S
Saeed Khezerloo-ye Aghdam
Researcher at Amirkabir University of Technology
Publications - 14
Citations - 249
Saeed Khezerloo-ye Aghdam is an academic researcher from Amirkabir University of Technology. The author has contributed to research in topics: Chemistry & Pulmonary surfactant. The author has an hindex of 4, co-authored 7 publications receiving 111 citations. Previous affiliations of Saeed Khezerloo-ye Aghdam include Petroleum University of Technology.
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Journal ArticleDOI
Assessment of swelling inhibitive effect of CTAB adsorption on montmorillonite in aqueous phase
Aghil Moslemizadeh,Saeed Khezerloo-ye Aghdam,Khalil Shahbazi,Hadi Khezerloo-ye Aghdam,Fatemeh Alboghobeish +4 more
TL;DR: In this paper, the effect of Cetyltrimethylammonium bromide (CTAB) adsorption on Mt in the aqueous phase was investigated.
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A triterpenoid saponin as an environmental friendly and biodegradable clay swelling inhibitor
TL;DR: In this article, a non-ionic surfactant glycyrrhizin (GGRE) was obtained as a clay swelling inhibitor, which was systematically evaluated through various experiments including mud making, filtration, shale cuttings recovery, sedimentation, and scanning electron microscopy (SEM).
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Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields
Abouzar Rajabi Behesht Abad,Hamzeh Ghorbani,Nima Mohamadian,Shadfar Davoodi,Mohammad Mehrad,Saeed Khezerloo-ye Aghdam,Hamid Reza Nasriani +6 more
TL;DR: Comparison of the prediction performance of the HML models developed with those of the previous empirical equations and artificial intelligence models reveals that the novel MELM-PSO model presents superior prediction efficiency and higher computational accuracy.
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Comparison of accuracy and computational performance between the machine learning algorithms for rate of penetration in directional drilling well
Omid Hazbeh,Saeed Khezerloo-ye Aghdam,Hamzeh Ghorbani,Nima Mohamadian,Mehdi Ahmadi Alvar,Jamshid Moghadasi +5 more
TL;DR: It was found that the MLP-ABC algorithm predicts the rate of penetration more accurately, by far, as compared with other methods, which means that this method is applicable to predict the drilling rate in that oilfield.
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Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm
Omid Hazbeh,Mehdi Ahmadi Alvar,Saeed Khezerloo-ye Aghdam,Hamzeh Ghorbani,Nima Mohamadian,Jamshid Moghadasi +5 more
TL;DR: Two hybrid methods multilayer perceptron (MLP) with artificial bee colony (ABC) and firefly (FF) algorithms are introduced to predict this parameter and the results show that MLP-ABC gives the best accuracy for predicting OFVF.