 of 0.9956 with MSE of 1.66 × 10-4 for the feed-forward back-propagation and layer recurrent neural network model.
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Improving dam and reservoir operation rules using stochastic dynamic programming and artificial neural network integration model
Sabah Saadi Fayaed,Seef Saadi Fiyadh,Wong Jee Khai,Ali Najah Ahmed,Haitham Abdulmohsin Afan,Rusul Khaleel Ibrahim,Chow Ming Fai,Suhana Koting,Nuruol Syuhadaa Mohd,Wan Zurina Binti Jaafar,Lai Sai Hin,Ahmed El-Shafie +11 more
TL;DR: A comparison of the models shows that the proposed Model 2 increased the reliability and resilience of the system by 7.5% and 6.3%, respectively, while the proposed SDP-ANN model demonstrated greater resilience and reliability with a lower supply deficit.