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Fiyadh SS, Alardhi SM, Al Omar M, Aljumaily MM, Al Saadi MA, Fayaed SS, Ahmed SN, Salman AD, Abdalsalm AH, Jabbar NM, El-Shafi A. A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique. Heliyon 2023; 9:e15455. [PMID: 37128319 PMCID: PMC10147989 DOI: 10.1016/j.heliyon.2023.e15455] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 04/04/2023] [Accepted: 04/10/2023] [Indexed: 05/03/2023] Open
Abstract
Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them.
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Affiliation(s)
| | - Saja Mohsen Alardhi
- Nanotechnology and Advanced Materials Research Center, University of Technology, Iraq
| | - Mohamed Al Omar
- Department of Civil Engineering, Al-Maarif University College, Ramadi, Iraq
| | | | | | - Sabah Saadi Fayaed
- Department of Civil Engineering, Al-Maarif University College, Ramadi, Iraq
- Ministry of Planning Dept. Social Services Projects Section, Baghdad, Iraq
| | | | - Ali Dawood Salman
- Sustainability Solutions Research Lab, University of Pannonia, Egyetem Str. 10, H-8200 Veszprem, Hungary
- Department of Chemical and Petroleum Refining Engineering, College of Oil and Gas Engineering, Basra University for Oil and Gas, Iraq
- Corresponding author. Sustainability Solutions Research Lab, University of Pannonia, Egyetem Str. 10, H-8200 Veszprem, Hungary.
| | - Alyaa H. Abdalsalm
- Nanotechnology and Advanced Materials Research Center, University of Technology, Iraq
| | - Noor Mohsen Jabbar
- Biochemical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
| | - Ahmed El-Shafi
- Department of Civil Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia
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Deep eutectic solvents-modified advanced functional materials for pollutant detection in food and the environment. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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Tolmachev D, Lukasheva N, Ramazanov R, Nazarychev V, Borzdun N, Volgin I, Andreeva M, Glova A, Melnikova S, Dobrovskiy A, Silber SA, Larin S, de Souza RM, Ribeiro MCC, Lyulin S, Karttunen M. Computer Simulations of Deep Eutectic Solvents: Challenges, Solutions, and Perspectives. Int J Mol Sci 2022; 23:645. [PMID: 35054840 PMCID: PMC8775846 DOI: 10.3390/ijms23020645] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 01/02/2022] [Accepted: 01/04/2022] [Indexed: 12/13/2022] Open
Abstract
Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications, such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can predict and reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.
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Affiliation(s)
- Dmitry Tolmachev
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Natalia Lukasheva
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Ruslan Ramazanov
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Victor Nazarychev
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Natalia Borzdun
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Igor Volgin
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Maria Andreeva
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Artyom Glova
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Sofia Melnikova
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Alexey Dobrovskiy
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Steven A. Silber
- Department of Physics and Astronomy, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada;
- The Centre of Advanced Materials and Biomaterials Research, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
| | - Sergey Larin
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Rafael Maglia de Souza
- Departamento de Química Fundamental, Instituto de Química, Universidade de São Paulo, Avenida Professor Lineu Prestes 748, São Paulo 05508-070, Brazil; (R.M.d.S.); (M.C.C.R.)
| | - Mauro Carlos Costa Ribeiro
- Departamento de Química Fundamental, Instituto de Química, Universidade de São Paulo, Avenida Professor Lineu Prestes 748, São Paulo 05508-070, Brazil; (R.M.d.S.); (M.C.C.R.)
| | - Sergey Lyulin
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Mikko Karttunen
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
- Department of Physics and Astronomy, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada;
- The Centre of Advanced Materials and Biomaterials Research, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
- Department of Chemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
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Bhagat SK, Paramasivan M, Al-Mukhtar M, Tiyasha T, Pyrgaki K, Tung TM, Yaseen ZM. Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:31670-31688. [PMID: 33611749 DOI: 10.1007/s11356-021-12836-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 02/02/2021] [Indexed: 06/12/2023]
Abstract
This study investigates the performance of support vector machine (SVM), multivariate adaptive regression spline (MARS), and random forest (RF) models for predicting the lead (Pb) adsorption by attapulgite clay. Models are constructed using batch stochastic data of heavy metal (HM) concentrations under different physicochemical conditions. Implementation of auto-hyper-parameter tuning using grid-search approach and comparative analysis is performed against the benchmark artificial intelligence (AI) models. Models are constructed based on Pb concentration (IC), the dosage of attapulgite clay (dose), contact time (CT), pH, and NaNO3 (SN). Principle component analysis (PCA) and correlation analysis (CA) methods are integrated to assess the importance of the applied predictors and their relationship with the target. Research findings approved the potential of the grid-RF model as a marginal superior predictive model against the grid-SVM in terms of MAE, i.e., 3.29 and 3.34, respectively; moreover, the md scored the same, i.e., 0.93, which reveals the potential predictability for both. Nonetheless, grid-MARS and standalone MARS models remained likewise in their predictability. IC parameter demonstrated the highest influential among all the predictors with the highest value of importance in the case of all three evaluators. The solution pH and dose stands together with marginal differences in case of PCA method; however, solution pH and CT appeared with similarity impact using the PCA method.
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Affiliation(s)
- Suraj Kumar Bhagat
- Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | | | | | - Tiyasha Tiyasha
- Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Konstantina Pyrgaki
- Department of Geology & Geoenvironment, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15784, Athens, Greece
| | - Tran Minh Tung
- Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Zaher Mundher Yaseen
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam.
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Deep eutectic solvents (DESs): A short overview of the thermophysical properties and current use as base fluid for heat transfer nanofluids. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2020.114752] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Bhagat SK, Tiyasha T, Awadh SM, Tung TM, Jawad AH, Yaseen ZM. Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 268:115663. [PMID: 33120144 DOI: 10.1016/j.envpol.2020.115663] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 09/05/2020] [Accepted: 09/14/2020] [Indexed: 05/25/2023]
Abstract
Hybrid artificial intelligence (AI) models are developed for sediment lead (Pb) prediction in two Bays (i.e., Bramble (BB) and Deception (DB)) stations, Australia. A feature selection (FS) algorithm called extreme gradient boosting (XGBoost) is proposed to abstract the correlated input parameters for the Pb prediction and validated against principal component of analysis (PCA), recursive feature elimination (RFE), and the genetic algorithm (GA). XGBoost model is applied using a grid search strategy (Grid-XGBoost) for predicting Pb and validated against the commonly used AI models, artificial neural network (ANN) and support vector machine (SVM). The input parameter selection approaches redimensioned the 21 parameters into 9-5 parameters without losing their learned information over the models' training phase. At the BB station, the mean absolute percentage error (MAPE) values (0.06, 0.32, 0.34, and 0.33) were achieved for the XGBoost-SVM, XGBoost-ANN, XGBoost-Grid-XGBoost, and Grid-XGBoost models, respectively. At the DB station, the lowest MAPE values, 0.25 and 0.24, were attained for the XGBoost-Grid-XGBoost and Grid-XGBoost models, respectively. Overall, the proposed hybrid AI models provided a reliable and robust computer aid technology for sediment Pb prediction that contribute to the best knowledge of environmental pollution monitoring and assessment.
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Affiliation(s)
- Suraj Kumar Bhagat
- Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
| | - Tiyasha Tiyasha
- Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
| | | | - Tran Minh Tung
- Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
| | - Ali H Jawad
- Faculty of Applied Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia
| | - Zaher Mundher Yaseen
- Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.
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Hansen BB, Spittle S, Chen B, Poe D, Zhang Y, Klein JM, Horton A, Adhikari L, Zelovich T, Doherty BW, Gurkan B, Maginn EJ, Ragauskas A, Dadmun M, Zawodzinski TA, Baker GA, Tuckerman ME, Savinell RF, Sangoro JR. Deep Eutectic Solvents: A Review of Fundamentals and Applications. Chem Rev 2020; 121:1232-1285. [PMID: 33315380 DOI: 10.1021/acs.chemrev.0c00385] [Citation(s) in RCA: 789] [Impact Index Per Article: 197.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Deep eutectic solvents (DESs) are an emerging class of mixtures characterized by significant depressions in melting points compared to those of the neat constituent components. These materials are promising for applications as inexpensive "designer" solvents exhibiting a host of tunable physicochemical properties. A detailed review of the current literature reveals the lack of predictive understanding of the microscopic mechanisms that govern the structure-property relationships in this class of solvents. Complex hydrogen bonding is postulated as the root cause of their melting point depressions and physicochemical properties; to understand these hydrogen bonded networks, it is imperative to study these systems as dynamic entities using both simulations and experiments. This review emphasizes recent research efforts in order to elucidate the next steps needed to develop a fundamental framework needed for a deeper understanding of DESs. It covers recent developments in DES research, frames outstanding scientific questions, and identifies promising research thrusts aligned with the advancement of the field toward predictive models and fundamental understanding of these solvents.
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Affiliation(s)
- Benworth B Hansen
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee37996-2200, United States
| | - Stephanie Spittle
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee37996-2200, United States
| | - Brian Chen
- Department of Chemical and Biomolecular Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Derrick Poe
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Yong Zhang
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Jeffrey M Klein
- Department of Chemical and Biomolecular Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Alexandre Horton
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee37996-2200, United States
| | - Laxmi Adhikari
- Department of Chemistry, University of Missouri-Columbia, Columbia, Missouri 65211, United States
| | - Tamar Zelovich
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Brian W Doherty
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Burcu Gurkan
- Department of Chemical and Biomolecular Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Edward J Maginn
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Arthur Ragauskas
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee37996-2200, United States
| | - Mark Dadmun
- Department of Chemistry, University of Tennessee, Knoxville, Tennessee 37916, United States
| | - Thomas A Zawodzinski
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee37996-2200, United States
| | - Gary A Baker
- Department of Chemistry, University of Missouri-Columbia, Columbia, Missouri 65211, United States
| | - Mark E Tuckerman
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Robert F Savinell
- Department of Chemical and Biomolecular Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Joshua R Sangoro
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee37996-2200, United States
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Alkhatib II, Bahamon D, Llovell F, Abu-Zahra MR, Vega LF. Perspectives and guidelines on thermodynamic modelling of deep eutectic solvents. J Mol Liq 2020. [DOI: 10.1016/j.molliq.2019.112183] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Fiyadh SS, AlOmar MK, Binti Jaafar WZ, AlSaadi MA, Fayaed SS, Binti Koting S, Lai SH, Chow MF, Ahmed AN, El-Shafie A. Artificial Neural Network Approach for Modelling of Mercury Ions Removal from Water Using Functionalized CNTs with Deep Eutectic Solvent. Int J Mol Sci 2019; 20:ijms20174206. [PMID: 31466219 PMCID: PMC6747871 DOI: 10.3390/ijms20174206] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 07/19/2019] [Accepted: 07/19/2019] [Indexed: 12/07/2022] Open
Abstract
Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R2) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R2 and MSE were 9.79%, 0.9701 and 1.15 × 10-3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 × 10-3 for the LR model; and 16.4%, 0.9313 and 2.27 × 10-3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models.
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Affiliation(s)
- Seef Saadi Fiyadh
- Nanotechnology & Catalysis Research Centre (NANOCAT), IPS Building, University of Malaya, Kuala Lumpur 50603, Malaysia
- Correspondence: saadisaif3@gmail (S.S.F.); (M.F.C.); Tel.: +60-1430-46953 (S.S.F.)
| | | | | | - Mohammed Abdulhakim AlSaadi
- Nanotechnology & Catalysis Research Centre (NANOCAT), IPS Building, University of Malaya, Kuala Lumpur 50603, Malaysia
- National Chair of Materials Science and Metallurgy, University of Nizwz, Sultanate of Oman, Nizwa 616, Oman
| | - Sabah Saadi Fayaed
- Department of Civil Engineering, Al-Maaref University College, Ramadi 31001, Iraq
| | - Suhana Binti Koting
- Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Sai Hin Lai
- Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Ming Fai Chow
- Institute of Energy Infrastructure (IEI), Department of Civil Engineering, Universiti Tenaga Nasional, 43000 Kajang, Selangor, Malaysia
- Correspondence: saadisaif3@gmail (S.S.F.); (M.F.C.); Tel.: +60-1430-46953 (S.S.F.)
| | - Ali Najah Ahmed
- Institute of Energy Infrastructure (IEI), Department of Civil Engineering, Universiti Tenaga Nasional, 43000 Kajang, Selangor, Malaysia
| | - Ahmed El-Shafie
- Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
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