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Effect of mixing Nano-silica and Perlite with pervious concrete for nitrate removal from the contaminated water

  • Hosseini, Seyed Azim (Department of Civil Engineering, South Tehran Branch, Islamic Azad University) ;
  • Toghroli, Ali (Department of Civil Engineering, South Tehran Branch, Islamic Azad University)
  • Received : 2020.07.26
  • Accepted : 2021.05.11
  • Published : 2021.06.25
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Abstract

Nitrogen and phosphorus contained in storm water runoff contaminate both surface and ground waters, causing problems for natural aquatic systems and human health. Pervious concrete particularly developed for pollutant removal might be a novel notion to remove nitrate from runoff. In this research, three aggregates were used in pervious concrete as Nano silica, perlite (Pe) and zeolite (Z) which are able to adsorb pollutants. The water absorption, compressive strength, porosity, permeability of aggregate and nitrate removal of concrete samples were evaluated in tests. While adding Nano silica, perlite and zeolite, experiments have shown that addition of fine grains enhances runoff efficiency and compressive strength, but reduces permeability and porosity. Both adsorbents improved the runoff quality while increasing the additives' ratio. The mechanical properties were density of 879 (Kg/m3), permeability of 1.06 (cm/s), the void rate of 19.7(%), compressive strength of 3.6 (MPa), and nitrate removal of about 75%. While adding perlite with the highest (40%) water absorption, the results showed no reactivity of aggregates in terms of alkali-silica reaction. Considering the Permeability tests, perlite has the minimum (1.4 cm/s) permeability, and then adding perlite to previous concrete brought the nitrate absorption capacity about 70 ml/g (60%). The best results belonged to samples containing zeolite treatments (with 15% adsorbent and 0, 10, 20% fine-grains) with a maximum pollution decline and improved Chemical Oxygen Demand (COD) (87.1, 82.6 and 89.3%), Biochemical oxygen demand (BOD) (88.1, 87.3 and 90.7%) and Total suspended solids (TSS) (75.8, 79.1 and 84.6%). Thus, zeolite has shown effective mechanical characteristics and runoff quality, while perlite and Nano silica has shown a poor and moderate performance among the adsorbents in all tests, respectively.

Keywords

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  84. Shariati, M., Sulong, N.R., Suhatril, M., Shariati, A., Khanouki, M.A. and Sinaei, H. (2013), "Comparison of behaviour between channel and angle shear connectors under monotonic and fully reversed cyclic loading", Constr. Build. Mater., 38, 582-593. https://doi.org/10.1016/j.conbuildmat.2012.07.050.
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  86. Shariati, M., Trung, N.T., Wakil, K., Mehrabi, P., Safa, M. and Majid. K.. (2019), "Moment-rotation estimation of steel rack connection using extreme learning machine", Steel Compos. Struct., 31(5), 427-435. https://doi.org/10.12989/scs.2019.31.5.427.
  87. Shariati, M. (2008), "Assessment of building using nonedestructive test techniques (ultra sonic pulse velocity and schmidt rebound hammer)", Universiti Putra Malaysia.
  88. Shariati, M., Armaghani, D.J., Khandelwal, M., Zhou, J. and Khorami, M. (2021), "Assessment of longstanding effects of fly ash and silica fume on the compressive strength of concrete using extreme learning machine and artificial neural network", J. Adv. Eng. Comput., 5(1), 50-74. http://doi.org/10.25073/jaec.202151.308.
  89. Shariati, M., Azar, S.M., Arjomand, M.A., Tehrani, H.S., Daei, M. and Safa, M. (2019), "Comparison of dynamic behavior of shallow foundations based on pile and geosynthetic materials in fine-grained clayey soils", Geomech. Eng., 19(6), 473-484. https://doi.org/10.12989/gae.2019.19.6.473.
  90. Shariati, M., Azar, S.M., Arjomand, M.A., Tehrani, H.S., Daei, M. and Safa, M. (2020), "Evaluating the impacts of using piles and geosynthetics in reducing the settlement of fine-grained soils under static load", Geomech. Eng., 20(2), 87-101. https://doi.org/10.12989/gae.2020.20.2.087.
  91. Shariati, M., Faegh, S.S., Mehrabi, P., Bahavarnia, S., Zandi, Y., Masoom, D.R., ... & Salih, M.N. (2019), "Numerical study on the structural performance of corrugated low yield point steel plate shear walls with circular openings", Steel Compos. Struct., 33(4), 569-581. https://doi.org/10.12989/scs.2019.33.4.569.
  92. Shariati, M., Ghorbani, M., Naghipour, M., Alinejad, N., & Toghroli, A. (2020), "The effect of RBS connection on energy absorption in tall buildings with braced tube frame system", Steel Compos. Struct., 34(3), 393-407. https://doi.org/10.12989/scs.2020.34.3.393.
  93. Shariati, M., Grayeli, M., Shariati, A. and Naghipour, M. (2020), "Performance of composite frame consisting of steel beams and concrete filled tubes under fire loading", Steel Compos. Struct., 36(5), 587-602. https://doi.org/10.12989/scs.2020.36.5.587.
  94. Shariati, M., Heyrati, A., Zandi, Y., Laka, H., Toghroli, A., Kianmehr, P., ... & Poi-Ngian, S. (2019), "Application of waste tire rubber aggregate in porous concrete", Smart Struct. Syst., 24(4), 553-566. https://doi.org/10.12989/sss.2019.24.4.553.
  95. Shariati, M., Mafipour, M.S., Haido, J.H., Yousif, S.T., Toghroli, A., Trung, N.T. and Shariati, A. (2020), "Identification of the most influencing parameters on the properties of corroded concrete beams using an Adaptive Neuro-Fuzzy Inference System (ANFIS)", Steel Compos. Struct., 34(1), 155. http://doi.org/10.12989/scs.2020.34.1.155.
  96. Shariati, M., Mafipour, M.S., Mehrabi, P., Bahadori, A., Zandi, Y., Salih, M.N., ... & Poi-Ngian, S. (2019), "Application of a hybrid artificial neural network-particle swarm optimization (ANN-PSO) model in behavior prediction of channel shear connectors embedded in normal and high-strength concrete", Appl. Sci., 9(24), 5534. https://doi.org/10.3390/app9245534.
  97. Shariati, M., Mafipour, M.S., Mehrabi, P., Shariati, A., Toghroli, A., Trung, N.T. and Salih, M.N. (2020), "A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques", Eng. Comput., 1-21. https://doi.org/10.1007/s00366-019-00930-x.
  98. Shariati, M., Mafipour, M.S., Mehrabi, P., Zandi, Y., Dehghani, D., Bahadori, A., ... & Poi-Ngian, S. (2019), "Application of Extreme Learning Machine (ELM) and Genetic Programming (GP) to design steel-concrete composite floor systems at elevated temperatures", Steel Compos. Struct., 33(3), 319-332. https://doi.org/10.12989/scs.2019.33.3.319.
  99. Shariati, M., Naghipour, M., Yousofizinsaz, G., Toghroli, A. and Tabarestani, N.P. (2020), "Numerical study on the axial compressive behavior of built-up CFT columns considering different welding lines", Steel Compos. Struct., 34(3), 377-391. https://doi.org/10.12989/scs.2020.34.3.377.
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