Absorption of CO2-rich gaseous mixtures in ionic liquids: A computational study

Ghazani, Seyed Hossein Hosseini Nazhad; Baghban, Alireza Akbarzadeh; Mohammadi, Amir H.; Habibzadeh, Sajjad

doi:10.1016/j.supflu.2017.10.024

Cited by 23 publications

(7 citation statements)

References 37 publications

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“…al. 104 (2018) worked on the prediction of the absorption of CO 2 containing common gaseous impurities (mainly focused on greenhouse gases). A LSSVM algorithm was trained on experimental data of ternary mixtures containing two gases and an IL, providing as input no structural details for the ILs.…”

Section: Solubility Of Gasesmentioning

confidence: 99%

A review on machine learning algorithms for the ionic liquid chemical space

et al. 2021

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Section: Solubility Of Gasesmentioning

confidence: 99%

A review on machine learning algorithms for the ionic liquid chemical space

et al. 2021

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“…Lately, heuristic models such as genetic programing, neural networks, and fuzzy logic have generated a lot of attention for their ability to predict thermophysical properties without the need for experimental verification (Aghayari, Maddah, Ahmadi, Yan, & Ghasemi, 2018;Ahmadi, Ahmadi, Mehrpooya, & Rosen, 2015;Ahmadi, Ahmadi, Sadatsakkak, & Feidt, 2015;Ahmadi, Tatar, et al, 2018;Ahmadi, Sorouri Ghare Aghaj, & Nazeri, 2013;Baghban, Bahadori, Mohammadi, & Behbahaninia, 2017;Baghban, Pourfayaz, et al, 2018;Baghban, Jalali, Mohammadi, & Habibzadeh, 2018;Baghban, Kardani, & Habibzadeh, 2017;Baghban, Mohammadi, & Taleghani, 2017;Faizollahzadeh Ardabili et al, 2018;Fotovatikhah et al, 2018;Ghazani, Baghban, Mohammadi, & Habibzadeh, 2018;Kazemi et al, 2018;Li, Zhang, & Liu, 2017;Loni, Asli-Ardeh, Ghobadian, Ahmadi, & Bellos, 2018;Pourkiaei, Ahmadi, & Hasheminejad, 2016;Taormina, Chau, & Sivakumar, 1985;Wu & Chau, 2011;S. Zhang & Chau, 2009;Z.…”

Section: Introductionmentioning

confidence: 99%

Developing an ANFIS-based swarm concept model for estimating the relative viscosity of nanofluids

Baghban

Jalali

Shafiee

et al. 2018

Engineering Applications of Computational Fluid Mechanics

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Nanofluid viscosity is an important physical property in convective heat transfer phenomena. However, the current theoretical models for nanofluid viscosity prediction are only applicable across a limited range. In this study, 1277 experimental data points of distinct nanofluid relative viscosity (NF-RV) were gathered from a plenary literature review. In order to create a general model, adaptive network-based fuzzy inference system (ANFIS) code was expanded based on the independent variables of temperature, nanoparticle diameter, nanofluid density, volumetric fraction, and viscosity of the base fluid. A statistical analysis of the data for training and testing (with R 2 = .99997) demonstrates the accuracy of the model. In addition, the results obtained from ANFIS are compared to similar experimental data and show absolute and maximum average relative deviations of about 0.42 and 6.45%, respectively. Comparisons with other theoretical models from previous research is used to verify the model and prove the prediction capabilities of ANFIS. Consequently, this tool can be of huge value in helping chemists and mechanical and chemical engineers -especially those who are dealing with heat transfer applications by nanofluids -by providing highly accurate predictions of NF-RVs.

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“…In order to realize the impact of each input parameter on the target variables, sensitivity analyses were conducted. A relevancy factor was defined as following to calculate the effect of each parameter quantitatively 43,44 :

r = \frac{\sum_{i = 1}^{n} ((), X_{k . i} - {\overset{false¯}{X}}_{k}) ((), Y_{i} - \overset{Y}{true})}{\sqrt{\sum_{i = 1}^{n} {((), X_{k . i} - {\overset{false¯}{X}}_{k})}^{2} - \sum_{i = 1}^{n} {((), Y_{i} - \overset{Y}{true})}^{2}}}

…”

Section: Methodsmentioning

confidence: 99%

Oxidation kinetics of water contaminants: New insights from artificial intelligence

Keivanimehr

Baghban

Habibzadeh

et al. 2020

Env Prog and Sustain Energy

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Degradation of water contaminants through the advanced oxidation processes (AOP) has become the focus of strategists of environmental science and technology. Hydroxyl radicals are regarded as promising oxidants for the efficient decomposition of the organic contaminants. Nevertheless, understanding and monitoring the kinetics of the hydroxyl radical reaction has remained cumbersome to be deciphered by the aid of mathematical and statistical analyses. Herein, a new stochastic gradient boosting (SGB) decision tree technique based on the quantitative structure–property relationship (QSPR) method was developed to model and capture an image of the degradation rate constant of the hydroxyl radicals. An artificial model was constructed, trained, and tested on the bedrock of 457 different cases of water contaminants from 27 chemical structures. Several statistical techniques, including outlier detection, regression, sensitivity, and error analyses were served to validate the reliability of the proposed model. The outcomes showed that the developed model could appropriately estimate the logarithmic hydroxyl radical rate constants of numerous water contaminants with the promising R‐squared of 0.97, and a quite low mean absolute relative error of 0.86%. Moreover, a sensitivity analysis revealed that Burden eigenvalue was found to be the most effective parameter as the input of the model. Finally, a comparison study was performed between the proposed QSPR and the models previously suggested, where the superiority of the present model was uncovered.

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Absorption of CO2-rich gaseous mixtures in ionic liquids: A computational study

Cited by 23 publications

References 37 publications

A review on machine learning algorithms for the ionic liquid chemical space

A review on machine learning algorithms for the ionic liquid chemical space

Developing an ANFIS-based swarm concept model for estimating the relative viscosity of nanofluids

Oxidation kinetics of water contaminants: New insights from artificial intelligence

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