Prediction of the binary surface tension of mixtures containing ionic liquids using Support Vector Machine algorithms

Hashemkhani, Mohammad; Soleimani, Reza; Fazeli, Hossein; Lee, Moonyong; Bahadori, Alireza; Tavalaeian, Mahsa

doi:10.1016/j.molliq.2015.07.038

Cited by 54 publications

(25 citation statements)

References 114 publications

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“…As a result of the above‐mentioned issues, there is currently a great interest in the development of more accurate and faster models capable of producing highly precise IFT values. Hence, nowadays an alternative approach is using advanced computer‐based models specifically applied to simulate the IFT of CO 2 ‐brine especially at conditions of deep saline aquifers. In recent years, various computer‐based models have been applied successfully in this area , , .…”

Section: Introductionmentioning

confidence: 95%

Estimation of Interfacial Tension for Geological CO₂ Storage

Dehaghani

Soleimani

2019

Chem Eng & Technol

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The effective CO 2 sequestration in saline aquifers as a climate change-lessening solution is mainly governed by the interfacial tension (IFT) behavior between CO 2 and brine. An innovative and competent decision tree-based approach called stochastic gradient boosting (SGB) tree algorithm was applied to predict the CO 2 -aquifer brine IFT as a function of temperature, pressure, and brine salinities. The produced results were compared with the previously reported outcomes of other machine learning models, namely, radial basis function networks, multilayer perceptron networks, least squares support vector machine, and adaptive neuro fuzzy inference system. Amongst all models, the developed SGB tree algorithm provided superior outputs and turned out to be the most accurate tool.

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Section: Introductionmentioning

confidence: 95%

Estimation of Interfacial Tension for Geological CO₂ Storage

Dehaghani

Soleimani

2019

Chem Eng & Technol

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“…These models include predictive quantitative structure–activity relationship (QSAR) models, molecular docking, structure–activity relationship (SAR) systems, read‐across models, physiology‐based pharmacokinetic models, and quantitative toxicity–toxicity relationship (QTTR) models . In addition to toxicity predictions, these models have been successful in forecasting various physicochemical properties of ILs, such as melting points, surface tensions, infinite dilution activity coefficients, viscosities, conductivities, solubilities, glass transition temperatures, and decomposition temperatures …”

Section: Computational Prediction Of the Toxicity Of Ionic Liquidsmentioning

confidence: 99%

An Overview on the Toxicological Properties of Ionic Liquids toward Microorganisms

Sivapragasam

Moniruzzaman

Goto

2020

Biotechnology Journal

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Ionic liquids (ILs), a class of materials with unique physicochemical properties, have been used extensively in the fields of chemical engineering, biotechnology, material sciences, pharmaceutics, and many others. Because ILs are very polar by nature, they can migrate into the environment with the possibility of inclusion in the food chain and bioaccumulation in living organisms. However, the chemical natures of ILs are not quintessentially biocompatible. Therefore, the practical uses of ILs must be preceded by suitable toxicological assessments. Among different methods, the use of microorganisms to evaluate IL toxicity provides many advantages including short generation time, rapid growth, and environmental and industrial relevance. This article reviews the recent research progress on the toxicological properties of ILs toward microorganisms and highlights the computational prediction of various toxicity models.

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“…Recent applications of machine learning in thermodynamics include solubility or phase equilibria , thermal ( pvT ) properties , , caloric properties , , transport properties , , and surface tension , to cite only a few. A substantial part of the recent work is dedicated to properties of ionic liquids , , , , , that are hard to describe otherwise. Machine learning has also been used for describing the properties of crude oil, asphaltene, and natural gas , , , , , , , .…”

Section: A Preliminary Look Into Machine Learningmentioning

confidence: 99%

Digitalization in Thermodynamics

Forte

Jirasek

Bortz

et al. 2019

Chemie Ingenieur Technik

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Digitalization is about data and how they are used. This has always been a key topic in applied thermodynamics. In the present work, the influence of the current wave of digitalization on thermodynamics is analyzed. Thermodynamic modeling and simulation is changing as large amounts of data of different nature and quality become easily available. The power and complexity of thermodynamic models and simulation techniques is rapidly increasing, and new routes become viable to link them to the data. Machine learning opens new perspectives, when it is suitably combined with classical thermodynamic theory. Illustrated by examples, different aspects of digitalization in thermodynamics are discussed: strengths and weaknesses as well as opportunities and threats.

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Prediction of the binary surface tension of mixtures containing ionic liquids using Support Vector Machine algorithms

Cited by 54 publications

References 114 publications

Estimation of Interfacial Tension for Geological CO₂ Storage

Estimation of Interfacial Tension for Geological CO₂ Storage

An Overview on the Toxicological Properties of Ionic Liquids toward Microorganisms

Digitalization in Thermodynamics

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Prediction of the binary surface tension of mixtures containing ionic liquids using Support Vector Machine algorithms

Cited by 54 publications

References 114 publications

Estimation of Interfacial Tension for Geological CO2 Storage

Estimation of Interfacial Tension for Geological CO2 Storage

An Overview on the Toxicological Properties of Ionic Liquids toward Microorganisms

Digitalization in Thermodynamics

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Product

Resources

About

Estimation of Interfacial Tension for Geological CO₂ Storage

Estimation of Interfacial Tension for Geological CO₂ Storage