Evolving machine learning models to predict hydrogen sulfide solubility in the presence of various ionic liquids

Amedi, Hamid Reza; Baghban, Alireza Akbarzadeh; Ahmadi, Mohammad Ali

doi:10.1016/j.molliq.2016.01.060

Cited by 75 publications

(28 citation statements)

References 70 publications

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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%

“…These models include predictive quantitative structure-activity relationship (QSAR) models, [42][43][44][45] molecular docking, [46] structure-activity relationship (SAR) systems, [47] read-across models, [48][49][50] physiology-based pharmacokinetic models, [51,52] and quantitative toxicity-toxicity relationship (QTTR) models. [53] In addition to toxicity predictions, these models have been successful in forecasting various physicochemical properties of ILs, such as melting points, [54,55] surface tensions, [56,57] infinite dilution activity coefficients, [58][59][60] viscosities, [61,62] conductivities, [63,64] solubilities, [65,66] glass transition temperatures, [64] and decomposition temperatures. [67] Determining the relationship between toxicity and structural features is one of the most basic processes of a model and this is made possible through computational chemistry.…”

Section: Computational Prediction Of the Toxicity Of Ionic Liquidsmentioning

confidence: 99%

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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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Section: Computational Prediction Of the Toxicity Of Ionic Liquidsmentioning

confidence: 99%

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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“…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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“…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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Evolving machine learning models to predict hydrogen sulfide solubility in the presence of various ionic liquids

Cited by 75 publications

References 70 publications

An Overview on the Toxicological Properties of Ionic Liquids toward Microorganisms

An Overview on the Toxicological Properties of Ionic Liquids toward Microorganisms

Digitalization in Thermodynamics

Modeling of true vapor pressure of petroleum products using ANFIS algorithm

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