References

  1. M. Ogórek, Ł. Gąsior, O. Pierzchała, R. Daszkiewicz, M. Lenartowicz, Role of copper in the process of spermatogenesis, Postepy Hig. Med. Dosw., 71 (2017) 663–670.
  2. A.K. Szczepanska, K. Malinowska, I. Majsterek, An evaluation of the antioxidant and anticancer properties of complex compounds of copper(II), platinum(II), palladium(II) and ruthenium(III) for use in cancer therapy, Mini Rev. Med. Chem., 18 (2018) 1373–1381.
  3. O. Kaplan, G. Kaya, M. Yaman, Sequential and selective extraction of copper in different soil phases and plant parts from former industrialized area, Commun. Soil Sci. Plant Anal., 42 (2011) 2391–2401.
  4. J. Zhang, The compare of different determine methods for cuprum content, Port Health Control, 6 (2001) 22–23 (in Chinese).
  5. M. Malik, A. Mansur, Copper sulphate poisoning and exchange transfusion, Saudi J. Kidney Dis. Transpl., 22 (2011) 1240–1242.
  6. W. Liu, T. Yang, J. Xu, Q. Chen, C. Yao, S. Zuo, Y. Kong, C. Fu, Preparation and adsorption property of attapulgite/carbon nanocomposite, Environ. Prog. Sustainable Energy, 34 (2015) 437–444.
  7. Y. Feng, Y. Wang, Y. Wang, S. Liu, J. Jiang, C. Cao, J. Yao, Simple fabrication of easy handling millimeter-sized porous attapulgite/polymer beads for heavy metal removal, J. Colloid Interface Sci., 502 (2017) 52–58.
  8. T. Falayi, F. Ntuli, Effect of attapulgite calcination on heavy metal adsorption from acid mine drainage, Korean J. Chem. Eng., 32 (2015) 707–716.
  9. Y.W. Zheng, W.W. Tao, G.F. Zhang, C. Lv, Y.P. Zhao, L. Chen, Adsorptive removal of Ni(II) ions from aqueous solution by polyacrylic acid/attapulgite composite hydrogels, Key Eng. Mater., 727 (2017) 859–865.
  10. J.D. Sudha, A. Pich, V.L. Reena, S. Sivakala, H.J.P. Adler, Waterdispersible multifunctional polyaniline-laponite-keggin iron nanocomposites through a template approach, J. Mater. Chem., 21 (2011) 16642–16650.
  11. G.I. Danmaliki, T.A. Saleh, A.A. Shamsuddeen, Response surface methodology optimization of adsorptive desulfurization on nickel/activated carbon, Chem. Eng. J., 313 (2017) 993–1003.
  12. H. Guan, Z. Dai, A. Zhao, J. He, A novel stock forecasting model based on high-order-fuzzy-fluctuation trends and back propagation neural network, PLoS One, 13 (2018) e0192366– e0192372, doi: 10.1371/journal.pone.0192366.
  13. N. Zhang, K. Zhou, L. Dong, Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China, Earth Sci. Inform., 11 (2018) 553–566.
  14. Y. Zhang, X.U. Jinrui, F.U. Xinghu, J. Liu, Y. Tian, Hybrid algorithm combining genetic algorithm with back propagation neural network for extracting the characteristics of multi-peak Brillouin scattering spectrum, Front. Optoelectron., 10 (2017) 62–69.
  15. L. Zhuo, J. Zhang, P. Dong, Y. Zhao, B. Peng, An SA–GA–BP neural network-based color correction algorithm for TCM tongue images, Neurocomputing, 134 (2014) 111–116.
  16. R. Hazime, Q.H. Nguyen, C. Ferronato, T.K.X. Huynh, J.M. Chovelon, Optimization of imazalil removal in the system UV/TiO2/K2S2O8 using a response surface methodology (RSM), Appl. Catal., B, 132–133 (2013) 519–526.
  17. M. Liu, X. Liu, M. Li, M. Fang, W. Chi, Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices, Biosyst. Eng., 106 (2010) 223–233.
  18. A.R. Awad, I. Von Poser, M.T. Aboul-Ela, Optimal removal of heavy metals pollutants from groundwater using a real genetic algorithm and finite-difference method, J. Comput. Civil Eng., 27 (2013) 522–533.
  19. Y. Ge, J.Z. He, Y.G. Zhu, J.B. Zhang, Z. Xu, L.M. Zhang, Y.M. Zheng, Differences in soil bacterial diversity: driven by contemporary disturbances or historical contingencies?, ISME J., 2 (2008) 254–264.
  20. J.Z. He, Y. Ge, Z. Xu, C. Chen, Linking soil bacterial diversity to ecosystem multifunctionality using backward-elimination boosted trees analysis, J. Soils Sediments, 9 (2009) 547–550.
  21. M. Shanmugaprakash, J. Kirthika, J. Ragupathy, K. Nilanee, A. Manickam, Statistical based media optimization and production of naringinase using Aspergillus brasiliensis 1344, Int. J. Biol. Macromol., 64 (2014) 443–452.
  22. M. Hesham, P. Bruno, S. Sadique, C. Gert, Hardwareefficient on-line learning through pipelined truncated-error backpropagation in binary-state networks, Front. Neurosci., 11 (2017) 496–504.
  23. S. Ding, C. Su, J. Yu, An optimizing BP neural network algorithm based on genetic algorithm, Artif. Intell. Rev., 36 (2011) 153–162.
  24. D. Zhang, J. Xu, C. Li, Model for food safety warning based on inspection data and BP neural network, Trans. Chin. Soc. Agric. Eng., 26 (2010) 221–226 (in Chinese).
  25. L.X. Guo, M.Y. Zhao, A parallel search genetic algorithm based on multiple peak values and multiple rules, Mech. Technol., 129 (2002) 539–544.
  26. U. Maulik, S. Bandyopadhyay, Genetic algorithm-based clustering technique, Pattern Recognit., 33 (2000) 1455–1465.
  27. Y. Fei, H. Mao, L. Hua, A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters, Mater. Des., 32 (2011) 3457–3464.
  28. Z. Niu, Q. Fan, W. Wang, J. Xu, L. Chen, W. Wu, Effect of pH, ionic strength and humic acid on the sorption of uranium(VI) to attapulgite, Appl. Radiat. Isot., 67 (2009) 1582–1590.
  29. F. Gourbilleau, C. Ternon, D. Maestre, O. Palais, C. Dufour, Silicon-rich SiO2/SiO2 multilayers: a promising material for the third generation of solar cell, J. Appl. Phys., 106 (2009) 13501–13507.
  30. A. Saito, A. Kawakami, H. Shimakage, Z. Wang, As-grown MgB2 thin films deposited on Al2O3 substrates with different crystal planes, Supercond. Sci. Technol., 15 (2002) 1325–1330.
  31. H. Xu, P.J. Heaney, G.H. Beall, Phase transitions induced by solid solution in stuffed derivatives of quartz: a powder synchrotron XRD study of the LiAlSiO4-SiO2 join, Am. Mineral, 85 (2015) 971–979.
  32. P.P. Kundu, R.C. Larock, Montmorillonite-filled nanocomposites of tung oil/styrene/divinylbenzene polymers prepared by thermal polymerization, J. Appl. Polym. Sci., 119 (2011) 1297–1306.
  33. M.Y. Fan, J.W. Hu, R.S. Cao, K.N. Xiong, X.H. Wei, Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO, Sci. Rep., 7 (2017) 18040–18047.
  34. R.S. Cao, M.Y. Fan, J.W. Hu, W.Q. Ruan, K.N. Xiong, X.H. Wei, Optimizing low-concentration mercury removal from aqueous solutions by reduced graphene oxide-supported Fe3O4 composites with the aid of an artificial neural network and genetic algorithm, Materials, 10 (2017) 1279–1285.
  35. W. Wang, M. Amiri, K. Kozma, Reaction pathway to the inaugural open-shell transition-metal Keggin ion without organic ligation, Eur. J. Inorg. Chem., 2018 (2018) 4638–4642.
  36. M. Igawa, S. Akiyama, R. Okada, C. Sugawara, T. Kurokawa, Separation of heavy metal Ions with a chelating reagent fixed in an anion-exchange membrane, J. Ion Exch., 18 (2007) 506–509.
  37. W.Q. Ruan, J.W. Hu, J.M. Qi, Y. Hou, R.S. Cao, X.H. Wei, Removal of crystal violet by using reduced-graphene-oxide-supported bimetallic Fe/Ni nanoparticles (rGO/Fe/Ni): application of artificial intelligence modeling for the optimization process, Materials, 11 (2018) 865–872.
  38. M.Y. Fan, T.J. Li, J. W. Hu, Synthesis and characterization of reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites used for Pb(II) removal, Materials, 9 (2016) 687–695.
  39. M.Y. Fan, J.W. Hu, R.S. Cao, W.Q. Ruan, X.H. Wei, A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence, Chemosphere, 200 (2018) 330–343.
  40. E. Yan, J. Song, C. Liu, Comparison of support vector machine, back propagation neural network and extreme learning machine for syndrome element differentiation, Artif. Intell. Rev., 53 (2019) 2453–2481.
  41. H. Mustafidah, S. Suwarsito, Correlation analysis between error rate of output and learning rate in backpropagation network, Adv. Sci. Lett., 24 (2018) 9182–9185.
  42. V. Couvreur, M.M. Kandelous, B.L. Sanden, Downscaling transpiration rate from field to tree scale, Agric. For. Meteorol., 221 (2016) 71–77.
  43. R.S. Cao, M.Y. Fan, J.W. Hu, W.Q. Ruan, X.L. Wu, X.H. Wei, Artificial intelligence based optimization for the Se(IV) removal from aqueous solution by reduced graphene oxide-supported nanoscale zero-valent iron composites, Materials, 11 (2018) 428–439.
  44. H. Norouzi, S. Shahmohammadi-Kalalagh, Locating groundwater artificial recharge sites using random forest: a case study of Shabestar region, Iran, Environ. Geol., 78 (2019) 380–391.
  45. B. He, D. Xiao, Q. Hu, Automatic magnetic resonance image prostate segmentation based on adaptive feature learning probability boosting tree initialization and CNN-ASM refinement, IEEE Access, 6 (2018) 2005–2015.
  46. C. Chen, T. Cheng, Y.S. Shi, Y. Tian, Adsorption of Cu(II) from aqueous solution on fly ash based linde F (K) Zeolite, Iran, J. Chem. Chem. Eng., 33 (2014) 29–35.
  47. C. Chen, T. Cheng, Z.L. Wang, C.H. Han, Removal of Zn2+ in aqueous solution by Linde F (K) zeolite prepared from recycled fly ash, J. Indian Chem. Soc., 91 (2014) 1–7.
  48. F.C. Wu, R.L. Tseng, R.S. Juang, Initial behavior of intraparticle diffusion model used in the description of adsorption kinetics, Chem. Eng. J., 153 (2009) 1–8.
  49. G. Postole, A. Auroux, The poisoning level of Pt/C catalysts used in PEM fuel cells by the hydrogen feed gas impurities: the bonding strength, Int. J. Hydrogen Energy, 36 (2011) 6817–6825.
  50. T. Wang, K.S. Lackner, A.B. Wright, Moisture-swing sorption for carbon dioxide capture from ambient air: a thermodynamic analysis, Phys. Chem. Chem. Phys., 15 (213) 504–512.
  51. T. Cheng, C. Chen, R. Tang, C.H. Han, Y. Tian, Competitive adsorption of Cu, Ni, Pb, and Cd from aqueous solution onto fly ash-based Linde F(K) zeolite, Iran, J. Chem. Chem. Eng., 37 (2018) 61–71.
  52. C. Chen, T. Cheng, X. Zhang, R.X. Wu, Q.Y. Wang, Synthesis of an efficient Pb adsorption nano-crystal under strong alkali hydrothermal environment using a gemini surfactant as directing agent, J. Chem. Soc. Pak., 41 (2019) 1034–1038.
  53. M. Eriksson, I. Lundstro, L. Ekedahl, A model of the Temkin isotherm behavior for hydrogen adsorption at Pd–SiO2 interfaces, J. Appl. Phys., 82 (1997) 3143–3146.
  54. Z. Abdeen, S.G. Mohammad, Study of the adsorption efficiency of an eco-friendly carbohydrate polymer for contaminated aqueous solution by organophosphorus pesticide, Open J. Organ. Polym. Mater., 4 (2014) 16–28.
  55. A.S.A. Khan, Evaluation of thermodynamic parameters of cadmium adsorption on sand from Temkin adsorption isotherm, Turk. J. Chem., 36 (2012) 437–443.
  56. A. Ferguson, The Gibbs free energy of a chemical reaction system as a function of the extent of reaction and the prediction of spontaneity, J. Chem. Educ., 81 (2004) 606–611, doi: 10.1021/ed081p606.2.
  57. A. Kausar, H.N. Bhatti, G. Mackinnon, Equilibrium, kinetic and thermodynamic studies on the removal of U(VI) by low cost agricultural waste, Colloids Surf., B, 111 (2013) 124–133.
  58. L. Dehabadi, A.H. Karoyo, L.D. Wilson, Spectroscopic and thermodynamic study of biopolymer adsorption phenomena in heterogeneous solid–liquid Systems, ACS Omega, 3 (2018) 15370–15379.
  59. A.S. Bhatt, P.L. Sakaria, M. Vasudevan, Adsorption of an anionic dye from aqueous medium by organoclays: equilibrium modeling, kinetic and thermodynamic exploration, RSC Adv., 2 (2012) 8663–8671.
  60. S. Kustov, M.L. Corro, J. Pons, Entropy change and effect of magnetic field on martensitic transformation in a metamagnetic Ni–Co–Mn–In shape memory alloy, Appl. Phys. Lett., 94 (2009) 40–42.
  61. W.C. Zhang, S.T. Zhang, Z. Sun, First-principles study of molecular carbon dioxide adsorption in covalent organic framework-112, Sci. Adv. Mater., 11 (2019) 317–324.
  62. E. Virga, E. Spruijt, W.M.D. Vos, Wettability of amphoteric surfaces: the effect of pH and ionic strength on surface ionization and wetting, Langmuir, 4 (2018) 15174–15180.
  63. Y. Wang, Q. Lu, Dendrimer functionalized nanocrystalline cellulose for Cu(II) removal, Cellulose, 27 (2020) 2173–2187.
  64. X.Q. Pu, Y. Lu, Y. Lin, W.J. Jiang, X. Jiang, Utilization of industrial waste lithium-silicon-powder for the fabrication of novel nap zeolite for aqueous Cu(II) removal, J. Cleaner Prod., 265 (2020) 121822, doi: 10.1016/j.jclepro.2020.121822.
  65. S. Yang, J.C. Huo, Y.B. He, A. Wang, Adsorption kinetics, isotherms, and thermodynamics of Cr(III), Pb(II), and Cu(II) on porous hydroxyapatite nanoparticles, J. Nanosci. Nanotechnol., 18 (2018) 3484–3491.
  66. H. Merrikhpour, M. Jalali, Comparative and competitive adsorption of cadmium, copper, nickel, and lead ions by Iranian natural zeolite, Clean Technol. Environ., 15 (2013) 303–316.
  67. A.A. Mohammed, I.S. Samaka, Bentonite coated with magnetite Fe3O4 nanoparticles as a novel adsorbent for copper(II) ions removal from water/wastewater, Environ. Technol. Innovation, 10 (2018) 162–174.
  68. W.P. Putra, A. Kamari, S.N.M. Yusoff, C.F. Ishak, A. Mohamed, N. Hashim, I.M. Isa, Biosorption of Cu(II), Pb(II) and Zn(II) ions from aqueous solutions using selected waste materials: adsorption and characterisation studies, J. Encapsulation Adsorpt. Sci., 4 (2014) 25–35.
  69. M. Imamoglu, O. Tekir, Removal of copper(II) and lead(II) ions from aqueous solutions by adsorption on activated carbon from a new precursor hazelnut husks, Desalination, 228 (2008) 108–113.