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Behera P, Sahu HB. Effective Removal of Selenium from Aqueous Solution Using Iron-Modified Dolochar: A Comprehensive Study and Machine Learning Predictive Analysis. ENVIRONMENTAL RESEARCH 2024:120003. [PMID: 39293754 DOI: 10.1016/j.envres.2024.120003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 09/11/2024] [Accepted: 09/13/2024] [Indexed: 09/20/2024]
Abstract
Selenium (Se) is an essential micronutrient for human beings, but excess concentration can lead to many health issues and degrade the ecosystem. This study focuses on the removal of selenium from an aqueous solution using iron-doped dolochar. SEM, EDX, BET, XRD, FTIR, and Pzpc were conducted to determine the surface characteristics of iron-doped dolochar (FeD). The characterization of the adsorbent gave an insight into surface morphology, surface area (100 m2/g), average pore diameter (3.9 nm), and surface composition, which contributed to the Se adsorption. The pHzpc of the iron-doped adsorbent surface was found to be 7.02, which provided a broad range for effective Se adsorption. To detect the optimum parameters, the parametric influence on removal efficiency was conducted by varying pH, dosages, contact time, and initial concentration. The experiment achieved maximum selenium removal, ∼98 %, at low concentration, 10 g/L dosage, and low pH (2) within 90 min at room temperature. It fits the Langmuir better than the Freundlich isotherm (R2 = 0.99), indicating monolayer adsorption. It fitted well with pseudo-second-order kinetics. The experiment is a spontaneous, endothermic (ΔH0=9.22 kJ/mol) and high randomness (ΔS0 =45.37 kJ/mol) suggested by thermodynamic study. The adsorption was influenced by competing ions as follows: phosphate > sulfate > nitrate > manganese > aluminum> zinc > iron. A regression learner tool was used to compare different models using the experimental data that showed the best fit with the Gaussian Process Regression with RMSE =0.246, MSE = 0.061, and R2 = 0.99. Thus, it can be concluded that FeD is preferred as a better adsorbent for selenium removal from aqueous solutions and could produce 35.5% ROI, 21.5% IRR, and 24.59% BEP on FeD production.
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Affiliation(s)
- Pallavi Behera
- Department of Mining Engineering, National Institute of Technology, Rourkela, India.
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2
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Saeedimasine M, Rahmani R, Lyubartsev AP. Biomolecular Adsorption on Nanomaterials: Combining Molecular Simulations with Machine Learning. J Chem Inf Model 2024; 64:3799-3811. [PMID: 38623916 PMCID: PMC11094735 DOI: 10.1021/acs.jcim.3c01606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 04/03/2024] [Accepted: 04/03/2024] [Indexed: 04/17/2024]
Abstract
Adsorption free energies of 32 small biomolecules (amino acids side chains, fragments of lipids, and sugar molecules) on 33 different nanomaterials, computed by the molecular dynamics - metadynamics methodology, have been analyzed using statistical machine learning approaches. Multiple unsupervised learning algorithms (principal component analysis, agglomerative clustering, and K-means) as well as supervised linear and nonlinear regression algorithms (linear regression, AdaBoost ensemble learning, artificial neural network) have been applied. As a result, a small set of biomolecules has been identified, knowledge of adsorption free energies of which to a specific nanomaterial can be used to predict, within the developed machine learning model, adsorption free energies of other biomolecules. Furthermore, the methodology of grouping of nanomaterials according to their interactions with biomolecules has been presented.
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Affiliation(s)
- Marzieh Saeedimasine
- Department of Materials and Environmental
Chemistry, Stockholm University, Stockholm SE-106 91, Sweden
| | - Roja Rahmani
- Department of Materials and Environmental
Chemistry, Stockholm University, Stockholm SE-106 91, Sweden
| | - Alexander P. Lyubartsev
- Department of Materials and Environmental
Chemistry, Stockholm University, Stockholm SE-106 91, Sweden
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3
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Maurya BM, Yadav N, T A, J S, A S, V P, Iyer M, Yadav MK, Vellingiri B. Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges. CHEMOSPHERE 2024; 353:141474. [PMID: 38382714 DOI: 10.1016/j.chemosphere.2024.141474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/17/2024] [Accepted: 02/14/2024] [Indexed: 02/23/2024]
Abstract
Heavy metals (HMs) enter waterbodies through various means, which, when exceeding a threshold limit, cause toxic effects both on the environment and in humans upon entering their systems. Recent times have seen an increase in such HM influx incident rates. This requires an instant response in this regard to review the challenges in the available classical methods for HM detection and removal. As well as provide an opportunity to explore the applications of artificial intelligence (AI) and machine learning (ML) for the identification and further redemption of water and wastewater from the HMs. This review of research focuses on such applications in conjunction with the available in-silico models producing worldwide data for HM levels. Furthermore, the effect of HMs on various disease progressions has been provided, along with a brief account of prediction models analysing the health impact of HM intoxication. Also discussing the ethical and other challenges associated with the use of AI and ML in this field is the futuristic approach intended to follow, opening a wide scope of possibilities for improvement in wastewater treatment methodologies.
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Affiliation(s)
- Brij Mohan Maurya
- Human Cytogenetics and Stem Cell Laboratory, Department of Zoology, School of Basic Sciences, Central University of Punjab, Bathinda, 151401, Punjab, India
| | - Nidhi Yadav
- Human Cytogenetics and Stem Cell Laboratory, Department of Zoology, School of Basic Sciences, Central University of Punjab, Bathinda, 151401, Punjab, India
| | - Amudha T
- Department of Computer Applications, Bharathiar University, Coimbatore, India
| | - Satheeshkumar J
- Department of Computer Applications, Bharathiar University, Coimbatore, India
| | - Sangeetha A
- Department of Computer Applications, Bharathiar University, Coimbatore, India
| | - Parthasarathy V
- Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Pollachi Main Road, Eachanari Post, Coimbatore, 641021, Tamil Nadu, India
| | - Mahalaxmi Iyer
- Centre for Neuroscience, Department of Biotechnology, Karpagam Academy of Higher Education, Coimbatore, 641021, Tamil Nadu, India; Department of Microbiology, Central University of Punjab, Bathinda, 151401, Punjab, India
| | - Mukesh Kumar Yadav
- Department of Microbiology, Central University of Punjab, Bathinda, 151401, Punjab, India
| | - Balachandar Vellingiri
- Human Cytogenetics and Stem Cell Laboratory, Department of Zoology, School of Basic Sciences, Central University of Punjab, Bathinda, 151401, Punjab, India.
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Yaseen ZM, Melini Wan Mohtar WH, Homod RZ, Alawi OA, Abba SI, Oudah AY, Togun H, Goliatt L, Ul Hassan Kazmi SS, Tao H. Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm. CHEMOSPHERE 2024; 352:141329. [PMID: 38296204 DOI: 10.1016/j.chemosphere.2024.141329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/09/2024] [Accepted: 01/28/2024] [Indexed: 02/09/2024]
Abstract
This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies.
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Affiliation(s)
- Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia; Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia.
| | - Wan Hanna Melini Wan Mohtar
- Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM, Bangi, Selangor, Malaysia; Environmental Management Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
| | - Raad Z Homod
- Department of Oil and Gas Engineering, Basrah University for Oil and Gas, Basra, Iraq.
| | - Omer A Alawi
- Department of Thermofluids, School of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor Bahru, Malaysia.
| | - Sani I Abba
- Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia.
| | - Atheer Y Oudah
- Department of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Nasiriyah, 64001, Iraq; Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah, 64001, Iraq.
| | - Hussein Togun
- Department of Mechanical Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq.
| | - Leonardo Goliatt
- Computational and Applied Mechanics Department, Federal University of Juiz de Fora, 36036-900, Brazil.
| | - Syed Shabi Ul Hassan Kazmi
- Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, and Guangdong Provincial Key Laboratory of Marine Biotechnology, Shantou University, Shantou, 515063, China.
| | - Hai Tao
- School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China; Institute of Big Data Application and Artificial Intelligence, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China; Faculty of Data Science and Information Technology, INTI International University, 71800, Malaysia.
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Mathaba M, Banza J. A comprehensive review on artificial intelligence in water treatment for optimization. Clean water now and the future. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART A, TOXIC/HAZARDOUS SUBSTANCES & ENVIRONMENTAL ENGINEERING 2024; 58:1047-1060. [PMID: 38293764 DOI: 10.1080/10934529.2024.2309102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 01/13/2024] [Indexed: 02/01/2024]
Abstract
Given the severe effects that toxic compounds present in wastewater streams have on humans, it is imperative that water and wastewater streams pollution be addressed globally. This review comprehensively examines various water and wastewater treatment methods and water quality management methods based on artificial intelligence (AI). Machine learning (ML) and AI have become a powerful tool for addressing problems in the real world and has gained a lot of interest since it can be used for a wide range of activities. The foundation of ML techniques involves training of a network with collected data, followed by application of learned network to the process simulation and prediction. The creation of ML models for process simulations requires measured data. In order to forecast and simulate chemical and physical processes such chemical reactions, heat transfer, mass transfer, energy, pharmaceutics and separation, a variety of machine-learning algorithms have recently been developed. These models have shown to be more adept at simulating and modeling processes than traditional models. Although AI offers many advantages, a number of disadvantages have kept these methods from being extensively applied in actual water treatment systems. Lack of evidence of application in actual water treatment scenarios, poor repeatability and data availability and selection are a few of the main problems that need to be resolved.
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Affiliation(s)
- Machodi Mathaba
- Department of Chemical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa
| | - JeanClaude Banza
- Department of Chemical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa
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Singa PK, Isa MH, Sivaprakash B, Ho YC, Lim JW, Rajamohan N. PAHs remediation from hazardous waste landfill leachate using fenton, photo - fenton and electro - fenton oxidation processes - performance evaluation under optimized conditions using RSM and ANN. ENVIRONMENTAL RESEARCH 2023; 231:116191. [PMID: 37211185 DOI: 10.1016/j.envres.2023.116191] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 05/23/2023]
Abstract
Polycyclic aromatic hydrocharbons (PAHs) are a class of highly toxic pollutants that are highly detrimental to the ecosystem. Landfill leechate emanated from municipal solid waste are reported to constitute significant PAHs. In the present investigation, three Fenton proceses, namely conventional Fenton, photo-fenton and electro-fenton methods have been employed to treat landfill leehcate for removing PAHs from a waste dumpig yard. Response surface methodology (RSM) and artificial neural network (ANN) methodologies were adopted to optimize and validate the conditions for optimum oxidative removal of COD and PAHs. The statistical analysis results showed that all independent variables chosen in the study are reported to have significant influence of the removal effects with P-values <0.05. Sensitivity analysis by the developed ANN model showed that the pH had the highest significance of 1.89 in PAH removal when compared to the other parameters. However for COD removal, H2O2 had the highest relative importance of 1.15, followed by Fe2+ and pH. Under optimal treatment conditions, the photo-fenton and electro-fenton processes showed better removal of COD and PAH compared to the Fenton process. The photo-fenton and electro-fenton treatment processes removed 85.32% and 74.64% of COD and 93.25% and 81.65% of PAHs, respectively. Also the investigations revelaed the presence of 16 distinct PAH compunds and the removal percentage of each of these PAHs are also reported. The PAH treatment research studies are generally limited to the assay of removal of PAH and COD levels. In the present investigation, in addition to the treatment of landfill leachate, particle size distribution analysis and elemental characterization of the resultant iron sludge by FESEM and EDX are reported. It was revealed that elemental oxygen is present in highest percentage, followed by iron, sulphur, sodium, chlorine, carbon and potassium. However, iron percentage can be reduced by treating the Fenton-treated sample with NaOH.
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Affiliation(s)
- Pradeep Kumar Singa
- Department of Civil Engineering, Guru-Nanak Dev Engineering College, Bidar, 585403, Karnataka, India.
| | - Mohamed Hasnain Isa
- Department of Civil Engineering, Universiti Teknologi Brunei, Jalan Tungku Link, Gadong, BE1410, Brunei Darussalam
| | - Baskaran Sivaprakash
- Department of Chemical Engineering, Annamalai University, Annamalai Nagar PC, 608002, India
| | - Yeek-Chia Ho
- Civil and Environmental Engineering Department, Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia.
| | - Jun-Wei Lim
- Fundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
| | - Natarajan Rajamohan
- Chemical Engineering Section, Faculty of Engineering, Sohar University, Sohar, PC-311, Oman.
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7
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Gasparetto H, Carolina Ferreira Piazzi Fuhr A, Paula Gonçalves Salau N. Forecasting soybean oil extraction using cyclopentyl methyl ether through soft computing models with a density functional theory study. J IND ENG CHEM 2023. [DOI: 10.1016/j.jiec.2023.03.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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8
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Zhang W, Huang W, Tan J, Huang D, Ma J, Wu B. Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives. CHEMOSPHERE 2023; 311:137044. [PMID: 36330979 DOI: 10.1016/j.chemosphere.2022.137044] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/22/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.
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Affiliation(s)
- Wentao Zhang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Wenguang Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China.
| | - Jie Tan
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Dawei Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Jun Ma
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Bingdang Wu
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, People's Republic of China; Key Laboratory of Suzhou Sponge City Technology, Suzhou, 215002, People's Republic of China.
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Maamoun I, Rushdi MA, Falyouna O, Eljamal R, Eljamal O. Insights into machine-learning modeling for Cr(VI) removal from contaminated water using nano-nickel hydroxide. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2022.122863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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10
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Al-Jadir T, Alardhi SM, Al-Sheikh F, Jaber AA, Kadhim WA, Rahim MHA. Modeling of lead (II) ion adsorption on multiwall carbon nanotubes using artificial neural network and Monte Carlo technique. CHEM ENG COMMUN 2022. [DOI: 10.1080/00986445.2022.2129622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Thaer Al-Jadir
- Environment Research Center, University of Technology- Iraq, Baghdad, Iraq
| | - Saja Mohsen Alardhi
- Nanotechnology and Advanced Materials Research Center, University of Technology- Iraq, Baghdad, Iraq
| | - Farooq Al-Sheikh
- Department of Chemical Engineering, University of Technology- Iraq, Baghdad, Iraq
| | - Alaa Abdulhady Jaber
- Mechanical Engineering Department, University of Technology- Iraq, Baghdad, Iraq
| | - Wafaa A Kadhim
- Nanotechnology and Advanced Materials Research Center, University of Technology- Iraq, Baghdad, Iraq
| | - Mohd Hasbi Ab. Rahim
- Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Pahang, Malaysia
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Almalawi A, Khan AI, Alqurashi F, Abushark YB, Alam MM, Qaiyum S. Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar. CHEMOSPHERE 2022; 303:135065. [PMID: 35618070 DOI: 10.1016/j.chemosphere.2022.135065] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/11/2022] [Accepted: 05/20/2022] [Indexed: 06/15/2023]
Abstract
Environmental distresses linked to heavy metal (HM) impurity in the water received significant attention among research communities. Recently, advancements in industrial sectors like paper industries, mining, non-ferrous metallurgy, electroplating, mineral paint production, etc. have resulted in massive heavy metals in wastewater. In contrast to organic pollutants, HMs are not recyclable and can be simply engrossed by living organisms. Recently, different solutions have been employed for removing HMs from water and wastewater, like membrane filtration, chemical precipitation, adsorption, ion-exchange, flotation, flocculation, etc. Sorption can be considered one of the efficient solutions for eradicating HMs from waste water. With this motivation, this article concentrates on the design of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction (RODL-HMSEP) model onto Biochar. The proposed RODL-HMSEP technique intends to determine the sorption performance of HMs of various biochar features. Initially, the density based clustering (DBSCAN) technique is applied to simulating the features of metal adsorption data and splitting them into clusters of identical features. Besides, deep belief network (DBN) model was employed for prediction and the efficiency of the DBN model is optimally adjusted with utilize of RO technique. The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods.
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Affiliation(s)
- Abdulmohsen Almalawi
- Computer Science Department, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Asif Irshad Khan
- Computer Science Department, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Fahad Alqurashi
- Computer Science Department, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Yoosef B Abushark
- Computer Science Department, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Md Mottahir Alam
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Sana Qaiyum
- Center for Research in Data Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 21 32610, 22 Perak, Malaysia.
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Bhagat SK, Tiyasha T, Kumar A, Malik T, Jawad AH, Khedher KM, Deo RC, Yaseen ZM. Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 309:114711. [PMID: 35182982 DOI: 10.1016/j.jenvman.2022.114711] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 01/17/2022] [Accepted: 02/09/2022] [Indexed: 06/14/2023]
Abstract
Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (Tmin, Tmax and TavgoC), rainfall (Rn mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the Tavg and Rn inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb prediction.
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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.
| | - Adarsh Kumar
- Institute of Natural Sciences and Mathematics, Ural Federal University, Ekaterinburg, 620002, Russia.
| | - Tabarak Malik
- Department of Biochemistry, College of Medicine & Health Sciences, School of Medicine, University of Gondar, Ethiopia.
| | - Ali H Jawad
- Faculty of Applied Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia.
| | - Khaled Mohamed Khedher
- Department of Civil Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia; Department of Civil Engineering, High Institute of Technological Studies, Mrezgua University Campus, Nabeul, 8000, Tunisia
| | - Ravinesh C Deo
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia
| | - Zaher Mundher Yaseen
- Adjunct Research Fellow, USQ's Advanced Data Analytics Research Group, School of Mathematics Physics and Computing, University of Southern Queensland, QLD 4350, Australia; Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, 454080 Chelyabinsk, Russia; College of Creative Design, Asia University, Taichung City, Taiwan; New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, Iraq; Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Kompleks Al-Khawarizmi, Universiti Teknologi MARA, Shah Alam, 40450 Selangor, Malaysia.
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Kooh MRR, Thotagamuge R, Chou Chau YF, Mahadi AH, Lim CM. Machine learning approaches to predict adsorption capacity of Azolla pinnata in the removal of methylene blue. J Taiwan Inst Chem Eng 2022. [DOI: 10.1016/j.jtice.2021.11.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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14
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Application of multilayer perceptron network and random forest models for modelling the adsorption of chlorobenzene on a modified bentonite by intercalation with hexadecyltrimethyl ammonium (HDTMA). REACTION KINETICS MECHANISMS AND CATALYSIS 2022. [DOI: 10.1007/s11144-021-02121-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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15
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Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12030999] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Arsenic is a deleterious heavy metal that is usually removed from polluted water based on adsorption processes. The latest mode of modeling such a process is to implement artificial intelligence (AI). In the current work, a new artificial neural network (ANN) model was developed to predict the adsorption efficiency of arsenate (As(III)) from contaminated water by analyzing different architectures of an adaptive network-based fuzzy inference system (ANFIS). The database for the current study consisted of the experimental data of the adsorption of As(III) by different adsorbents/biosorbents. The data were randomly divided into two sets: 70% for the training phase and 30% for the testing phase. Four statistical evaluation metrics, namely, mean square error (MSE), root-mean-square error (RMSE), Pearson’s correlation coefficient (R%), and the determination coefficient (R2) were used for the analysis. The best performing ANFIS model was characterized with the average values of 97.72%, 0.9333, 0.137, and 0.274 of R%, R2, MSE, and RMSE, respectively. In addition, a parametric investigation revealed that the most dominating parameters on the adsorption process efficiency were in the following order: pH, As initial concentration, contact time, adsorbent dosage, inoculum size, and temperature. The results of the current study would be useful in the adsorption process scale-up and optimization.
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Yaseen ZM. An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions. CHEMOSPHERE 2021; 277:130126. [PMID: 33774235 DOI: 10.1016/j.chemosphere.2021.130126] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 01/23/2021] [Accepted: 02/23/2021] [Indexed: 06/12/2023]
Abstract
The development of computer aid models for heavy metals (HMs) simulation has been remarkably advanced over the past two decades. Several machine learning (ML) models have been developed for modeling HMs over the past two decades with outstanding progress. Although there have been a noticeable number of diverse ML models investigations, it is essential to have an informative vision on the progression of those computer aid models. In the current short review covering the simulation of heavy metals in contaminated soil, water bodies and removal from aqueous solution, numerous aspects on the methodological and conceptual HMs modeling are reviewed and discussed in detail. For instance, the limitation of the classical analytical methods, types of heavy metal dataset, necessity for new versions of ML models exploration, HM input parameters selection, ML models internal parameters tuning, performance metrics selection and the types of the modelled HM. The current review provides few outlooks in understanding the underlying od the ML models application for HM simulation. Tackling these modeling aspects is significantly essential for ML developers and environmental scientists to obtain creditability and scientific consistency in the domain of environmental science. Based on the discussed modeling aspects, it was concluded several future research directions, which will promote environmental scientists for better understanding of the underlying HMs simulation.
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Affiliation(s)
- Zaher Mundher Yaseen
- New era and development in civil engineering research group, Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, Iraq.
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