M
Mohammad Ali Ghayyem
Researcher at Petroleum University of Technology
Publications - 14
Citations - 196
Mohammad Ali Ghayyem is an academic researcher from Petroleum University of Technology. The author has contributed to research in topics: Acid gas & Multilayer perceptron. The author has an hindex of 6, co-authored 14 publications receiving 158 citations.
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Charcoal Ash as an Adsorbent for Ni(II) Adsorption and Its Application for Wastewater Treatment
TL;DR: In this article, the authors investigated the sorption characteristic of nanoadsobent charcoal ash (ash) for the removal of Ni(II) from aqueous solutions and wastewater.
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Prediction of solubility of CO2 in ethanol–[EMIM][Tf2N] ionic liquid mixtures using artificial neural networks based on genetic algorithm
TL;DR: Results obtained demonstrated that there is a very little difference between predicted and experimental data of CO2 capture rate giving very low value of average absolute deviation (AAD) and high value of least square (R2) very close to one, indicating high accuracy of this model to predict output variable.
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Developing a simple and accurate correlation for initial estimation of hydrate formation temperature of sweet natural gases using an eclectic approach
TL;DR: In this article, an accurate and simple correlation with only one set of coefficients, achieved using a novel fitting method which can apply for the large range of temperatures (273-299 k), pressures (350-30,000 kPa) and molecular weights (16-29 g/mol).
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Separation of CO2 from CH4 using a synthesized Pebax-1657/ZIF-7 mixed matrix membrane
TL;DR: In this paper, ZIF-7 nanoparticles were first synthesized and then incorporated into polyether-blockamide (Pebax-1657) matrix with different loadings to prepare mixed matrix membranes.
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Intelligent Prediction of CO2 Capture in Propyl Amine Methyl Imidazole Alanine Ionic Liquid: An Artificial Neural Network Model
TL;DR: A new artificial neural network model to predict solubility of CO2 in a new structure of task specific ionic liquids called propyl amine methyl imidazole alanine, which exhibited much better performance in prediction task than RBF neural network with the same neuron numbers in the hidden layer.