1
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Fayet G, Rotureau P. QSPR models to predict the physical hazards of mixtures: a state of art. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:745-764. [PMID: 37706255 DOI: 10.1080/1062936x.2023.2253150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 08/24/2023] [Indexed: 09/15/2023]
Abstract
Physical hazards of chemical mixtures, associated for example with their fire or explosion risks, are generally characterized using experimental tools. These tests can be expensive, complex, long to perform and even dangerous for operators. Therefore, for several years and especially with the implementation of the REACH regulation, predictive methods like quantitative structure-property relationships have been encouraged as alternatives tests to determine (eco)toxicological but also physical hazards of chemical substances. Initially, these approaches were intended for pure products, by considering a molecular similarity principle. However, additional to those for pure products, QSPR models for mixtures recently appeared and represent an increasing field of research. This study proposes a state of the art of existing QSPR models specifically dedicated to the prediction of the physical hazards of mixtures. Identified models have been analysed on the key elements of model development (experimental data and fields of application, descriptors used, development and validation methods). It draws up an overview of the potential and limitations of current models as well as areas of progress towards enlarged deployment as a complement to experimental characterizations, for example in the search for safer substances (according to safety-by-design concepts).
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Affiliation(s)
- G Fayet
- Ineris, Parc Technologique Alata, Verneuil-en-Halatte, France
| | - P Rotureau
- Ineris, Parc Technologique Alata, Verneuil-en-Halatte, France
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2
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Zeng F, Wan R, Xiao Y, Song F, Peng C, Liu H. Predicting the Self-Diffusion Coefficient of Liquids Based on Backpropagation Artificial Neural Network: A Quantitative Structure–Property Relationship Study. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c03342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Fazhan Zeng
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai200237, China
| | - Ren Wan
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai200237, China
| | - Yongjun Xiao
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai200237, China
| | - Fan Song
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai200237, China
| | - Changjun Peng
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai200237, China
| | - Honglai Liu
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai200237, China
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Allers JP, Keth J, Alam TM. Prediction of Self-Diffusion in Binary Fluid Mixtures Using Artificial Neural Networks. J Phys Chem B 2022; 126:4555-4564. [PMID: 35675158 DOI: 10.1021/acs.jpcb.2c01723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for individual components in binary fluid mixtures. The ANNs were tested on an experimental database of 4328 self-diffusion constants from 131 mixtures containing 75 unique compounds. The presence of strong hydrogen bonding molecules may lead to clustering or dimerization resulting in non-linear diffusive behavior. To address this, self- and binary association energies were calculated for each molecule and mixture to provide information on intermolecular interaction strength and were used as input features to the ANN. An accurate, generalized ANN model was developed with an overall average absolute deviation of 4.1%. Forward input feature selection reveals the importance of critical properties and self-association energies along with other fluid properties. Additional ANNs were developed with subsets of the full input feature set to further investigate the impact of various properties on model performance. The results from two specific mixtures are discussed in additional detail: one providing an example of strong hydrogen bonding and the other an example of extreme pressure changes, with the ANN models predicting self-diffusion well in both cases.
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Affiliation(s)
- Joshua P Allers
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States.,Virtual Technologies and Engineering, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States
| | - Jane Keth
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States
| | - Todd M Alam
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States.,ACC Consulting New Mexico, Cedar Crest, New Mexico 87008, United States
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Allers JP, Priest CW, Greathouse JA, Alam TM. Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids. J Phys Chem B 2021; 125:12990-13002. [PMID: 34793167 DOI: 10.1021/acs.jpcb.1c07092] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.
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Affiliation(s)
- Joshua P Allers
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States
| | - Chad W Priest
- Geochemistry Department, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States
| | - Jeffery A Greathouse
- Geochemistry Department, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States
| | - Todd M Alam
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States.,ACC Consulting New Mexico, Cedar Crest, New Mexico 87008, United States
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5
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Allers JP, Garzon FH, Alam TM. Artificial neural network prediction of self-diffusion in pure compounds over multiple phase regimes. Phys Chem Chem Phys 2021; 23:4615-4623. [PMID: 33620369 DOI: 10.1039/d0cp06693a] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for pure components in liquid, gas and super critical phases. The ANNs were tested on an experimental database of 6625 self-diffusion constants for 118 different chemical compounds. The presence of multiple phases results in a heavy skew in the distribution of diffusion constants and multiple approaches were used to address this challenge. First, an ANN was developed with the raw diffusion values to assess what the main drawbacks of this direct method were. The first approach for improving the predictions involved taking the log 10 of diffusion to provide a more uniform distribution and reduce the range of target output values used to develop the ANN. The second approach involved developing individual ANNs for each phase using the raw diffusion values. Results show that the log transformation leads to a model with the best self-diffusion constant predictions and an overall average absolute deviation (AAD) of 6.56%. The resultant ANN is a generalized model that can be used to predict diffusion across all three phases and over a diverse group of compounds. The importance of each input feature was ranked using a feature addition method revealing that the density of the compound has the largest impact on the ANN prediction of self-diffusion constants in pure compounds.
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Affiliation(s)
- Joshua P Allers
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, NM 87185, USA.
| | - Fernando H Garzon
- Advanced Materials Laboratory, Sandia National Laboratories, Albuquerque, NM 87185, USA and Center of Micro-Engineered Materials, University of New Mexico, Albuquerque, NM 87106, USA
| | - Todd M Alam
- Department of Organic Materials Science, Sandia National Laboratories, Albuquerque, NM 87185, USA.
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Aniceto JP, Zêzere B, Silva CM. Machine learning models for the prediction of diffusivities in supercritical CO2 systems. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.115281] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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7
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Predictive Models for the Binary Diffusion Coefficient at Infinite Dilution in Polar and Nonpolar Fluids. MATERIALS 2021; 14:ma14030542. [PMID: 33498723 PMCID: PMC7866074 DOI: 10.3390/ma14030542] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 01/07/2021] [Accepted: 01/19/2021] [Indexed: 12/03/2022]
Abstract
Experimental diffusivities are scarcely available, though their knowledge is essential to model rate-controlled processes. In this work various machine learning models to estimate diffusivities in polar and nonpolar solvents (except water and supercritical CO2) were developed. Such models were trained on a database of 90 polar systems (1431 points) and 154 nonpolar systems (1129 points) with data on 20 properties. Five machine learning algorithms were evaluated: multilinear regression, k-nearest neighbors, decision tree, and two ensemble methods (random forest and gradient boosted). For both polar and nonpolar data, the best results were found using the gradient boosted algorithm. The model for polar systems contains 6 variables/parameters (temperature, solvent viscosity, solute molar mass, solute critical pressure, solvent molar mass, and solvent Lennard-Jones energy constant) and showed an average deviation (AARD) of 5.07%. The nonpolar model requires five variables/parameters (the same of polar systems except the Lennard-Jones constant) and presents AARD = 5.86%. These results were compared with four classic models, including the 2-parameter correlation of Magalhães et al. (AARD = 5.19/6.19% for polar/nonpolar) and the predictive Wilke-Chang equation (AARD = 40.92/29.19%). Nonetheless Magalhães et al. requires two parameters per system that must be previously fitted to data. The developed models are coded and provided as command line program.
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Allers JP, Harvey JA, Garzon FH, Alam TM. Machine learning prediction of self-diffusion in Lennard-Jones fluids. J Chem Phys 2020; 153:034102. [DOI: 10.1063/5.0011512] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Affiliation(s)
- Joshua P. Allers
- Department of Organic Materials Science, Albuquerque, New Mexico 87185, USA
| | - Jacob A. Harvey
- Department of Geochemistry, Albuquerque, New Mexico 87185, USA
| | - Fernando H. Garzon
- Department of Power Sources Research and Development, Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
- Center of Micro-Engineered Materials, University of New Mexico, Albuquerque, New Mexico 87106, USA
| | - Todd M. Alam
- Department of Organic Materials Science, Albuquerque, New Mexico 87185, USA
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9
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Liu S, Jin L, Yu H, Lv L, Chen CE, Ying GG. Understanding and predicting the diffusivity of organic chemicals for diffusive gradients in thin-films using a QSPR model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 706:135691. [PMID: 31784180 DOI: 10.1016/j.scitotenv.2019.135691] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 11/20/2019] [Accepted: 11/21/2019] [Indexed: 06/10/2023]
Abstract
The diffusion coefficient (D) is a key physicochemical parameter for the diffusive gradients in thin films technique (DGT) for environmental sampling, which has been extended to organic chemicals (so called o-DGT). D can be measured in the laboratory, although for organic chemicals this parameter might be predicted based on chemical structure. Here we developed for the first time a Quantitative Structure-Property Relationship (QSPR) model to predict the D values. Twenty quantum chemical descriptors that quantify the electronic and energy properties of 120 organic compounds were selected together with molecular mass, solubility and hydrophobicity. The best QSPR model was established by using genetic algorithm and multiple linear regression (GA-MLR). The results indicated that the model derived from the average molecular polarizability (α), the chemical potential (ξ) and the global electrophilicity index (ω) could explain the diffusion of organics in o-DGT and had good statistical performance (R2 = 0.767, RMSE = 0.101). Different validation strategies confirmed that the developed model was robust and predictive. 93% of tested compounds were within the applicability domain (AD) and predicted accurately. We concluded that the proposed QSPR model can serve as an efficient predictive tool for new chemicals in the AD, would be useful to cross validate measured D values and provide a better the understanding of the diffusive behaviour of organics in o-DGT and measurements in the environment. It might also be useful in the non-target analysis with o-DGT for chemicals without measured D values.
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Affiliation(s)
- Sisi Liu
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; School of Environment, South China Normal University, University Town, Guangzhou 510006, China
| | - Lingmin Jin
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
| | - Haiying Yu
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
| | - Liang Lv
- Dalian Product Quality Inspection and Testing Institute Co., Ltd., Dalian, China
| | - Chang-Er Chen
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; School of Environment, South China Normal University, University Town, Guangzhou 510006, China.
| | - Guang-Guo Ying
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; School of Environment, South China Normal University, University Town, Guangzhou 510006, China
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10
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QSPRs for Molecular Diffusion Coefficients in Polymeric Passive Samplers: A Comparison of Simple Molecular and Quantum‐mechanical Sigma‐moment Descriptors. Mol Inform 2019; 38:e1800110. [DOI: 10.1002/minf.201800110] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 04/08/2019] [Indexed: 11/07/2022]
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11
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Borhani TNG, Saniedanesh M, Bagheri M, Lim JS. QSPR prediction of the hydroxyl radical rate constant of water contaminants. WATER RESEARCH 2016; 98:344-53. [PMID: 27124124 DOI: 10.1016/j.watres.2016.04.038] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 02/09/2016] [Accepted: 04/15/2016] [Indexed: 05/24/2023]
Abstract
In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed.
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Affiliation(s)
- Tohid Nejad Ghaffar Borhani
- Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK.
| | - Mohammadhossein Saniedanesh
- Process Systems Engineering Centre (PROSPECT), Research Institute for Sustainable Environment, Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia (UTM), 81310 UTM Johor Bahru, Johor, Malaysia
| | - Mehdi Bagheri
- Department of Chemical and Biological Engineering, The University of British Columbia, 2360 East Mall, Vancouver, BC V6T 1Z3, Canada
| | - Jeng Shiun Lim
- Process Systems Engineering Centre (PROSPECT), Research Institute for Sustainable Environment, Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia (UTM), 81310 UTM Johor Bahru, Johor, Malaysia
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Golzar K, Amjad-Iranagh S, Modarress H. Prediction of Density, Surface Tension, and Viscosity of Quaternary Ammonium-Based Ionic Liquids ([N222(n)]Tf2N) by Means of Artificial Intelligence Techniques. J DISPER SCI TECHNOL 2014. [DOI: 10.1080/01932691.2013.879533] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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13
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Sattari M, Gharagheizi F, Ilani-Kashkouli P, Mohammadi AH, Ramjugernath D. A chemical structure based model for the determination of speed of sound in ionic liquids. J Mol Liq 2014. [DOI: 10.1016/j.molliq.2014.02.041] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Golzar K, Amjad-Iranagh S, Modarress H. Prediction of Thermophysical Properties for Binary Mixtures of Common Ionic Liquids with Water or Alcohol at Several Temperatures and Atmospheric Pressure by Means of Artificial Neural Network. Ind Eng Chem Res 2014. [DOI: 10.1021/ie5007432] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Karim Golzar
- Department
of Chemical Engineering, Amirkabir University of Technology, No. 424,
Hafez Street, Tehran, Iran
| | - Sepideh Amjad-Iranagh
- Department
of Chemistry, Amirkabir University of Technology, No. 424, Hafez Street, Tehran, Iran
| | - Hamid Modarress
- Department
of Chemical Engineering, Amirkabir University of Technology, No. 424,
Hafez Street, Tehran, Iran
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15
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Sattari M, Gharagheizi F, Ilani-Kashkouli P, Mohammadi AH, Ramjugernath D. Estimation of the Heat Capacity of Ionic Liquids: A Quantitative Structure–Property Relationship Approach. Ind Eng Chem Res 2013. [DOI: 10.1021/ie401782n] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Mehdi Sattari
- Thermodynamics Research Unit, School of Engineering, University of KwaZulu-Natal, Howard College Campus, King George V Avenue, Durban 4041, South
Africa
| | - Farhad Gharagheizi
- Thermodynamics Research Unit, School of Engineering, University of KwaZulu-Natal, Howard College Campus, King George V Avenue, Durban 4041, South
Africa
| | - Poorandokht Ilani-Kashkouli
- Thermodynamics Research Unit, School of Engineering, University of KwaZulu-Natal, Howard College Campus, King George V Avenue, Durban 4041, South
Africa
| | - Amir H. Mohammadi
- Thermodynamics Research Unit, School of Engineering, University of KwaZulu-Natal, Howard College Campus, King George V Avenue, Durban 4041, South
Africa
- Institut de Recherche en Génie Chimique et Pétrolier, Paris
Cedex, France
| | - Deresh Ramjugernath
- Thermodynamics Research Unit, School of Engineering, University of KwaZulu-Natal, Howard College Campus, King George V Avenue, Durban 4041, South
Africa
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16
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Response to comment on “QSPR approach for determination of parachor of non-electrolyte organic compounds” [Chem. Eng. Sci. 66 (2012) 2959–2967]. Chem Eng Sci 2013. [DOI: 10.1016/j.ces.2013.05.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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17
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Hemmateenejad B, Ilani-kashkouli P. Quantitative Structure–Property Relationship Study to Predict Speed of Sound in Diverse Organic Solvents from Solvent Structural Information. Ind Eng Chem Res 2012. [DOI: 10.1021/ie3016297] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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18
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Gharagheizi F, Ilani-Kashkouli P, Mohammadi AH, Ramjugernath D, Richon D. Development of a group contribution method for determination of viscosity of ionic liquids at atmospheric pressure. Chem Eng Sci 2012. [DOI: 10.1016/j.ces.2012.06.045] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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19
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Gharagheizi F, Ilani-Kashkouli P, Mirkhani SA, Mohammadi AH. Computation of Upper Flash Point of Chemical Compounds Using a Chemical Structure-Based Model. Ind Eng Chem Res 2012. [DOI: 10.1021/ie202868v] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Farhad Gharagheizi
- Department
of Chemical Engineering, Science and Research Branch, Islamic Azad University,
Tehran, Iran
| | | | - Seyyed Alireza Mirkhani
- Department
of Chemical Engineering, Science and Research Branch, Islamic Azad University,
Tehran, Iran
| | - Amir H. Mohammadi
- MINES ParisTech, CEP/TEP—Centre Énergétique et Procédés, 35 Rue Saint Honoré, 77305 Fontainebleau, France
- Thermodynamics Research Unit,
School of Chemical Engineering, University of KwaZulu-Natal, Howard College Campus, King George V Avenue, Durban 4041, South
Africa
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