Application of Machine Learning Models for Prediction of the Sorption Ability of Rubber Chips Filter

Document Type : Original Article

Authors

1 Lectuerer, Department of Soil Science, Faculty of Water and Soil, Zabol University, Iran

2 Former PhD Student, Department of water engineering, Faculty of Water and Soil, Zabol University, Iran.

Abstract

In recent decades, the potential danger of heavy metals in effluents and the entry of water into surface and groundwater resources have been increasingly being considered by the international community. The aim of this study is to provide an indirect method for estimating the efficiency of absorption of rubber chips filter for heavy metals lead, zinc and manganese from industrial effluents. Column adsorption test in a pilot system was conducted as a factorial experiment with three factors based on a completely randomized design with three replications. Three factors were studied including particle size (0.5 and 5 cm), filter thickness (10, 30 and 50 cm) and sorbent contact time with solution. The adsorption of the elements was modeled using 6 models of linear regression, regression tree, artificial neural network, random forest, cubist and support vector machine, based on the Field absorption experiments data. The results showed random forest models, artificial neural network, regression tree and cubist had acceptable performance for predicting adsorption efficiency in all three elements. However, according to the R2 coefficient and RMSE, random forest and artificial neural network showed more satisfactory performance than regression tree and cubist model. Evaluation of the importance showed the high importance of the parameter of adsorbent contact time with the metal solution in all machine learning models. The accurate predicted ability of developed models could significantly reduce field experiment workload such as predicting the removal efficiency of rubber chips. The relative importance of variables could provide a right direction for better treatments of heavy metals.

Keywords

References

Abbas, A.; Al-Amer, A.M.; Laoui, T.; Al-Marri, M.J.; Nasser, M.S.; Khraisheh, M. & Atieh, M.A. 2016. Heavy metal removal from aqueous solution by advanced carbon nanotubes: critical review of adsorption applications. Separation and Purification Technology. 157:141–161.
Abedi Koupaei, J. & Mousavi, F. 2004. Adsorption of lead from industrial effluent by paddy husk ash, Journal of Water and Wastewater, 40: 37-33. (In Persian)
Ajamzadeh, A.; Mollaeinia, M.R. & Ghandahari, Gh. 2017. Comparison of Artificial Intelligence Methods in Predicting Daily Time Series of Minimum and Maximum Temperature and Precipitation in Tangab Dam Station (Fars Province). Geographical Space, 59: 205-228. (In Persian)
Ansari Mahabadi, A. 2005. Removal of Nitrate and Ammonium from Groundwater by Fine Mineral Filters, Applied Research Project, Deputy of Research and Basic Studies, Iran Water Resources Management Company. (In Persian)
Asadi, F. 2002. Removal of heavy metals from industrial wastewater by rice husk, sawdust and soil, Master Thesis in Soil Science, Faculty of Agriculture, Isfahan University of Technology. (In Persian)
Babiker, E.; Al-Ghouti, M.A.; Zouari, N. & McKay, G. 2019. Removal of boron from water using adsorbents derived from waste tire rubber. Journal of environmental chemical engineering. 7(2): 102948.
Bădescu, I.S.; Bulgariu, D.; Ahmad, I. & Bulgariu, L. 2018. Valorisation possibilities of exhausted biosorbents loaded with metal ions–a review. Journal of Environmental Management. 224: 288–297.
Dehghanian, N.; Ghaedi, M.; Ansari, A.; Ghaedi, A.; Vafaei, A.; Asif, M.; Agarwal, S.; Tyagi, I. & Gupta, V.K., 2016. A random forest approach for predicting the removal of Congo red from aqueous solutions by adsorption onto tin sulfide nanoparticles loaded on activated carbon. Desalination and Water Treatment. 57: 9272–9285.
Emigdio, Z.; Abatal, M.; Bassam, A.; Trujillo, L.; Juárez-Smith, P. & El Hamzaoui, Y. 2017. Modeling the adsorption of phenols and nitrophenols by activated carbon using genetic programming. Journal of Cleaner Production. 161:860–870.
Febrianto, J.; Kosasih, A.N.; Sunarso, J.; Ju, Y.; Indraswati, N. & Ismadji, S. 2009. Equilibrium and kinetic studies in adsorption of heavy metals using biosorbent: a summary of recent studies. Journal of Hazardous Materials. 162:616–645.
Granata, F.; Papirio, S.; Esposito, G.; Gargano, R. & Marinis, G. 2017. Machine Learning Algorithms for the Forecasting of Wastewater Quality Indicators. Water. 9(105), doi:10.3390/w9020105.
Gupta, V.; Ganjali, M.R.; Nayak, A.; Bhushan, B. & Agarwal, Sh. 2012. Enhanced heavy metals removal and recovery by mesoporous adsorbent prepared from waste rubber tire. Chemical Engineering Journal. 197: 330–342.
Hasani, Z.: Mirabbasi-Najafabadi, R. and Ghasemi, A.R. 2018. Prediction of Groundwater Quality of Khanmirza plain Using Decision tree Method. Hydrogeology, 3(1): 99-110. (In Persian)
Kazemipour, M.; Ansari, M.; Tajrobehkar, S.; Majdzadeh, M. & Reihani Kermani, H. 2008. Removal of lead, cadmium, zinc and cooper from industrial wastewater by carbon developed from walnut, hazelnut, almond, pistachio shell and apricot stone, Journal of Hazardous Materials. 150: 322-327.
Khaleghi, M.; Hassanpour, F.; Karandish, F. & Shahnazari, A. 2020. Integrating partial root-zone drying and saline water irrigation to sustain sunflower production in freshwater-scarce regions. Agricultural Water Management. 234, https://doi.org/10.1016/j.agwat.2020.106094.
Kołodyńska, D.; Krukowska, J. & Thomas, P. 2017. Comparison of sorption and desorption studies of heavy metal ions from biochar and commercial active carbon. Chemical Engineering Journal. 307: 353–363.
Ma, W.; Tan, K. & Du, P. 2016. Predicting soil heavy metal based on random forest model. IGARSS: 4331-4334. 978-1-5090-3332-4/16/$31.00 ©2016 IEEE.
Mirakzehi, Kh.; Shahriari, A.; Pahlavan-Rad, M.R. & Bameri, A. 2017. Application of random forest method for predicting soil classes in low relief lands (Case study: Hirmand county). Journal of Water and Soil Conservation, 24(1): 67-84. (In Persian)
Noi, P.T.; Degener, J. & Kappas, M. 2017. Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data. Remote sensing. 9(398). doi:10.3390/rs9050398.
Norouzi, H.; Nadiri, A.A.; Asghari Mogaddam, A. & Gharekhani, M. 2017. Prediction of Transmissivity of Malikan Plain Aquifer Using Random Forest Method. Water and Soil Science, 27(2):61-75. (In Persian)
Omidvar, K.; Shafie, Sh.; Taghizadeh, Z. & Alipour, M. 2014. Evaluating the efficiency of the decision tree model in predicting rainfall in Kermanshah synoptic station. Journal of Applied researches in Geographical Sciences, 34: 89-110. (In Persian)
Peng, W.; Li, H.; Liu, Y. & Song, S. 2017. A review on heavy metal ions adsorption from water by graphene oxide and its composites. Journal of Molecular Liquids. 230: 496–504.
Phasuphan, W.; Praphairaksit, N. & Imyim, A. 2019. Removal of ibuprofen, diclofenac, and naproxen from water using chitosan-modified waste tire crumb rubber. Journal of Molecular Liquids. (294): 111554.
Quinlan, R. 1993. Combining instance based and model based learning. In Proceedings of the Tenth International Conference on Machine Learning. Amherst. MA. USA. 27–29. pp. 236–243.
Ram Bishnoi, N.; Bijaj, M.; Sharma, N. & Gupta, A. 2004. Adsorption of Cr (VI) on activated rice husk carbon and activated alumina. Bioresource Technology. 91: 305-307.
Rastegarnia, M. & Sanaati, p. 2016. Determination of hydraulic flow units using random forest method for one of Iran's oil reservoirs, Scientific-Extension Monthly of Oil and Gas Exploration and Production, 135: 55-59. (In Persian)
Rezaei, M.: Sameni, A. & Fallah-Shamsi, S.R. 2018. Advanced machine learning methods for wind erosion monitoring in southern Iran. Journal of Environmental Erosion Research, Vol: 29(8:1): 39-58. (In Persian)
Samadianfard, S. & Panahi, S. 2018. Estimating Daily Reference Evapotranspiration using Data Mining Methods of Support Vector Regression and M5 Model Tree. Journal of Watershed Management Research, 10(18):157-167. (In Persian)
Tanji, K.K. 1990. Agricultural Salinity Assessment and Management. ASCE Manuals and Reports on engineering practice. No. 71. New York. 481P.
Tariqul Islam M.d.; Saenz-Arana, R.; Hernandez, C.; Guinto, T.; Ariful Ahsan, M.d.; Bragg, D.T.; Wang, H.; Alvarado-Tenorio, B. & Noveron, J. 2018. Conversion of waste tire rubber into a high-capacity adsorbent for the removal of methylene blue, methyl orange, and tetracycline from water. Journal of Environmental Chemical Engineering. https://doi.org/10.1016/j.jece.2018.04.058.
Uddin, M.K. 2017. A review on the adsorption of heavy metals by clay minerals, with special focus on the past decade. Chemical Engineering Journal, 308: 438–462.
Zhou, J.; Li, E.; Wei, H.; Li, Ch.; Qiao, Q. & Jahed Armaghani, D. 2019. Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials. Applied Sciences. 9(1621), doi:10.3390/app9081621.
Zhu, X.; Wang, X. & Sik Ok, Y. 2019. The application of machine learning methods for prediction of metal sorption onto biochars. Journal of Hazardous Materials. 378: 120727.
Zhu, X.; Wu, G.; Coulon, F.; Wu, L. & Chen, D. 2018. Correlating asphaltene dimerization with its molecular structure by potential of mean force calculation and data mining. Energy Fuel. 32:5779–5788.
 
Environmental Researches
Volume 12, Issue 24
Recerches
March 2022
Pages 197-208

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