Application of cascade forward neural network and group method of data handling to modeling crude oil pyrolysis during thermal enhanced oil recovery

MR Mohammadi, A Hemmati-Sarapardeh… - Journal of Petroleum …, 2021 - Elsevier
Oil recovery during in situ combustion is majorly controlled by hydrocarbon oxidation and
pyrolysis reactions, which govern fuel formation and heat evolution. Fuel deposition, in turn …

Comparison of LSSVM model results with artificial neural network model for determination of the solubility of SO2 in ionic liquids

H Mokarizadeh, S Atashrouz, H Mirshekar… - Journal of Molecular …, 2020 - Elsevier
In this study, the least square support vector machine (LSSVM) as a robust approach along
with genetic algorithm (GA) was utilized for prediction of SO2 solubility in ionic liquids (ILs) …

Modeling interfacial tension and minimum miscibility pressure in paraffin-nitrogen systems: Application to gas injection processes

A Hemmati-Sarapardeh, E Mohagheghian - Fuel, 2017 - Elsevier
Nitrogen has emerged as an attractive gas for many petroleum and chemical engineering
applications such as gas lift, gas recycling, pressure maintenance, and enhanced oil …

Application of Nanosilica for inhibition of fines migration during low salinity water injection: Experimental study, mechanistic understanding, and model development

R Moghadasi, A Rostami, A Hemmati-Sarapardeh… - Fuel, 2019 - Elsevier
Low salinity (LS) water injection has emerged as one the most promising methods of
improved oil recovery (IOR). However, its performance is often adversely affected by …

Modeling of reactive orange 16 dye removal from aqueous media by mesoporous silica/crosslinked polymer hybrid using RBF, MLP and GMDH neural network …

HA Tayebi, M Ghanei, K Aghajani… - Journal of Molecular …, 2019 - Elsevier
In this study, SBA-15 mesoporous silica was synthesized and functionalized with cross-
linked polyacrylic acid and used to remove Reactive Orange 16 (RO16) from aqueous …

Modeling heat capacity of ionic liquids using group method of data handling: A hybrid and structure-based approach

A Rostami, A Hemmati-Sarapardeh… - International Journal of …, 2019 - Elsevier
Ionic liquids (ILs) are a significant class of chemicals with applications in solar cells, sensors,
capacitors, batteries, plasticizers and thermal fluids. These compounds have attracted wide …

Modeling the thermal conductivity of ionic liquids and ionanofluids based on a group method of data handling and modified Maxwell model

S Atashrouz, M Mozaffarian… - Industrial & Engineering …, 2015 - ACS Publications
The objective of this study is to develop a model to determine the thermal conductivity of
pure ionic liquids and ionanofluids. In order to estimate the thermal conductivity of pure ionic …

Modeling gas/vapor viscosity of hydrocarbon fluids using a hybrid GMDH-type neural network system

A Dargahi-Zarandi, A Hemmati-Sarapardeh… - Journal of Molecular …, 2017 - Elsevier
Estimation of natural gas viscosity is essential for accurate analysis of gas reserves,
reservoir simulation and optimum gas consumption. The general method for calculation of …

[PDF][PDF] Ramadhan short-term electric load: A hybrid model of cycle spinning wavelet and group method data handling (CSW-GMDH)

RE Caraka, RC Chen, T Toharudin… - IAENG Int J …, 2019 - researchgate.net
In general, performing a nonlinearity time series analysis in the modeling of data can reach
a robust and increase the quality of the results. Wavelet methods have successfully been …

Modeling of carbon dioxide solubility in ionic liquids based on group method of data handling

H Moosanezhad-Kermani, F Rezaei… - Engineering …, 2021 - Taylor & Francis
Due to industrial development, the volume of carbon dioxide (CO2) is rapidly increasing..
Several techniques have been used to eliminate CO2 from the output gas mixtures. One of …