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Pande CB, Kushwaha NL, Alawi OA, Sammen SS, Sidek LM, Yaseen ZM, Pal SC, Katipoğlu OM. Daily Scale Air Quality Index Forecasting using Bidirectional Recurrent Neural Networks: Case Study of Delhi, India. Environ Pollut 2024:124040. [PMID: 38685551 DOI: 10.1016/j.envpol.2024.124040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/01/2024] [Accepted: 04/22/2024] [Indexed: 05/02/2024]
Abstract
This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum determination coefficient (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city.
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Affiliation(s)
- Chaitanya Baliram Pande
- Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang 43000, Malaysia; New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq.
| | - Nand Lal Kushwaha
- Division of Agricultural Engineering, ICAR-Indian Agriculture Research Institute,110012, New Delhi, India
| | - Omer A Alawi
- Department of Thermofluids, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor Bahru, Malaysia
| | - Saad Sh Sammen
- Department of Civil Engineering, College of Engineering, Diyala University, Diyala Governorate, Iraq
| | - Lariyah Mohd Sidek
- Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang 43000, Malaysia
| | - Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
| | - Subodh Chandra Pal
- Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal, 713104, India
| | - Okan Mert Katipoğlu
- Faculty of Engineering and Architecture, Department of Civil Engineering, Erzincan Binali Yıldırım University, 24100 Erzincan, Turkey
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Mallah AR, Aljuraid N, Alawi OA, Yaseen ZM, Singh K, Ataki A. A hybrid numerical/machine learning model development to improve the bimetal performance in the electric circuit breakers. Sci Rep 2022; 12:18087. [PMID: 36302924 PMCID: PMC9613663 DOI: 10.1038/s41598-022-22763-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 10/19/2022] [Indexed: 11/09/2022] Open
Abstract
Bimetals are widely used as a thermal tripping mechanism inside the miniature circuit breakers (MCBs) products when an overload current passes through the circuit for a certain period. Experimental, numerical, and, recently artificial intelligence methods are widely used in designing electric components. However, developing the bimetal for MCB products somewhat differs from developing other conductor items since they are strongly related to the electrical, mechanical, and thermal performance of the MCB. The conventional experimental and numerical approaches are time-consuming processes that cannot be easily utilized in optimizing the product's performance within the development lead time. In this study, a simple, fast, robust, and accurate novel methodology has been introduced to predict the temperature rise of the bimetal and other related performance characteristics. The numerical model has been built on the time-based finite difference method to frame the theoretical thermal model of the bimetal. Then, the numerical model has been consolidated by the machine learning (ML) model to take advantage of the experiments to provide an accurate, fast and reliable model finally. The novel model agrees well with the experimental tests, where the maximum error does not exceed 8%. The model has been used to redesign the bimetal of a 32 A MCB product and significantly reduce the maximum temperature by 24 °C. The novel model is promising since it considerably reduces the required design time, provides accurate predictions, and helps to optimize the performance of the circuit breaker products.
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Affiliation(s)
- Abdul Rahman Mallah
- Department of Engineering, Reykjavik University, Menntavegur 1, Reykjavík, 102, Iceland
| | - Nawaf Aljuraid
- Alfanar Electrical Systems, Riyadh, 11383, Kingdom of Saudi Arabia
| | - Omer A Alawi
- Department of Thermofluids, School of Mechanical Engineering, Universiti Teknologi Malaysia (UTM), Skudai, 81310, Johor Bahru, Malaysia
| | - Zaher Mundher Yaseen
- New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, Iraq.
| | - Kamaljeet Singh
- Department of Engineering, Reykjavik University, Menntavegur 1, Reykjavík, 102, Iceland
| | - Adel Ataki
- Department of Mechanical and Process Engineering, TU Kaiserslautern,, Kaiserslautern, Germany
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Tao H, Alawi OA, Hussein OA, Ahmed W, Abdelrazek AH, Homod RZ, Eltaweel M, Falah MW, Al-Ansari N, Yaseen ZM. Thermohydraulic analysis of covalent and noncovalent functionalized graphene nanoplatelets in circular tube fitted with turbulators. Sci Rep 2022; 12:17710. [PMID: 36271129 PMCID: PMC9586947 DOI: 10.1038/s41598-022-22315-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 10/12/2022] [Indexed: 11/30/2022] Open
Abstract
Covalent and non-covalent nanofluids were tested inside a circular tube fitted with twisted tape inserts with 45° and 90° helix angles. Reynolds number was 7000 ≤ Re ≤ 17,000, and thermophysical properties were assessed at 308 K. The physical model was solved numerically via a two-equation eddy-viscosity model (SST k-omega turbulence). GNPs-SDBS@DW and GNPs-COOH@DW nanofluids with concentrations (0.025 wt.%, 0.05 wt.% and 0.1 wt.%) were considered in this study. The twisted pipes' walls were heated under a constant temperature of 330 K. The current study considered six parameters: outlet temperature, heat transfer coefficient, average Nusselt number, friction factor, pressure loss, and performance evaluation criterion. In both cases (45° and 90° helix angles), GNPs-SDBS@DW nanofluids presented higher thermohydraulic performance than GNPs-COOH@DW and increased by increasing the mass fractions such as 1.17 for 0.025 wt.%, 1.19 for 0.05 wt.% and 1.26 for 0.1 wt.%. Meanwhile, in both cases (45° and 90° helix angles), the value of thermohydraulic performance using GNPs-COOH@DW was 1.02 for 0.025 wt.%, 1.05 for 0.05 wt.% and 1.02 for 0.1 wt.%.
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Affiliation(s)
- Hai Tao
- College of Artificial Intelligence, Nanchang Institute of Science and Technology, Nanchang, China.,School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou, 558000, China.,Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia
| | - Omer A Alawi
- Department of Thermofluids, School of Mechanical Engineering, Universiti Teknologi Malaysia (UTM), Skudai, 81310, Johor Bahru, Malaysia
| | - Omar A Hussein
- Petroleum system control engineering department, College of Petroleum Processes Engineering, Tikrit University, Tikrit, Iraq
| | - Waqar Ahmed
- Takasago i-Kohza, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
| | - Ali H Abdelrazek
- Takasago i-Kohza, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
| | - Raad Z Homod
- Department of Oil and Gas Engineering, Basrah University for Oil and Gas, Basrah, Iraq
| | - Mahmoud Eltaweel
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, UK
| | - Mayadah W Falah
- Building and construction techniques engineering department, AL-Mustaqbal University College, Hillah, 51001, Iraq
| | - Nadhir Al-Ansari
- Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187, Lulea, Sweden
| | - Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.
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Tao H, Salih S, Oudah AY, Abba SI, Ameen AMS, Awadh SM, Alawi OA, Mostafa RR, Surendran UP, Yaseen ZM. Development of new computational machine learning models for longitudinal dispersion coefficient determination: case study of natural streams, United States. Environ Sci Pollut Res Int 2022; 29:35841-35861. [PMID: 35061183 DOI: 10.1007/s11356-022-18554-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2023]
Abstract
Natural streams longitudinal dispersion coefficient (Kx) is an essential indicator for pollutants transport and its determination is very important. Kx is influenced by several parameters, including river hydraulic geometry, sediment properties, and other morphological characteristics, and thus its calculation is a highly complex engineering problem. In this research, three relatively explored machine learning (ML) models, including Random Forest (RF), Gradient Boosting Decision Tree (GTB), and XGboost-Grid, were proposed for the Kx determination. The modeling scheme on building the prediction matrix was adopted from the well-established literature. Several input combinations were tested for better predictability performance for the Kx. The modeling performance was tested based on the data division for the training and testing (70-30% and 80-20%). Based on the attained modeling results, XGboost-Grid reported the best prediction results over the training and testing phase compared to RF and GTB models. The development of the newly established machine learning model revealed an excellent computed-aided technology for the Kx simulation.
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Affiliation(s)
- Hai Tao
- School of Electronics and Information Engineering, Ankang University, Ankang, China
- School of Computer Sciences, Baoji University of Arts and Sciences, Shaanxi, China
- Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
| | - Sinan Salih
- Computer Science Department, Dijlah University College, Al-Dora, Baghdad, Iraq
- Artificial Intelligence Research Unit (AIRU), Dijlah University College, Al-Dora, Baghdad, Iraq
| | - Atheer Y Oudah
- Department of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Thi-Qar, Iraq
- Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, Iraq
| | - S I Abba
- Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia
- Faculty of Engineering, Department of Civil Engineering, Baze University, Abuja, Nigeria
| | | | | | - Omer A Alawi
- Department of Thermofluids, School of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM, Skudai, Johor Bahru, Malaysia
| | - Reham R Mostafa
- Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, 35516, Egypt
| | - Udayar Pillai Surendran
- Land and Water Management Research Group, Centre for Water Resources Development and Management (CWRDM), Kozhikode, Kerala, India
| | - Zaher Mundher Yaseen
- Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, 454080, Chelyabinsk, Russia.
- New era and development in civil engineering research group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq.
- College of Creative Design, Asia University, Taichung City, Taiwan.
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Alawi OA, Kamar HM, Hussein OA, Mallah A, Mohammed HA, Khiadani M, Roomi AB, Kazi S, Yaseen ZM. Effects of binary hybrid nanofluid on heat transfer and fluid flow in a triangular-corrugated channel: An experimental and numerical study. POWDER TECHNOL 2022. [DOI: 10.1016/j.powtec.2021.09.046] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Abdelrazek AH, Alawi OA, Kazi S, Yusoff N. Thermal performance evaluation for alumina coated MWCNTs composite nanofluid in annular passage of various eccentricities. POWDER TECHNOL 2021. [DOI: 10.1016/j.powtec.2021.06.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Alawi OA, Abdelrazek AH, Aldlemy MS, Ahmed W, Hussein OA, Ghafel ST, Khedher KM, Scholz M, Yaseen ZM. Heat Transfer and Hydrodynamic Properties Using Different Metal-Oxide Nanostructures in Horizontal Concentric Annular Tube: An Optimization Study. Nanomaterials (Basel) 2021; 11:nano11081979. [PMID: 34443809 PMCID: PMC8400204 DOI: 10.3390/nano11081979] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/25/2021] [Accepted: 07/27/2021] [Indexed: 11/16/2022]
Abstract
Numerical studies were performed to estimate the heat transfer and hydrodynamic properties of a forced convection turbulent flow using three-dimensional horizontal concentric annuli. This paper applied the standard k–ε turbulence model for the flow range 1 × 104 ≤ Re ≥ 24 × 103. A wide range of parameters like different nanomaterials (Al2O3, CuO, SiO2 and ZnO), different particle nanoshapes (spherical, cylindrical, blades, platelets and bricks), different heat flux ratio (HFR) (0, 0.5, 1 and 2) and different aspect ratios (AR) (1.5, 2, 2.5 and 3) were examined. Also, the effect of inner cylinder rotation was discussed. An experiment was conducted out using a field-emission scanning electron microscope (FE-SEM) to characterize metallic oxides in spherical morphologies. Nano-platelet particles showed the best enhancements in heat transfer properties, followed by nano-cylinders, nano-bricks, nano-blades, and nano-spheres. The maximum heat transfer enhancement was found in SiO2, followed by ZnO, CuO, and Al2O3, in that order. Meanwhile, the effect of the HFR parameter was insignificant. At Re = 24,000, the inner wall rotation enhanced the heat transfer about 47.94%, 43.03%, 42.06% and 39.79% for SiO2, ZnO, CuO and Al2O3, respectively. Moreover, the AR of 2.5 presented the higher heat transfer improvement followed by 3, 2, and 1.5.
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Affiliation(s)
- Omer A. Alawi
- Department of Thermofluids, School of Mechanical Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia;
| | - Ali H. Abdelrazek
- Department of Mechanical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia;
| | - Mohammed Suleman Aldlemy
- Department of Mechanical Engineering, Collage of Mechanical Engineering Technology, Benghazi, Libya;
| | - Waqar Ahmed
- Malaysia-Japan International Institute of Technology (MJIIT), Kuala Lumpur 54100, Malaysia;
- Institute for Advanced Studies, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Omar A. Hussein
- Department of Mechanical Engineering, College of Engineering-Alsharkat, Tikrit University, Tikrit 34005, Iraq;
| | | | - 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
| | - Miklas Scholz
- Division of Water Resources Engineering, Faculty of Engineering, Lund University, P.O. Box 118, 221 00 Lund, Sweden
- Department of Civil Engineering Science, School of Civil Engineering and the Built Environment, University of Johannesburg, Kingsway Campus, P.O. Box 524, Aukland Park 2006, Johannesburg 2092, South Africa
- Department of Town Planning, Engineering Networks and Systems, South Ural State University (National Research University), 76, Lenin prospekt, 454080 Chelyabinsk, Russia
- Institute of Environmental Engineering, Wroclaw University of Environmental and Life Sciences, ul. Norwida 25, 50-375 Wrocław, Poland
- Correspondence: (M.S.); (Z.M.Y.)
| | - Zaher Mundher Yaseen
- New era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar 64001, Iraq
- College of Creative Design, Asia University, Taichung City, Taiwan
- Correspondence: (M.S.); (Z.M.Y.)
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Hussein OA, Habib K, Saidur R, Muhsan AS, Shahabuddin S, Alawi OA. The influence of covalent and non-covalent functionalization of GNP based nanofluids on its thermophysical, rheological and suspension stability properties. RSC Adv 2019; 9:38576-38589. [PMID: 35540235 PMCID: PMC9075839 DOI: 10.1039/c9ra07811h] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 11/17/2019] [Indexed: 12/01/2022] Open
Abstract
Covalent functionalization (CF-GNPs) and non-covalent functionalization (NCF-GNPs) approaches were applied to prepare graphene nanoplatelets (GNPs). The impact of using four surfactants (SDS, CTAB, Tween-80, and Triton X-100) was studied with four test times (15, 30, 60, and 90 min) and four weight concentrations. The stable thermal conductivity and viscosity were measured as a function of temperature. Fourier transform infrared spectroscopy (FTIR), thermo-gravimetric analysis (TGA), X-ray diffraction (XRD) and Raman spectroscopy verified the fundamental efficient and stable CF. Several techniques, such as dispersion of particle size, FESEM, FETEM, EDX, zeta potential, and UV-vis spectrophotometry, were employed to characterize both the dispersion stability and morphology of functionalized materials. At ultrasonic test time, the highest stability of nanofluids was achieved at 60 min. As a result, the thermal conductivity displayed by CF-GNPs was higher than NCF-GNPs and distilled water. In conclusion, the improvement in thermal conductivity and stability displayed by CF-GNPs was higher than those of NCF-GNPs, while the lowest viscosity was 8% higher than distilled water, and the best thermal conductivity improvement was recorded at 29.2%.
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Affiliation(s)
- Omar A Hussein
- Department of Mechanical Engineering, Universiti Teknologi PETRONAS 32610 Bandar Seri Iskandar Perak Darul Ridzuan Malaysia +60 53687146
- Mechanical Engineering Department, College of Engineering, Tikrit University Tikrit Iraq
| | - Khairul Habib
- Department of Mechanical Engineering, Universiti Teknologi PETRONAS 32610 Bandar Seri Iskandar Perak Darul Ridzuan Malaysia +60 53687146
| | - R Saidur
- Research Centre for Nano-Materials and Energy Technology (RCNMET), School of Science and Technology, Sunway University Malaysia
- Department of Engineering, Lancaster University LA1 4YW UK
| | - Ali S Muhsan
- Petroleum Engineering Department, Universiti Teknologi PETRONAS 32610 Bandar Seri Iskandar Perak Malaysia
| | - Syed Shahabuddin
- Research Centre for Nano-Materials and Energy Technology (RCNMET), School of Science and Technology, Sunway University Malaysia
| | - Omer A Alawi
- Department of Thermofluids, School of Mechanical Engineering, Universiti Teknologi Malaysia 81310 UTM Skudai Johor Bahru Malaysia
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Alawi OA, Che Sidik NA, Dawood HK. Numerical Study of Turbulent Mixed Convection of Nanofluids in Three-Dimensional Horizontal Concentric Annuli. ACTA ACUST UNITED AC 2015. [DOI: 10.1166/jctn.2015.3987] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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