55
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Assessment and modeling of benzene micropollutant in surface waters proximal to coal-fired thermal power plants

&
Pages 740-755 | Received 07 Aug 2023, Accepted 17 Mar 2024, Published online: 21 Mar 2024

References

  • Ahmed, N., Y. S. Ok, B. H. Jeon, J. R. Kim, K. J. Chae, and S. E. Oh. 2019. “Assessment of Benzene, Toluene, Ethyl-Benzene, and Xylene (BTEX) Toxicity in Soil Using Sulfur-Oxidizing Bacterial (SOB) Bioassay.” Chemosphere 220:651–657. https://doi.org/10.1016/j.chemosphere.2018.12.102.
  • Alver, A. 2019. “Evaluation of Conventional Drinking Water Treatment Plant Efficiency According to Water Quality Index and Health Risk Assessment.” Environmental Science and Pollution Research 26:27225–27238. https://doi.org/10.1007/s11356-019-05801-y.
  • Alver, A., and Z. Kazan. 2020. “Prediction of Full-Scale Filtration Plant Performance Using Artificial Neural Networks Based on Principal Component Analysis.” Separation and Purification Technology 230:115868. https://doi.org/10.1016/j.seppur.2019.115868.
  • Arkoç, O., T. Ç. Akıncı, and H. S. Nogay. 2016. “Yapay Sinir Ağları Yardımı ile Yeraltı Suyunda Sodyum Absorpsiyon Oranı (SAR) Tahmini: Ergene Havzası Doğu Akiferi Örneği.” Jeoloji Mühendisliği Dergisi 40 (2): 177–188. https://doi.org/10.24232/jeoloji-muhendisligi-dergisi.295443.
  • Badkar, D. S., K. S. Pandey, and G. Buvanashekaran. 2013. “Development of RSM- and ANN-Based Models to Predict and Analyze the Effects of Process Parameters of Laser-Hardened Commercially Pure Titanium on Heat Input and Tensile Strength.” The International Journal of Advanced Manufacturing Technology 65:1319–1338. https://doi.org/10.1007/s00170-012-4259-0.
  • Bagherzadeh, F., M. Mohamad-Javad, M. Basirifard, and J. Roostaei. 2021. “Comparative Study on Total Nitrogen Prediction in Wastewater Treatment Plant and Effect of Various Feature Selection Methods on Machine Learning Algorithms Performance.” Journal of Water Process Engineering 41:102033. https://doi.org/10.1016/j.jwpe.2021.102033.
  • Baştürk, E., and A. Alver. 2019. “Modeling Azo Dye Removal by Sono-Fenton Processes Using Response Surface Methodology and Artificial Neural Network Approaches.” Journal of Environmental Management 248:109300. https://doi.org/10.1016/j.jenvman.2019.109300.
  • Bergou, E. H., Y. Diouane, and V. Kungurtsev. 2020. “Convergence and Complexity Analysis of a Levenberg–Marquardt Algorithm for Inverse Problems.” Journal of Optimization Theory and Applications 185:927–944. https://doi.org/10.1007/s10957-020-01666-1.
  • Chang, W., Y. S. Um, B. Hoffman, and T. R. P. Holoman. 2005. “Molecular Characterization of Polycyclic Aromatic Hydrocarbon (PAH)-Degrading Methanogenic Communities.” Biotechnology Progress 21 (3): 682–688. https://doi.org/10.1021/bp049579l.
  • Dehmani, Y., J. Lain, A. Daouli, L. Sellaoui, A. Bonilla-Petriciolet, T. Lamhasni, S. Abouarnadasse, and M. Badawi. 2023. “Unravelling the Adsorption Mechanism of Phenol on Zinc Oxide at Various Coverages via Statistical Physics, Artificial Neural Network Modeling and Ab Initio Molecular Dynamics.” Chem Eng J 452:139171. https://doi.org/10.1016/j.cej.2022.139171.
  • Dou, J. F., X. Liu, Z. F. Hu, and D. Deng. 2008. “Anaerobic BTEX Biodegradation Linked to Nitrate and Sulfate reduction.” Journal of Hazardous Materials 151:720–729. https://doi.org/10.1016/j.jhazmat.2007.06.043.
  • Elmolla, E. S., M. Chaudhuria, and M. M. Eltoukhy. 2010. “The Use of Artificial Neural Network (ANN) for Modeling of COD Removal from Antibiotic Aqueous Solution by the Fenton Process.” Journal of Hazardous Materials 179: 127–134. https://doi.org/10.1016/j.jhazmat.2010.02.068.
  • Elwardany, M., A. M. Nassib, and H. A. Mohamed. 2024. “Advancing Sustainable Thermal Power Generation: Insights from Recent Energy and Exergy Studies.” Process Safety and Environmental Protection 183:617–644. https://doi.org/10.1016/j.psep.2024.01.039.
  • Erickson, T. A., R. Nijjar, M. J. Kipper, and K. L. Lear. 2014. “Characterization of Plasma-Enhanced Teflon AF for Sensing Benzene, Toluene, and Xylenes in Water with Near-IR Surface Plasmon Resonance.” Talanta 119:151–155. https://doi.org/10.1016/j.talanta.2013.10.038.
  • García-Alba, J., J. F. Barcena, C. Ugarteburu, and A. García. 2019. “Artificial Neural Networks As Emulators of Process-Based Models to Analyse Bathing Water Quality in Estuaries.” Water Research 150:283–295. https://doi.org/10.1016/j.watres.2018.11.063.
  • Gholizadeh, F., and F. Sabzi. 2017. “Prediction of CO2 Sorption in Poly(ionic Liquid)s Using ANN-GC and ANFIS- GC Models.” International Journal of Greenhouse Gas Control 63:95–106. https://doi.org/10.1016/j.ijggc.2017.05.013.
  • Granato, D., J. S. Santos, G. B. Escher, B. L. Ferreira, and R. M. Maggio. 2018. “Use of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) for Multivariate Association Between Bioactive Compounds and Functional Properties in Foods: A Critical Perspective.” Trends in Food Science & Technology 72: 83–90. https://doi.org/10.1016/j.tifs.2017.12.006.
  • Hornik, K. M., M. Stinchcombe, and H. White. 1989. “Multilayer Feedforward Networks Are Universal Approximators.” Neural Networks 2 (5): 359–366. https://doi.org/10.1016/0893-6080(89)90020-8.
  • Hosseinzadeh, A., A. A. Najafpoor, A. J. Jafari, R. K. Jazani, M. Baziar, H. Bargozin, and F. G. Piranloo. 2018. “Application of Response Surface Methodology and Artificial Neural Network Modeling to Assess Non-Thermal Plasma Efficiency in Simultaneous Removal of BTEX from Waste Gases: Effect of Operating Parameters and Prediction Performance.” Process Safety and Environmental Protection 119: 261–270. doi:https://doi.org/10.1016/j.psep.2018.08.010.
  • Huang, L., H. Cheng, S. Ma, R. He, J. Gong, G. Li, and T. An. 2021. “The Exposures and Health Effects of Benzene, Toluene and Naphthalene for Chinese Chefs in Multiple Cooking Styles of Kitchens.” Environment International 156:106721. https://doi.org/10.1016/j.envint.2021.106721.
  • Jamro, I. A., A. Raheem, S. Khoso, H. A. Baloch, A. Kumar, G. Chen, W. A. Bhagat, T. Wenga, and W. Ma. 2023. “Investigation of Enhanced H2 Production from Municipal Solid Waste Gasification via Artificial Neural Network with Data on Tar Compounds.” Journal of Environmental Management 328:117014. https://doi.org/10.1016/j.jenvman.2022.117014.
  • Jolliffe, I. T., and J. Cadima. 2015. “Principal Component Analysis: A Review and Recent Developments.” Philosophical Transactions A 374: 20150202. https://doi.org/10.1098/rsta.2015.0202.
  • Khajeh, M., M. Kaykhaii, S. H. Hashemi, and M. Shakeri. 2014. “Particle Swarm Optimization–Artificial Neural Network Modeling and Optimization of Leachable Zinc from Flour Samples by Miniaturized Homogenous Liquid–Liquid Microextraction.” Journal of Food Composition and Analysis 33 (1): 32–38. https://doi.org/10.1016/j.jfca.2013.11.002.
  • Liu, Y., S. Hao, X. Zhao, X. Li, X. Qiao, D. D. Dionysiou, and B. Zheng. 2020. “Distribution Characteristics and Health Risk Assessment of Volatile Organic Compounds in the Groundwater of Lanzhou City, China.” Environmental Geochemical Health 42 (11): 3609–3622. https://doi.org/10.1007/s10653-020-00591-6.
  • The MathWorks Inc. 2019. MATLAB version: 9.13.0 (R2019b). Natick, Massachusetts: The MathWorks Inc. https://www.mathworks.com.
  • Onifade, M. 2021. “Countermeasures Against Coal Spontaneous Combustion: A Review.” International Journal of Coal Preparation and Utilization 42 (10): 2953–2975. https://doi.org/10.1080/19392699.2021.1920933.
  • Ozel, H. U., B. T. Gemici, E. Gemici, H. B. Ozel, M. Cetin, and H. Sevik. 2020. “Application of Artificial Neural Networks to Predict the Heavy Metal Contamination in the Bartin River.” Environmental Science and Pollution Research 27 (34): 42495–42512. https://doi.org/10.1007/s11356-020-10156-w.
  • Rajasekhar, B., I. M. Nambi, and S. K. Govindarajan. 2020. “Human Health Risk Assessment for Exposure to BTEXN in an Urban Aquifer Using Deterministic and Probabilistic Methods: A Case Study of Chennai City, India.” Environmental Pollution 265:114814. https://doi.org/10.1016/j.envpol.2020.114814.
  • Rumelhart, D. E., G. E. Hinton, and R. J. Williams. 1986. “Learning Representations by Back-Propagating Errors.” Nature 323 (6088): 533–536. https://doi.org/10.1038/323533a0.
  • Santos, M. D. A., B. E. Tavora, S. Koide, and E. D. Caldas. 2013. “Human Risk Assessment of Benzene After a Gasoline Station Fuel Leak.” Revista de Saúde Pública 47 (2): 335–344. https://doi.org/10.1590/s0034-8910.2013047004381.
  • Tao, H., N. K. Al-Bedyry, K. M. Khedher, S. Shahid, and Z. M. Yaseenf. 2021. “River Water Level Prediction in Coastal Catchment Using Hybridized Relevance Vector Machine Model with Improved Grasshopper Optimization.” Journal of Hydrology 598:126477. https://doi.org/10.1016/j.jhydrol.2021.126477.
  • Thambavani, D. S., and B. Kavitha. 2014. “Prediction and Simulations of Chromium (VI) Ions Removal Efficiency by Riverbed Sand Adsorbent Using Artificial Neural Networks.” International Journal of Engineering Sciences & Research Technology 3 (5): 906–913.
  • Tumer, A. E., and S. Edebali. 2015. “An Artificial Neural Network Model for Wastewater Treatment Plant of Konya.” International Journal of Intelligent Systems and Applications in Engineering 3 (4): 131–135. https://doi.org/10.18201/ijisae.65358.
  • Ubah, J. I., L. C. Orakwe, K. N. Ogbu, J. I. Awu, I. E. Ahaneku, and E. C. Chukwuma. 2021. “Forecasting Water Quality Parameters Using Artificial Neural Network for Irrigation Purposes.” Scientific Reports 11:24438. https://doi.org/10.1038/s41598-021-04062-5.
  • Voumik, L. C., M. A. Islam, S. Ray, N. Y. Mohamed Yusop, and A. R. Ridzuan. 2023. “CO2 Emissions from Renewable and Non-Renewable Electricity Generation Sources in the G7 Countries: Static and Dynamic Panel Assessment.” Energies 16:1044. https://doi.org/10.3390/en16031044.
  • Wu, W., G. C. Dandy, and H. R. Maier. 2014. “Protocol for Developing ANN Models and Its Application to the Assessment of the Quality of the ANN Model Development Process in Drinking Water Quality Modelling.” Environmental Modelling & Software 54:108–127. https://doi.org/10.1016/j.envsoft.2013.12.016.
  • Yang, S., X. Wang, Q. Yang, E. Dong, and S. Du. 2022. “Instance Segmentation Based on Improved Self-Adaptive Normalization.” Sensors 22:4396. https://doi.org/10.3390/s22124396.
  • Yaseen, Z. M., A. El-Shafie, O. Jaafar, H. A. Afan, and K. N. Sayl. 2015. “Artificial Intelligence Based Models for Stream-Flow Forecasting: 2000–2015.” Journal of Hydrology 530:829–844. https://doi.org/10.1016/j.jhydrol.2015.10.038.
  • Zhang, Y., X. Gao, K. Smith, G. Inial, S. Liu, L. B. Conil, and B. Pan. 2019. “Integrating Water Quality and Operation into Prediction of Water Production in Drinking Water Treatment Plants by Genetic Algorithm Enhanced Artificial Neural Network.” Water Research 164:114888. https://doi.org/10.1016/j.watres.2019.114888.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.