Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches

Baghban, Alireza Akbarzadeh; Ahmadi, Mohammad Ali; Shahraki, Bahram Hashemi

doi:10.1016/j.supflu.2015.01.002

Cited by 193 publications

(50 citation statements)

References 75 publications

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“…ANNs are powerful mathematical tools which are capable of providing a reliable model of a complex and nonlinear system between inputs and outputs through mimicking the biological neural network behavior. To date, ANNs have seen enormous applications in various engineering fields [22][23][24][25][26][27][28][29][30][31][32][33][34]. This paper presents the use of an ANN to accurately estimate CO 2 -brine IFT based on experimental data acquired from previous literature reports.…”

Section: Introductionmentioning

confidence: 99%

Estimation of CO2–brine interfacial tension using an artificial neural network

Zhang

Feng

Wang

et al. 2016

The Journal of Supercritical Fluids

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Section: Introductionmentioning

confidence: 99%

Estimation of CO2–brine interfacial tension using an artificial neural network

Zhang

Feng

Wang

et al. 2016

The Journal of Supercritical Fluids

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“…Recently, intelligence schemes have widely applied in different fields in order to modeling nonlinear relationships between concerned variables (Shafiei et al, 2014;Ahmadi and Baghban, 2015;Baghban et al, 2015a;Baghban et al, 2015b;Amedi et al, 2016;Baghban et al, 2016;Bahadori et al, 2016). Artificial neural network, fuzzy logic system, adaptive network-based fuzzy inference system, and support vector machine are for popular and widely used embranchments of intelligence schemes.…”

Section: Introductionmentioning

confidence: 99%

Modeling of true vapor pressure of petroleum products using ANFIS algorithm

Baghban

Bahadori

Ahmad

et al. 2016

Petroleum Science and Technology

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The aim of this contribution was to develop a simple tool based on fuzzy logic concepts to predict true vapor pressure of volatile petroleum products. In this regard, the adaptive neuro fuzzy inference system was evolved to estimate the true vapor pressure of volatile petroleum products as function of temperature and Reid vapor pressure. In addition, to determine optimal membership function parameters, the particle swarm optimization as an amazing evolutionary algorithm was applied. This predictive tool is suggested as a precise technique to measure the true vapor pressures of typical liquefied petroleum gases, natural gasoline, and motor fuel components at broad ranges of temperatures. This technique was trained and tested by 156 set of data points collected from the reference. The temperature range is 253-373 K and the range of Reid vapor pressure is 35-250 KPa. Results obtained from the present tool found to be in acceptable agreement with the actual reported data in the literature. The values of root mean square error and regression coefficient obtained 5.34 and 0.9975, respectively.

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“…In addition, Genetic Expression Programming (GEP), as a new generation of evolutionary algorithms, was used effectively to predict the H 2 S solubility in ILs [36]. Interested readers should refer to the literature [37][38][39][40][41][42][43][44][45][46][47][48][49][50][51], which provides valuable references on the application of different intelligence modeling approaches in the field of ILs.…”

Section: Introductionmentioning

confidence: 99%