1
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Albedah MA, Li Z, Tlili I. A tripe diffusion bioconvective model for thixotropic nanofluid with applications of induced magnetic field. Sci Rep 2024; 14:8232. [PMID: 38589393 PMCID: PMC11002012 DOI: 10.1038/s41598-024-58195-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 03/26/2024] [Indexed: 04/10/2024] Open
Abstract
Owing to enhanced thermal characteristics of nanomaterials, multidisciplinary applications of such particles have been utilized in the industrial and engineering processes, chemical systems, solar energy, extrusion processes, nuclear systems etc. The aim of current work is to suggests the thermal performances of thixotropic nanofluid with interaction of magnetic force. The suspension of microorganisms in thixotropic nanofluid is assumed. The investigation is further supported with the triple diffusion flow. The motivations for considering the triple diffusion phenomenon are associated to attaining more thermal applications. The flow pattern is subject to novel stagnation point flow. The convective thermal constraints are incorporated. The modeled problem is numerically evaluated by using shooting technique. Different consequences of physical parameters involving the problem are graphically attributed. The insight analysis is presented for proposed problem with different engineering applications. It is claimed that induced magnetic field enhanced due to magnetic parameter while declining results are observed for thixotropic parameter. The heat transfer enhances due to variation of Dufour number. Furthermore, low profile of nanoparticles concentration has been observed for thixotropic parameter and nano-Lewis number.
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Affiliation(s)
- Mohammed A Albedah
- Department of Physics, College of Sciences, Al-Zulfi, Majmaah University, 11952, Al-Majmaah, Saudi Arabia
| | - Zhixiong Li
- Faculty of Mechanical Engineering, Opole University of Technology, 45-758, Opole, Poland
- Yonsei Frontier Lab, Yonsei University, Seoul, 03722, South Korea
| | - Iskander Tlili
- Department of Physics, College of Sciences, Al-Zulfi, Majmaah University, 11952, Al-Majmaah, Saudi Arabia.
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2
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Yuan S, Ajam H, Sinnah ZAB, Altalbawy FMA, Abdul Ameer SA, Husain A, Al Mashhadani ZI, Alkhayyat A, Alsalamy A, Zubaid RA, Cao Y. The roles of artificial intelligence techniques for increasing the prediction performance of important parameters and their optimization in membrane processes: A systematic review. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 260:115066. [PMID: 37262969 DOI: 10.1016/j.ecoenv.2023.115066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/13/2023] [Accepted: 05/22/2023] [Indexed: 06/03/2023]
Abstract
Membrane-based separation processes has been recently of significant global interest compared to other conventional separation approaches due to possessing undeniable advantages like superior performance, environmentally-benign nature and simplicity of application. Computational simulation of fluids has shown its undeniable role in modeling and simulation of numerous physical/chemical phenomena including chemical engineering, chemical reaction, aerodynamics, drug delivery and plasma physics. Definition of fluids can be occurred using the Navier-Stokes equations, but solving the equations remains an important challenge. In membrane-based separation processes, true perception of fluid's manner through disparate membrane modules is an important concern, which has been significantly limited applying numerical/computational procedures such s computational fluid dynamics (CFD). Despite this noteworthy advantage, the optimization of membrane processes using CFD is time-consuming and expensive. Therefore, combination of artificial intelligence (AI) and CFD can result in the creation of a promising hybrid model to accurately predict the model results and appropriately optimize membrane processes and phase separation. This paper aims to provide a comprehensive overview about the advantages of commonly-employed ML-based techniques in combination with the CFD to intelligently increase the optimization accuracy and predict mass transfer and the unfavorable events (i.e., fouling) in various membrane processes. To reach this objective, four principal strategies of AI including SL, USL, SSL and ANN were explained and their advantages/disadvantages were discussed. Then after, prevalent ML-based algorithm for membrane-based separation processes. Finally, the application potential of AI techniques in different membrane processes (i.e., fouling control, desalination and wastewater treatment) were presented.
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Affiliation(s)
- Shuai Yuan
- Information Engineering College, Yantai Institute of Technology, Yantai, Shandong 264005, China.
| | - Hussein Ajam
- Department of Intelligent Medical Systems, Al Mustaqbal University College, Babylon 51001, Iraq
| | - Zainab Ali Bu Sinnah
- Mathematics Department, University Colleges at Nairiyah, University of Hafr Al Batin, Saudi Arabia
| | - Farag M A Altalbawy
- National Institute of Laser Enhanced Sciences (NILES), University of Cairo, Giza 12613, Egypt; Department of Chemistry, University College of Duba, University of Tabuk, Tabuk, Saudi Arabia
| | | | - Ahmed Husain
- Department of Medical Instrumentation, Al-farahidi University, Baghdad, Iraq
| | | | - Ahmed Alkhayyat
- Scientific Research Centre of the Islamic University, The Islamic University, Najaf, Iraq
| | - Ali Alsalamy
- College of Technical Engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna 66002, Iraq
| | | | - Yan Cao
- School of Computer Science and Engineering, Xi'an Technological University, Xi'an 710021, China
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3
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Li W, Musa DAR, Ahmad N, Adil M, Altimari US, Ibrahim AK, Alshehri AM, Riyahi Y, Jaber AS, Kadhim SI, Rushchitc AA, Aljuaid MO. Comprehensive review on the efficiency of ionic liquid materials for membrane separation and environmental applications. CHEMOSPHERE 2023; 332:138826. [PMID: 37150454 DOI: 10.1016/j.chemosphere.2023.138826] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/19/2023] [Accepted: 04/29/2023] [Indexed: 05/09/2023]
Abstract
In the current twenty years, industrial applications of ionic liquids (ILs) have been of paramount attention due to their indisputable positive characteristics like negligible volatility and chemical/thermal stability. These brilliant advantages open new horizons towards environmentally friendly application of ILs in several industrial activities like membrane-based CO2 separation, electrolyte, bioprocessing, targeted drug delivery and solar panels. The principal intention of this article is to prepare a comprehensive review on the potential efficiency of IL-based absorbents to separate CO2 acidic contaminant from industrial gaseous streams compared to alkanolamine absorbents as the benchmark. For this purpose, a techno-economic evaluation is presented to compare the cost-effectiveness of ILs compared to alkanolamine absorbents. Finally, major environmental impacts of the ILs applications in industries are discussed and future perspectives towards solving the operational challenges are presented in detail.
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Affiliation(s)
- Weidong Li
- Hangzhou Normal University Qianjiang College, Zhejiang, Hangzhou, 310018, China; School of Engineering, Hangzhou Normal University, Zhejiang, Hangzhou, 310018, China.
| | - Duaa Abdul Rida Musa
- Chemical Engineering and Petroleum Industries Department, Al-Mustaqbal University College, 51001, Hilla, Babil, Iraq
| | - Nafis Ahmad
- Department of Physics, College of Science, King Khalid University, P.O. Box: 960, Abha, 61421, Kingdom of Saudi Arabia.
| | - Mohaned Adil
- College of Pharmacy, Al-Farahidi University, Iraq
| | - Usama S Altimari
- Department of Medical Laboratories Technology, AL-Nisour University College, Baghdad, Iraq
| | | | - A M Alshehri
- Department of Physics, College of Science, King Khalid University, P.O. Box: 960, Abha, 61421, Kingdom of Saudi Arabia
| | | | - Asala Salam Jaber
- Department of Medical Laboratories Technology, Mazaya University College, Iraq
| | - Sokaina Issa Kadhim
- Building and Construction Technical Engineering Department, College of Technical Engineering, The Islamic University, Najaf, Iraq
| | | | - Mutlaq Owaidh Aljuaid
- Material Management Department, Prince Mansour Military Hospital, Al Faisaliyah, Taif, Saudi Arabia
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4
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Chen Y, Abed AM, Faisal Raheem AB, Altamimi AS, Yasin Y, Abdi Sheekhoo W, Fadhil Smaisim G, Ali Ghabra A, Ahmed Naseer N. Current advancements towards the use of nanofluids in the reduction of CO2 emission to the atmosphere. J Mol Liq 2023. [DOI: 10.1016/j.molliq.2022.121077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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5
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Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models. Sci Rep 2023; 13:1313. [PMID: 36693828 PMCID: PMC9873658 DOI: 10.1038/s41598-022-25562-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 12/01/2022] [Indexed: 01/25/2023] Open
Abstract
Particle size, shape and morphology can be considered as the most significant functional parameters, their effects on increasing the performance of oral solid dosage formulation are indisputable. Supercritical Carbon dioxide fluid (SCCO2) technology is an effective approach to control the above-mentioned parameters in oral solid dosage formulation. In this study, drug solubility measuring is investigated based on artificial intelligence model using carbon dioxide as a common supercritical solvent, at different pressure and temperature, 120-400 bar, 308-338 K. The results indicate that pressure has a strong effect on drug solubility. In this investigation, Decision Tree (DT), Adaptive Boosted Decision Trees (ADA-DT), and Nu-SVR regression models are used for the first time as a novel model on the available data, which have two inputs, including pressure, X1 = P(bar) and temperature, X2 = T(K). Also, output is Y = solubility. With an R-squared score, DT, ADA-DT, and Nu-SVR showed results of 0.836, 0.921, and 0.813. Also, in terms of MAE, they showed error rates of 4.30E-06, 1.95E-06, and 3.45E-06. Another metric is RMSE, in which DT, ADA-DT, and Nu-SVR showed error rates of 4.96E-06, 2.34E-06, and 5.26E-06, respectively. Due to the analysis outputs, ADA-DT selected as the best and novel model and the find optimal outputs can be shown via vector: (x1 = 309, x2 = 317.39, Y1 = 7.03e-05).
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6
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Exploration of the effects of Coriolis force and thermal radiation on water-based hybrid nanofluid flow over an exponentially stretching plate. Sci Rep 2022; 12:21733. [PMID: 36526629 PMCID: PMC9758182 DOI: 10.1038/s41598-022-21799-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 10/04/2022] [Indexed: 12/23/2022] Open
Abstract
Hybrid nanofluids' enhanced thermophysical properties make them applicable in a plethora of mechanical and engineering applications requiring augmented heat transfer. The present study focuses on a three-dimensional Copper-Aluminium Oxide [Formula: see text]-water based hybrid nanofluid flow within the boundary layer with heat transfer over a rotating exponentially stretching plate, subjected to an inclined magnetic field. The sheet rotates at an angular velocity [Formula: see text] and the angle of inclination of the magnetic field is [Formula: see text]. Employing a set of appropriate similarity transformation reduces the governing PDEs to ODEs. The resulting ODEs are solved with the finite difference code with Shooting Technique. Primary velocity increases at large rotation but the secondary velocity reduces as the rotation increases. In addition, the magnetic field is found to oppose the flow and thereby causing a reduction in both the primary and secondary velocities. Increasing the volume fraction reduces the skin friction coefficient and enhances the heat transfer rate.
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7
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Integration of ANN and NSGA-II for Optimization of Nusselt Number and Pressure Drop in a Coiled Heat Exchanger via Water-Based Nanofluid Containing Alumina and Ag Nanoparticles. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-07480-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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8
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Development of a machine learning computational technique for estimation of molecular diffusivity of nonelectrolyte organic molecules in aqueous media. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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9
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Membrane distillation technology for molecular separation: A review on the fouling, wetting and transport phenomena. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2021.118115] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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10
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Zhuang W, Hachem K, Bokov D, Javed Ansari M, Taghvaie Nakhjiri A. Ionic liquids in pharmaceutical industry: A systematic review on applications and future perspectives. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2021.118145] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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11
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Salahshoori I, Ramezani Z, Cacciotti I, Yazdanbakhsh A, Hossain MK, Hassanzadeganroudsari M. Cisplatin uptake and release assessment from hydrogel synthesized in acidic and neutral medium: An experimental and molecular dynamics simulation study. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.117890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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12
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A state-of-the-art review on the application of various pharmaceutical nanoparticles as a promising technology in cancer treatment. ARAB J CHEM 2021. [DOI: 10.1016/j.arabjc.2021.103352] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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13
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Hutapea S, Elveny M, Amin MA, Attia M, Khan A, Sarkar SM. Adsorption of thallium from wastewater using disparate nano-based materials: A systematic review. ARAB J CHEM 2021. [DOI: 10.1016/j.arabjc.2021.103382] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
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14
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Li M, Khan A, Mahlouji MD, Zare MH, Albadarin AB. Catalytic conversion modeling of methanol in dehydration reactor using Voronoi 3D pore network model. ARAB J CHEM 2021. [DOI: 10.1016/j.arabjc.2021.103284] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
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15
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Cao Y, Khan A, Nakhjiri AT, Albadarin AB, Kurniawan TA, Rezakazemi M. Recent advancements in molecular separation of gases using microporous membrane systems: A comprehensive review on the applied liquid absorbents. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116439] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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16
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Thermo-electro-rheological behaviour of vanadium electrolyte-based electrochemical graphene oxide nanofluid designed for redox flow battery. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116860] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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17
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Ghanei-Nasab S, Hadizadeh F, Foroumadi A, Marjani A. A QSAR Study for the Prediction of Inhibitory Activity of Coumarin Derivatives for the Treatment of Alzheimer’s Disease. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-020-05064-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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18
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Cao Y, Rehman ZU, Ghasem N, Al-Marzouqi M, Abdullatif N, Nakhjiri AT, Ghadiri M, Rezakazemi M, Marjani A, Pishnamazi M, Shirazian S. Intensification of CO 2 absorption using MDEA-based nanofluid in a hollow fibre membrane contactor. Sci Rep 2021; 11:2649. [PMID: 33514851 PMCID: PMC7846757 DOI: 10.1038/s41598-021-82304-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 01/19/2021] [Indexed: 11/09/2022] Open
Abstract
Porous hollow fibres made of polyvinylidene fluoride were employed as membrane contactor for carbon dioxide (CO2) absorption in a gas–liquid mode with methyldiethanolamine (MDEA) based nanofluid absorbent. Both theoretical and experimental works were carried out in which a mechanistic model was developed that considers the mass transfer of components in all subdomains of the contactor module. Also, the model considers convectional mass transfer in shell and tube subdomains with the chemical reaction as well as Grazing and Brownian motion of nanoparticles effects. The predicted outputs of the developed model and simulations showed that the dispersion of CNT nanoparticles to MDEA-based solvent improves CO2 capture percentage compared to the pure solvent. In addition, the efficiency of CO2 capture for MDEA-based nanofluid was increased with rising MDEA content, liquid flow rate and membrane porosity. On the other hand, the enhancement of gas velocity and the membrane tortuosity led to reduced CO2 capture efficiency in the module. Moreover, it was revealed that the CNT nanoparticles effect on CO2 removal is higher in the presence of lower MDEA concentration (5%) in the solvent. The model was validated by comparing with the experimental data, and great agreement was obtained.
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Affiliation(s)
- Yan Cao
- School of Mechatronic Engineering, Xi'an Technological University, Xi'an, 710021, China
| | - Zia Ur Rehman
- Department of Chemical & Petroleum Engineering, UAE University, AL-Ain, UAE
| | - Nayef Ghasem
- Department of Chemical & Petroleum Engineering, UAE University, AL-Ain, UAE
| | | | - Nadia Abdullatif
- Department of Chemical & Petroleum Engineering, UAE University, AL-Ain, UAE
| | - Ali Taghvaie Nakhjiri
- Department of Petroleum and Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mahdi Ghadiri
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam.,The Faculty of Environment and Chemical Engineering, Duy Tan University, Da Nang, 550000, Vietnam
| | - Mashallah Rezakazemi
- Faculty of Chemical and Materials Engineering, Shahrood University of Technology, Shahrood, Iran
| | - Azam Marjani
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam. .,Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
| | - Mahboubeh Pishnamazi
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam.,The Faculty of Pharmacy, Duy Tan University, Da Nang, 550000, Vietnam
| | - Saeed Shirazian
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam.,The Faculty of Environmental and Chemical Engineering, Duy Tan University, Da Nang, 550000, Vietnam.,Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin prospekt, 454080, Chelyabinsk, Russia
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19
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Pelalak R, Nakhjiri AT, Marjani A, Rezakazemi M, Shirazian S. Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors. Sci Rep 2021; 11:1891. [PMID: 33479358 PMCID: PMC7820399 DOI: 10.1038/s41598-021-81514-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 01/07/2021] [Indexed: 11/09/2022] Open
Abstract
To understand impact of input and output parameters during optimization and degree of complexity, in the current study we numerically designed a bubble column reactor with a single sparger in the middle of the reactor. After that, some input and output parameters were selected in the post-processing of the numerical method, and then the machine learning observation started to investigate the level of complexity and impact of each input on output parameters. The adaptive neuro-fuzzy inference system (ANFIS) method was exploited as a machine learning approach to analyze the gas-liquid flow in the reactor. The ANFIS method was used as a machine learning approach to simulate the flow of a 3D (three-dimensional) bubble column reactor. This model was also used to analyze the influence of input and output parameters together. More specifically, by analyzing the degree of membership functions as a function of each input, the level of complexity of gas fraction was investigated as a function of computing nodes (X, Y, and Z directions). The results showed that a higher number of membership functions results in a better understanding of the process and higher model accuracy and prediction capability. X and Y computing nodes have a similar impact on the gas fraction, while Z computing points (height of reactor) have a uniform distribution of membership function across the column. Four membership functions (MFs) in each input parameter are insufficient to predict the gas fraction in the 3D bubble column reactor. However, by adding two membership functions, all features of gas fraction in the 3D reactor can be captured by the machine learning algorithm. Indeed, the degree of MFs was considered as a function of each input parameter and the effective parameter was found based on the impact of MFs on the output.
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Affiliation(s)
- Rasool Pelalak
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam.,Faculty of Environmental and Chemical Engineering, Duy Tan University, Da Nang, 550000, Viet Nam
| | - Ali Taghvaie Nakhjiri
- Department of Petroleum and Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Azam Marjani
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. .,Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
| | - Mashallah Rezakazemi
- Faculty of Chemical and Materials Engineering, Shahrood University of Technology, Shahrood, Iran
| | - Saeed Shirazian
- Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin Prospekt, Chelyabinsk, Russia, 454080
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20
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Investigation on performance of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) in a combination of CFD modeling for prediction of fluid flow. Sci Rep 2021; 11:1505. [PMID: 33452362 PMCID: PMC7810899 DOI: 10.1038/s41598-021-81111-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 01/04/2021] [Indexed: 01/29/2023] Open
Abstract
Herein, a reactor of bubble column type with non-equilibrium thermal condition between air and water is mechanistically modeled and simulated by the CFD technique. Moreover, the combination of the adaptive network (AN) trainer with the fuzzy inference system (FIS) as the artificial intelligence method calling ANFIS has already shown potential in the optimization of CFD approach. Although the artificial intelligence method of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) has a good background for optimizing the other fields of research, there are not any investigations on the cooperation of this method with the CFD. The PSOFIS can reduce all the difficulties and simplify the investigation by elimination of the additional CFD simulations. In fact, after achieving the best intelligence, all the predictions can be done by the PSOFIS instead of the massive computational efforts needed for CFD modeling. The first aim of this study is to develop the PSOFIS for use in the CFD approach application. The second one is to make a comparison between the PSOFIS and ANFIS for the accurate prediction of the CFD results. In the present study, the CFD data are learned by the PSOFIS for prediction of the water velocity inside the bubble column. The values of input numbers, swarm sizes, and inertia weights are investigated for the best intelligence. Once the best intelligence is achieved, there is no need to mesh refinement in the CFD domain. The mesh density can be increased, and the newer predictions can be done in an easier way by the PSOFIS with much less computational efforts. For a strong verification, the results of the PSOFIS in the prediction of the liquid velocity are compared with those of the ANFIS. It was shown that for the same fuzzy set parameters, the PSOFIS predictions are closer to the CFD in comparison with the ANFIS. The regression number (R) of the PSOFIS (0.98) was a little more than that of the ANFIS (0.97). The PSOFIS showed a powerful potential in mesh density increment from 9477 to 774,468 and accurate predictions for the new nodes independent of the CFD modeling.
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21
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Babanezhad M, Nakhjiri AT, Marjani A, Rezakazemi M, Shirazian S. Evaluation of product of two sigmoidal membership functions (psigmf) as an ANFIS membership function for prediction of nanofluid temperature. Sci Rep 2020; 10:22337. [PMID: 33339873 PMCID: PMC7749144 DOI: 10.1038/s41598-020-79293-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Accepted: 12/07/2020] [Indexed: 11/12/2022] Open
Abstract
A nanofluid containing water and nanoparticles made of copper (Cu) inside a cavity with square shape is simulated utilizing the computational fluid dynamics (CFD) approach. The nanoparticles made up 15% of the nanofluid. By performing the simulation, the CFD output is characterized by the coordinates in the x, y, nanofluid temperature, and velocity in the y-direction that these outputs are obtained for different physical time iterations. Moreover, the CFD outputs are examined by one of the artificial techniques, i.e. adaptive network-based fuzzy inference system (ANFIS). For this purpose, the data was clustered via grid partition clustering, and the type of membership functions (MFs) was chosen product of two sigmoidal membership functions (psigmf). After reaching 99.9% of intelligence in ANFIS, the nanofluid temperature is predicted for the entire data, which are included in the learning processes. The results showed that the method of ANFIS can predict the thermal properties in different physical times at different computing points without having a training background at those times. Additionally, this study shows that with three membership functions at each input, the model’s accuracy is higher than four functions.
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Affiliation(s)
- Meisam Babanezhad
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam.,Faculty of Electrical - Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam
| | - Ali Taghvaie Nakhjiri
- Department of Petroleum and Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Azam Marjani
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam. .,Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
| | - Mashallah Rezakazemi
- Faculty of Chemical and Materials Engineering, Shahrood University of Technology, Shahrood, Iran
| | - Saeed Shirazian
- Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin prospekt, 454080, Chelyabinsk, Russia
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22
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Babanezhad M, Behroyan I, Nakhjiri AT, Marjani A, Heydarinasab A, Shirazian S. Liquid temperature prediction in bubbly flow using ant colony optimization algorithm in the fuzzy inference system as a trainer. Sci Rep 2020; 10:21884. [PMID: 33318542 PMCID: PMC7736853 DOI: 10.1038/s41598-020-78751-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 11/30/2020] [Indexed: 11/10/2022] Open
Abstract
In the current research paper a novel hybrid model combining first-principle and artificial intelligence (AI) was developed for simulation of a chemical reactor. We study a 2-dimensional reactor with heating sources inside it by using computational fluid dynamics (CFD). The type of considered reactor is bubble column reactor (BCR) in which a two-phase system is created. Results from CFD were analyzed in two different stages. The first stage, which is the learning stage, takes advantage of the swarm intelligence of the ant colony. The second stage results from the first stage, and in this stage, the predictions are according to the previous stage. This stage is related to the fuzzy logic system, and the ant colony optimization learning framework is build-up this part of the model. Ants movements or swarm intelligence of ants lead to the optimization of physical, chemical, or any kind of processes in nature. From point to point optimization, we can access a kind of group optimization, meaning that a group of data is studied and optimized. In the current study, the swarm intelligence of ants was used to learn the data from CFD in different parts of the BCR. The learning was also used to map the input and output data and find out the complex connection between the parameters. The results from mapping the input and output data show the full learning framework. By using the AI framework, the learning process was transferred into the fuzzy logic process through membership function specifications; therefore, the fuzzy logic system could predict a group of data. The results from the swarm intelligence of ants and fuzzy logic suitably adapt to CFD results. Also, the ant colony optimization fuzzy inference system (ACOFIS) model is employed to predict the temperature distribution in the reactor based on the CFD results. The results indicated that instead of solving Navier–Stokes equations and complex solving procedures, the swarm intelligence could be used to predict a process. For better comparisons and assessment of the ACOFIS model, this model is compared with the genetic algorithm fuzzy inference system (GAFIS) and Particle swarm optimization fuzzy inference system (PSOFIS) method with regards to model accuracy, pattern recognition, and prediction capability. All models are at a similar level of accuracy and prediction ability, and the prediction time for all models is less than one second. The results show that the model’s accuracy with low computational learning time can be achieved with the high number of CIR (0.5) when the number of inputs ≥ 4. However, this finding is vice versa, when the number of inputs < 4. In this case, the CIR number should be 0.2 to achieve the best accuracy of the model. This finding could also highlight the importance of sensitivity analysis of tuning parameters to achieve an accurate model with a cost-effective computational run.
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Affiliation(s)
- Meisam Babanezhad
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam.,Faculty of Electrical-Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam
| | - Iman Behroyan
- Mechanical and Energy Engineering Department, Shahid Beheshti University, Tehran, Iran
| | - Ali Taghvaie Nakhjiri
- Department of Petroleum and Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Azam Marjani
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam. .,Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
| | - Amir Heydarinasab
- Department of Petroleum and Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Saeed Shirazian
- Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin Prospekt, 454080, Chelyabinsk, Russia
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23
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Babanezhad M, Behroyan I, Nakhjiri AT, Marjani A, Shirazian S. Computational Modeling of Transport in Porous Media Using an Adaptive Network-Based Fuzzy Inference System. ACS OMEGA 2020; 5:30826-30835. [PMID: 33324792 PMCID: PMC7726747 DOI: 10.1021/acsomega.0c04497] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 11/06/2020] [Indexed: 05/24/2023]
Abstract
This investigation is conducted to study the integration of the artificial intelligence (AI) method with computational fluid dynamics (CFD). The case study is hydrodynamic and heat-transfer analyses of water flow in a metal foam tube under a constant wall heat flux (i.e., 55 kW/m2). The adaptive network-based fuzzy inference system (ANFIS) is an AI method. A 3D CFD model is established in ANSYS-FLUENT software. The velocity of the fluid in the x-direction (Ux) is considered as an output of the ANFIS. The x, y, and z coordinates of the node's location are added to the ANFIS step-by-step to achieve the best intelligence. The number and type of membership functions (MFs) are changed in each step. The training process is done by the CFD results on the tube cross-sections at different lengths (i.e., z = 0.1, 0.2, 0.3, 0.4, 0.6, 0.7, 0.8, and 0.9), while all data (including z = 0.5) are selected for the testing process. The results showed that the ANFIS reaches the best intelligence with all three inputs, five MFs, and "gbellmf"-type MF. At this condition, the regression number is close to 1.
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Affiliation(s)
- Meisam Babanezhad
- Institute
of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
- Faculty
of Electrical−Electronic Engineering, Duy Tan University, Da Nang 550000, Vietnam
| | - Iman Behroyan
- Faculty
of Mechanical and Energy Engineering, Shahid
Beheshti University, Tehran 1983969411, Iran
| | - Ali Taghvaie Nakhjiri
- Department of Petroleum and Chemical
Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran
| | - Azam Marjani
- Department
for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi
Minh City 758307, Vietnam
- Faculty
of
Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Saeed Shirazian
- Department
of Chemical Sciences, Bernal Institute, University of Limerick, Limerick V94 T9PX, Ireland
- Laboratory
of Computational Modeling of Drugs, South
Ural State University, 76 Lenin prospekt, Chelyabinsk 454080, Russia
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24
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Babanezhad M, Behroyan I, Nakhjiri AT, Marjani A, Rezakazemi M, Shirazian S. High-performance hybrid modeling chemical reactors using differential evolution based fuzzy inference system. Sci Rep 2020; 10:21304. [PMID: 33277606 PMCID: PMC7718251 DOI: 10.1038/s41598-020-78277-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 11/23/2020] [Indexed: 11/09/2022] Open
Abstract
Bubbly flow behavior simulation in two-phase chemical reactors such bubble column type reactors is widely employed for chemical industry purposes. The computational fluid dynamics (CFD) approach has been employed by engineers and researchers for modeling these types of chemical reactors. In spite of the CFD robustness for simulating transport phenomena and chemical reactions in these reactors, this approach has been known as expensive for modeling such turbulent complex flows. Artificial intelligence (AI) algorithm of the adaptive network-based fuzzy inference system (ANFIS) are largely understood and utilized for the CFD approach optimization. In this hybrid approach, the CFD findings are learned by AI algorithms like ANFIS to save computational time and expenses. Once the pattern of the CFD results have been captured by the AI model, this hybrid model can be then used for process simulation and optimization. As such, there is no need for further simulations of new conditions. The objective of this paper is to obviate the need for expensive CFD computations for two-phase flows in chemical reactors via coupling CFD data to an AI algorithm, i.e., differential evolution based fuzzy inference system (DEFIS). To do so, air velocity as the output and the values of the x, and y coordinates, water velocity, and time step as the inputs are inputted the AI model for learning the flow pattern. The effects of cross over as the DE parameter and also the number of inputs on the best intelligence are investigated. Indeed, DEFIS correlates the air velocity to the nodes coordinates, time, and liquid velocity and then after the CFD modeling could be replaced with the simple correlation. For the first time, a comparison is made between the ANFIS and the DEFIS performances in terms of the prediction capability of the gas (air) velocity. The results released that both ANFIS and DEFIS could accurately predict the CFD pattern. The prediction times of both methods were obtained to be equal. However, the learning time of the DEFIS was fourfold of ANFIS.
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Affiliation(s)
- Meisam Babanezhad
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam.,Faculty of Electrical-Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam
| | - Iman Behroyan
- Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran
| | - Ali Taghvaie Nakhjiri
- Department of Petroleum and Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Azam Marjani
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam. .,Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
| | - Mashallah Rezakazemi
- Faculty of Chemical and Materials Engineering, Shahrood University of Technology, Shahrood, Iran
| | - Saeed Shirazian
- Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin prospekt, 454080, Chelyabinsk, Russia
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25
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Prediction of turbulence eddy dissipation of water flow in a heated metal foam tube. Sci Rep 2020; 10:19280. [PMID: 33159145 PMCID: PMC7648062 DOI: 10.1038/s41598-020-76260-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 10/26/2020] [Indexed: 11/29/2022] Open
Abstract
The insertion of porous metal media inside the pipes and channels has already shown a significant heat transfer enhancement by experimental and numerical studies. Porous media could make a mixing flow and small-scale eddies. Therefore, the turbulence parameters are attractive in such cases. The computational fluid dynamics (CFD) approach can predict the turbulence parameters using the turbulence models. However, the CFD is unable to find the relation of the turbulence parameters to the boundary conditions. The artificial intelligence (AI) has shown potential in combination with the CFD to build high-performance predictive models. This study is aimed to establish a new AI algorithm to capture the patterns of the CFD results by changing the system’s boundary conditions. The ant colony optimization-based fuzzy inference system (ACOFIS) method is used for the first time to reduce time and computational effort needed in the CFD simulation. This investigation is done on turbulent forced convection of water through an aluminum metal foam tube under constant wall heat flux. The ANSYS-FLUENT CFD software is used for the simulations. The x and y of the fluid nodal locations, inlet temperature, velocity, and turbulent kinetic energy (TKE) are the inputs of the ACOFIS to predict turbulence eddy dissipation (TED) as the output. The results revealed that for the best intelligence of the ACOFIS, the number of inputs, the number of ants, the number of membership functions (MFs) and the rule are 5, 10, 93 and 93, respectively. Further comparison is made with the adaptive network-based fuzzy inference system (ANFIS). The coefficient of determination for both methods was close to 1. The ANFIS showed more learning and prediction times (785 s and 10 s, respectively) than the ACOFIS (556 s and 3 s, respectively). Finding the member function versus the inputs, the value of TED is calculated without the CFD modeling. So, solving the complicated equations by the CFD is replaced with a simple correlation.
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26
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Babanezhad M, Nakhjiri AT, Marjani A, Shirazian S. Pattern recognition of the fluid flow in a 3D domain by combination of Lattice Boltzmann and ANFIS methods. Sci Rep 2020; 10:15908. [PMID: 32985599 PMCID: PMC7522723 DOI: 10.1038/s41598-020-72926-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/09/2020] [Indexed: 11/15/2022] Open
Abstract
Many numerical methods have been used to simulate the fluid flow pattern in different industrial devices. However, they are limited with modeling of complex geometries, numerical stability and expensive computational time for computing, and large hard drive. The evolution of artificial intelligence (AI) methods in learning large datasets with massive inputs and outputs of CFD results enables us to present completely artificial CFD results without existing numerical method problems. As AI methods can not feel barriers in numerical methods, they can be used as an assistance tool beside numerical methods to predict the process in complex geometries and unstable numerical regions within the short computational time. In this study, we use an adaptive neuro-fuzzy inference system (ANFIS) in the prediction of fluid flow pattern recognition in the 3D cavity. This prediction overview can reduce the computational time for visualization of fluid in the 3D domain. The method of ANFIS is used to predict the flow in the cavity and illustrates some artificial cavities for a different time. This method is also compared with the genetic algorithm fuzzy inference system (GAFIS) method for the assessment of numerical accuracy and prediction capability. The result shows that the ANFIS method is very successful in the estimation of flow compared with the GAFIS method. However, the GAFIS can provide faster training and prediction platform compared with the ANFIS method.
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Affiliation(s)
- Meisam Babanezhad
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam.,Faculty of Electrical - Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam
| | - Ali Taghvaie Nakhjiri
- Department of Petroleum and Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Azam Marjani
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam. .,Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
| | - Saeed Shirazian
- Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick, Ireland.,Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin Prospekt, Chelyabinsk, Russia, 454080
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