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Cao Y, Taghvaie Nakhjiri A, Ghadiri M. Breakthrough applications of porous organic materials for membrane-based CO 2 separation: a review. Front Chem 2024; 12:1381898. [PMID: 38576848 PMCID: PMC10991746 DOI: 10.3389/fchem.2024.1381898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 03/11/2024] [Indexed: 04/06/2024] Open
Abstract
Over the last decades, porous organic materials (POMs) have been extensively employed in various industrial approaches including gas separation, catalysis and energy production due to possessing indisputable advantages like great surface area, high permeability, controllable pore size, appropriate functionalization and excellent processability compared to traditional substances like zeolites, Alumina and polymers. This review presents the recent breakthroughs in the multifunctional POMs for potential use in the membrane-based CO2 separation. Some examples of highly-selective membranes using multifunctional POMs are described. Moreover, various classifications of POMs following with their advantages and disadvantages in CO2 separation processes are explained. Apart from reviewing the state-of-the-art POMs in CO2 separation, the challenges/limitations of POMs with tailored structures for reasonable application are discussed.
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Affiliation(s)
- Yan Cao
- School of Computer Science and Engineering, Xi’an Technological University, Xi’an, China
| | - 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, Vietnam
- The Faculty of Environment and Chemical Engineering, Duy Tan University, Da Nang, Vietnam
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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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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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4
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Prediction of molecular diffusivity of organic molecules based on group contribution with tree optimization and SVM models. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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5
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Parsaei M, Roudbari E, Piri F, El-Shafay AS, Su CH, Nguyen HC, Alashwal M, Ghazali S, Algarni M. Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment. Sci Rep 2022; 12:4125. [PMID: 35260785 PMCID: PMC8904475 DOI: 10.1038/s41598-022-08171-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 03/03/2022] [Indexed: 12/17/2022] Open
Abstract
We developed a computational-based model for simulating adsorption capacity of a novel layered double hydroxide (LDH) and metal organic framework (MOF) nanocomposite in separation of ions including Pb(II) and Cd(II) from aqueous solutions. The simulated adsorbent was a composite of UiO-66-(Zr)-(COOH)2 MOF grown onto the surface of functionalized Ni50-Co50-LDH sheets. This novel adsorbent showed high surface area for adsorption capacity, and was chosen to develop the model for study of ions removal using this adsorbent. A number of measured data was collected and used in the simulations via the artificial intelligence technique. Artificial neural network (ANN) technique was used for simulation of the data in which ion type and initial concentration of the ions in the feed was selected as the input variables to the neural network. The neural network was trained using the input data for simulation of the adsorption capacity. Two hidden layers with activation functions in form of linear and non-linear were designed for the construction of artificial neural network. The model's training and validation revealed high accuracy with statistical parameters of R2 equal to 0.99 for the fitting data. The trained ANN modeling showed that increasing the initial content of Pb(II) and Cd(II) ions led to a significant increment in the adsorption capacity (Qe) and Cd(II) had higher adsorption due to its strong interaction with the adsorbent surface. The neural model indicated superior predictive capability in simulation of the obtained data for removal of Pb(II) and Cd(II) from an aqueous solution.
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Affiliation(s)
- Mozhgan Parsaei
- School of Chemistry, College of Science, University of Tehran, Tehran, Iran.
| | - Elham Roudbari
- Department of Chemistry, Faculty of Science, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Farhad Piri
- Electrical Engineering Department, Amirkabir University of Technology, Hafez Avenue, Tehran, Iran
| | - A S El-Shafay
- Department of Mechanical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia.
| | - Chia-Hung Su
- Department of Chemical Engineering, Ming Chi University of Technology, New Taipei City, Taiwan.
| | - Hoang Chinh Nguyen
- Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, 700000, Vietnam
| | - May Alashwal
- Department of Computer Science, Jeddah International College, Jeddah, Saudi Arabia
| | - Sami Ghazali
- Mechanical and Materials Engineering Department, Faculty of Engineering, University of Jeddah, P.O. Box 80327, Jeddah, 21589, Saudi Arabia
| | - Mohammed Algarni
- Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, P.O. Box 344, Rabigh, 21911, Saudi Arabia
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6
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Zhu X, Wang X, Liu K, Zhou S, Alqsair UF, El-Shafay A. Machine learning simulation of Cr (VI) separation from aqueous solutions via a hierarchical nanostructure material. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118565] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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7
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Development of multiple machine-learning computational techniques for optimization of heterogenous catalytic biodiesel production from waste vegetable oil. ARAB J CHEM 2022. [DOI: 10.1016/j.arabjc.2022.103843] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
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8
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Implementation of AdaBoost and genetic algorithm machine learning models in prediction of adsorption capacity of nanocomposite materials. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118527] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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9
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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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10
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Wei Y, Yu J, Du Y, Li H, Su CH. Artificial intelligence simulation of Pb(II) and Cd(II) adsorption using a novel metal organic framework-based nanocomposite adsorbent. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.117681] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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11
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Panwar A, Shirazian S, Singh M, Walker GM. Comprehensive modelling of pharmaceutical solvation energy in different solvents. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.117390] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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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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Zhu H, Zhu L, Sun Z, Khan A. Machine learning based simulation of an anti-cancer drug (busulfan) solubility in supercritical carbon dioxide: ANFIS model and experimental validation. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116731] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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16
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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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17
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Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system. Sci Rep 2021; 11:1308. [PMID: 33446789 PMCID: PMC7809283 DOI: 10.1038/s41598-020-80207-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 12/17/2020] [Indexed: 11/22/2022] Open
Abstract
Computational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.
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18
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Babanezhad M, Behroyan I, Nakhjiri AT, Marjani A, Shirazian S. Performance and application analysis of ANFIS artificial intelligence for pressure prediction of nanofluid convective flow in a heated pipe. Sci Rep 2021; 11:902. [PMID: 33441682 PMCID: PMC7806621 DOI: 10.1038/s41598-020-79628-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 12/10/2020] [Indexed: 11/08/2022] Open
Abstract
Heat transfer augmentation of the nanofluids is still an attractive concept for researchers due to rising demands for designing efficient heat transfer fluids. However, the pressure loss arisen from the suspension of nanoparticles in liquid is known as a drawback for developing such novel fluids. Therefore, prediction of the nanofluid pressure, especially in internal flows, has been focused on studies. Computational fluid dynamics (CFD) is a commonly used approach for such a prediction of fluid flow. The CFD tools are perfect and precise in prediction of the fluid flow parameters. But they might be time-consuming and expensive, especially for complex models such as 3-dimension modeling and turbulent flow. In addition, the CFD could just predict the pressure, and it is disabled for finding the relationship of such variables. This study is intended to show the performance of the artificial intelligence (AI) algorithm as an auxiliary method for cooperation with the CFD. The turbulent flow of Cu/water nanofluid warming up in a pipe is considered as a sample of a physical phenomenon. The AI algorithm learns the CFD results. Then, the relation between the CFD results is discovered by the AI algorithm. For this purpose, the adaptive network-based fuzzy inference system (ANFIS) is adopted as AI tool. The intelligence condition of the ANFIS is checked by benchmarking the CFD results. The paper outcomes indicated that the ANFIS intelligence is met by employing gauss2mf in the model as the membership function and x, y, and z coordinates, the nanoparticle volume fraction, and the temperature as the inputs. The pressure predicted by the ANFIS at this condition is the same as that predicted by the CFD. The artificial intelligence of ANFIS could find the relation of the nanofluid pressure to the nanoparticle fraction and the temperature. The CFD simulation took much more time (90-110 min) than the total time of the learning and the prediction of the ANFIS (369 s). The CFD modeling was done on a workstation computer, while the ANFIS method was run on a normal desktop.
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Affiliation(s)
- Meisam Babanezhad
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
- Faculty of Electrical and Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam
- Department of Artificial Intelligence, Shunderman Industrial Strategy Co., Tehran, Iran
| | - Iman Behroyan
- Department of Computational Fluid Dynamics, Shunderman Industrial Strategy Co., Tehran, Iran
- 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.
| | - Saeed Shirazian
- Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin Prospekt, Chelyabinsk, Russia, 454080
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19
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Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results. Sci Rep 2021; 11:1209. [PMID: 33441681 PMCID: PMC7806800 DOI: 10.1038/s41598-020-79913-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 12/15/2020] [Indexed: 01/29/2023] Open
Abstract
Utilizing artificial intelligence algorithm of adaptive network-based fuzzy inference system (ANFIS) in combination with the computational lfuid dynamics (CFD) has recently revealed great potential as an auxiliary method for simulating challenging fluid mechnics problems. This research area is at the beginning, and needs sophisticated algorithms to be developed. No studies are available to consider the efficiency of the other trainers like differential evolution (DE) integrating with the FIS for capturing the pattern of the simulation results generated by CFD technique. Besides, the adjustment of the tuning parameters of the artificial intelligence (AI) algorithm for finding the highest level of intelligence is unavailable. The performance of AI algorithms in the meshing process has not been considered yet. Therfore, herein the Al2O3/water nanofluid flow in a porous pipe is simulated by a sophisticated hybrid approach combining mechnsitic model (CFD) and AI. The finite volume method (FVM) is employed as the CFD approach. Also, the differential evolution-based fuzzy inference system (DEFIS) is used for learning the CFD results. The DEFIS learns the nanofluid velocity in the y-direction, as output, and the nodes coordinates (i.e., x, y, and z), as inputs. The intelligence of the DEFIS is assessed by adjusting the methd's variables including input number, population number, and crossover. It was found that the DEFIS intelligence is related to the input number of 3, the crossover of 0.8, and the population number of 120. In addition, the nodes increment from 4833 to 774,468 was done by the DEFIS. The DEFIS predicted the velocity for the new dense mesh without using the CFD data. Finally, all CFD results were covered with the new predictions of the DEFIS.
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20
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Babanezhad M, Behroyan I, Marjani A, Shirazian S. Pressure and temperature predictions of Al 2O 3/water nanofluid flow in a porous pipe for different nanoparticles volume fractions: combination of CFD and ACOFIS. Sci Rep 2021; 11:60. [PMID: 33420204 PMCID: PMC7794232 DOI: 10.1038/s41598-020-79689-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 12/11/2020] [Indexed: 11/25/2022] Open
Abstract
Artificial intelligence (AI) techniques have illustrated significant roles in finding general patterns of CFD (Computational fluid dynamics) results. This study is conducted to develop combination of the ant colony optimization (ACO) algorithm with the fuzzy inference system (ACOFIS) for learning the CFD results of a physical case study. This binary join of the ACOFIS and CFD was used for pressure and temperature predictions of Al2O3/water nanofluid flow in a heated porous pipe. The intelligence of ACOFIS is investigated for different input numbers and pheromone effects, as the ant colony tuning parameter. The results showed that the intelligence of the ACOFIS could be found for three inputs (x and y nodes coordinates and nanoparticles fraction) and the pheromone effect of 0.1. At the system intelligence, the ACOFIS could predict the pressure and temperature of the nanofluid on any values of the nanoparticles fraction between 0.5 and 2%. Comparing the ANFIS and the ACOFIS, it was shown that both methods could reach the same accuracy in predictions of the nanofluid pressure and temperature. The root mean square error (RMSE) of the ACOFIS (~ 1.3) was a little more than that of the ANFIS (~ 0.03), while the total process time of the ANFIS (~ 213 s) was a bit more than that of the ACOFIS (~ 198 s). The AI algorithms process time (less than 4 min) shows their ability in the reduction of CFD modeling calculations and expenses.
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Affiliation(s)
- Meisam Babanezhad
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam.,Faculty of Electrical-Electronic Engineering, Duy Tan University, Da Nang, 550000, Viet Nam.,Department of Artificial Intelligence, Shunderman Industrial Strategy Co., Tehran, Iran
| | - Iman Behroyan
- Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran.,Department of Computational Fluid Dynamics, Shunderman Industrial Strategy Co., 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.
| | - Saeed Shirazian
- Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin prospekt, 454080, Chelyabinsk, Russia
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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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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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Babanezhad M, Taghvaie Nakhjiri A, Rezakazemi M, Marjani A, Shirazian S. Functional input and membership characteristics in the accuracy of machine learning approach for estimation of multiphase flow. Sci Rep 2020; 10:17793. [PMID: 33082441 PMCID: PMC7575550 DOI: 10.1038/s41598-020-74858-4] [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: 06/27/2020] [Accepted: 10/08/2020] [Indexed: 11/19/2022] Open
Abstract
In the current study, Artificial Intelligence (AI) approach was used for the learning of a physical system. We applied four inputs and one output in the learning process of AI. In the learning process, the inputs are space locations of a BCR (bubble column reactor), which are x, y, and z coordinate as well as the amount of gas fraction in BCR. The liquid velocity is also considered as output. A variety of functions were used in learning, such as gbellmf and gaussmf functions, to examine which functions can give the best learning. At the end of the study, all of the results were compared to CFD (computational fluid dynamics). A three-dimensional (3D) BCR was used in this research, and we studied simulation by CFD as well as AI. The data from CFD in a 3D BCR was studied in the AI domain. In AI, we tuned for various parameters to achieve the best intelligence in the system. For instance, different inputs, different membership functions, different numbers of membership functions were used in the learning process. Moreover, the meshless prediction was used, meaning that some data in the BCR have not participated in the learning, and they were predicted in the prediction process, which gives us a special capability to compare the results with the CFD outcomes. The findings showed us that AI can predict the CFD results, and a great agreement was achieved between CFD computing nodes and AI elements. This novel methodology can suggest a meshless and multifunctional AI model to simulate the turbulence flow in the BCR. For further evaluation, the ANFIS method is compared with ACOFIS and PSOFIS methods with regards to model’s accuracy. The results show that ANFIS method contains higher accuracy and prediction capability compared with ACOFIS and PSOFIS methods.
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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
| | - 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.
| | - 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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