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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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2
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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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3
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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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4
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Machine learning-based CFD simulations: a review, models, open threats, and future tactics. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07838-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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5
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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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6
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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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7
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State-of-the-Art Review on the Application of Membrane Bioreactors for Molecular Micro-Contaminant Removal from Aquatic Environment. MEMBRANES 2022; 12:membranes12040429. [PMID: 35448399 PMCID: PMC9032214 DOI: 10.3390/membranes12040429] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 03/29/2022] [Accepted: 04/08/2022] [Indexed: 12/27/2022]
Abstract
In recent years, the emergence of disparate micro-contaminants in aquatic environments such as water/wastewater sources has eventuated in serious concerns about humans’ health all over the world. Membrane bioreactor (MBR) is considered a noteworthy membrane-based technology, and has been recently of great interest for the removal micro-contaminants. The prominent objective of this review paper is to provide a state-of-the-art review on the potential utilization of MBRs in the field of wastewater treatment and micro-contaminant removal from aquatic/non-aquatic environments. Moreover, the operational advantages of MBRs compared to other traditional technologies in removing disparate sorts of micro-contaminants are discussed to study the ways to increase the sustainability of a clean water supplement. Additionally, common types of micro-contaminants in water/wastewater sources are introduced and their potential detriments on humans’ well-being are presented to inform expert readers about the necessity of micro-contaminant removal. Eventually, operational challenges towards the industrial application of MBRs are presented and the authors discuss feasible future perspectives and suitable solutions to overcome these challenges.
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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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Chen P, Ansari MJ, Bokov D, Suksatan W, Rahman ML, Sarjadi MS. A review on key aspects of wet granulation process for continuous pharmaceutical manufacturing of solid dosage oral formulations. ARAB J CHEM 2022. [DOI: 10.1016/j.arabjc.2021.103598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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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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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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12
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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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13
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A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation. SENSORS 2021; 21:s21082801. [PMID: 33921160 PMCID: PMC8071578 DOI: 10.3390/s21082801] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/09/2021] [Accepted: 04/12/2021] [Indexed: 11/17/2022]
Abstract
Real-time monitoring of multiphase fluid flows with distributed fibre optic sensing has the potential to play a major role in industrial flow measurement applications. One such application is the optimization of hydrocarbon production to maximize short-term income, and prolong the operational lifetime of production wells and the reservoir. While the measurement technology itself is well understood and developed, a key remaining challenge is the establishment of robust data analysis tools that are capable of providing real-time conversion of enormous data quantities into actionable process indicators. This paper provides a comprehensive technical review of the data analysis techniques for distributed fibre optic technologies, with a particular focus on characterizing fluid flow in pipes. The review encompasses classical methods, such as the speed of sound estimation and Joule-Thomson coefficient, as well as their data-driven machine learning counterparts, such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Ensemble Kalman Filter (EnKF) algorithms. The study aims to help end-users establish reliable, robust, and accurate solutions that can be deployed in a timely and effective way, and pave the wave for future developments in the field.
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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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15
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Babanezhad M, Rezakazemi M, Marjani A, Shirazian S. Predicting Air Superficial Velocity of Two-Phase Reactors Using ANFIS and CFD. ACS OMEGA 2021; 6:239-252. [PMID: 33458476 PMCID: PMC7807482 DOI: 10.1021/acsomega.0c04386] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 12/08/2020] [Indexed: 06/12/2023]
Abstract
In predicting the turbulence property of gas (bubble) flow in the domain of continuous fluid and liquid, the integration of machine learning and computational fluid dynamics (CFD) methods reduces the overall computational time. This combination enables us to see the effective input parameters in the engineering process and the impact of operating conditions on final outputs, such as gas hold-up, heat and mass transfer, and the flow regime (uniform bubble distribution or nonuniform bubble properties). This paper uses the combination of machine learning and single-size calculation of the Eulerian method to estimate the gas flow distribution in the continuous liquid fluid. To present the machine-learning method besides the Eulerian method, an adaptive neuro-fuzzy inference system (ANFIS) is used to train the CFD finding and then estimate the flow based on the machine-learning method. The gas velocity and turbulent eddy dissipation rate are trained throughout the bubble column reactor (BCR) for each CFD node, and the artificial BCR is predicted by the ANFIS method. This smart reactor can represent the artificial CFD of the BCR, resulting in the reduction of expensive numerical simulations. The results showed that the number of inputs could significantly change this method's accuracy, representing the intelligence of method in the learning data set. Additionally, the membership function specifications can impact the accuracy, particularly, when the process is trained with different inputs. The turbulent eddy dissipation rate can also be predicted by the ANFIS method with a similar model pattern for air superficial gas velocity.
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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
| | - 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
- Laboratory
of Computational Modeling of Drugs, South
Ural State University, 76 Lenin Prospekt, Chelyabinsk 454080, Russia
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