Artificial Neural Network Modeling of The Removal of Cr (VI) on by Polymeric Calix[6]arene in Aqueous Solutions

The artificial neural network-based model was developed to predict the sorption capacity and removal efficiency of calixarene for Cr(VI) in aqueous solutions. The input variables were initial concentration of Cr(VI), adsorbent dosage, contact time, and pH, while the sorption capacity and the removal efficiency were considered as output. They have been used for the training and simulation of the network in the current work. The training results were tested using the input data (simulated data) that were not shown to the network. According to the indicator, the optimum and most reliable model was found based on the test results


Aber, S., Amani-Ghadim, A., & Mirzajani, V. (2009). Removal of Cr (VI) from polluted solutions by electrocoagulation: Modeling of experimental results using artificial neural network. Journal of hazardous materials, 171(1-3), 484-490.

Amani-Ghadim, A., & Dorraji, M. S. (2015). Modeling of photocatalyatic process on synthesized ZnO nanoparticles: Kinetic model development and artificial neural networks. Applied Catalysis B: Environmental, 163, 539-546.

Asfari, M.-Z., Böhmer, V., Harrowfield, J., & Vicens, J. (2007). Calixarenes 2001: Springer Science & Business Media.

Azarpour, A., Alwi, S. R. W., Zahedi, G., Madooli, M., & Millar, G. J. (2015). Catalytic activity evaluation of industrial Pd/C catalyst via gray-box dynamic modeling and simulation of hydropurification reactor. Applied Catalysis A: General, 489, 262-271.

Babaei, A. A., Khataee, A., Ahmadpour, E., Sheydaei, M., Kakavandi, B., & Alaee, Z. (2016). Optimization of cationic dye adsorption on activated spent tea: equilibrium, kinetics, thermodynamic and artificial neural network modeling. Korean journal of chemical engineering, 33(4), 1352-1361.

Bounar, N., Boulkroune, A., Boudjema, F., & Farza, M. (2015). Adaptive fuzzy vector control for a doubly-fed induction motor. Neurocomputing, 151, 756-769.

Chairez, I., García-Peña, I., & Cabrera, A. (2009). Dynamic numerical reconstruction of a fungal biofiltration system using differential neural network. Journal of Process Control, 19(7), 1103-1110.

Dutta, S., Parsons, S. A., Bhattacharjee, C., Bandhyopadhyay, S., & Datta, S. (2010). Development of an artificial neural network model for adsorption and photocatalysis of reactive dye on TiO2 surface. Expert Systems with Applications, 37(12), 8634-8638.

Fu, F., & Wang, Q. (2011). Removal of heavy metal ions from wastewaters: a review. Journal of environmental management, 92(3), 407-418.

Fu, R.-Q., Xu, T.-W., & Pan, Z.-X. (2005). Modelling of the adsorption of bovine serum albumin on porous polyethylene membrane by back-propagation artificial neural network. Journal of membrane science, 251(1-2), 137-144.

Gao, H., Liang, L., Chen, X., & Xu, G. (2015). Feature extraction and recognition for rolling element bearing fault utilizing shorttime Fourier transform and non-negative matrix factorization. Chinese Journal of Mechanical Engineering, 28(1), 96-105.

Ghaedi, A., Ghaedi, M., & Karami, P. (2015). Comparison of ultrasonic with stirrer performance for removal of sunset yellow (SY) by activated carbon prepared from wood of orange tree: Artificial neural network modeling. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 138, 789-799.

Gutsche, C. D. (2008). Calixarenes: an introduction: Royal Society of Chemistry. Halder, G., Dhawane, S., Barai, P. K., & Das, A. (2015). Optimizing chromium (VI) adsorption onto superheated steam activated granular carbon through response surface methodology and artificial neural network. Environmental Progress & Sustainable Energy, 34(3), 638-647.

Heaton, J. (2008). Introduction to neural networks with Java: Heaton Research, Inc. Heibati, B., Rodriguez-Couto, S., Ozgonenel, O., Turan, N. G., Aluigi, A., Zazouli, M. A., . . . Albadarin, A. B. (2016). A modeling study by artificial neural network on ethidium bromide adsorption optimization using natural pumice and iron-coated pumice. Desalination and Water Treatment, 57(29), 13472-13483.

Kang, H. T., & Yoon, C. J. (1994). Neural network approaches to aid simple truss design problems. Computer‐Aided Civil and Infrastructure Engineering, 9(3), 211-218.

Khandanlou, R., Masoumi, H. R. F., Ahmad, M. B., Shameli, K., Basri, M., & Kalantari, K. (2016). Enhancement of heavy metals sorption via nanocomposites of rice straw and Fe3O4 nanoparticles using artificial neural network (ANN). Ecological Engineering, 91, 249-256.

Kooh, M. R. R., Dahri, M. K., Lim, L. B., Lim, L. H., & Malik, O. A. (2016). Batch adsorption studies of the removal of methyl violet 2B by soya bean waste: isotherm, kinetics and artificial neural network modelling. Environmental Earth Sciences, 75(9), 783. Köhler, T., Bock, R., Hornegger, J., & Michelson, G. (2015). Computer-aided diagnostics and pattern recognition: Automated glaucoma detection Teleophthalmology in Preventive Medicine (pp. 93-104): Springer.

Lertworasirikul, S., & Tipsuwan, Y. (2008). Moisture content and water activity prediction of semi-finished cassava crackers from drying process with artificial neural network. Journal of food Engineering, 84(1), 65-74.

Li, W., Zhu, Z., Jiang, F., Zhou, G., & Chen, G. (2015). Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method. Mechanical Systems and Signal Processing, 50, 414-426.

Mahmoodi, N. M., Hosseinabadi-Farahani, Z., Bagherpour, F., Khoshrou, M. R., Chamani, H., & Forouzeshfar, F. (2016). Synthesis of CuO–NiO nanocomposite and dye adsorption modeling using artificial neural network. Desalination and Water Treatment, 57(37), 17220-17229.

Mahmoodi, N. M., Hosseinabadi‐Farahani, Z., & Chamani, H. (2017). Dye adsorption from single and binary systems using NiO‐ MnO2 nanocomposite and artificial neural network modeling. Environmental Progress & Sustainable Energy, 36(1), 111-119.

McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.

Nayak, P. C., Rao, Y. S., & Sudheer, K. (2006). Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water resources management, 20(1), 77-90.

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985). Learning internal representations by error propagation. Retrieved from Sato, A., Sha, Z., & Palosaari, S. (1999). Neural networks for chemical engineering unit operations. Chemical Engineering & Technology: Industrial Chemistry‐Plant Equipment‐Process Engineering‐Biotechnology, 22(9), 732-739.

Self, J. (1988). Artificial intelligence and human learning: intelligent computer-aided instruction: Chapman and Hall London. Sengupta, A. K., & Clifford, D. (1986). Important process variables in chromate ion exchange. Environmental science & technology, 20(2), 149-155.

Tabakci, M. (2008). Immobilization of calix [6] arene bearing carboxylic acid and amide groups on aminopropyl silica gel and its sorption properties for Cr (VI). Journal of Inclusion Phenomena and Macrocyclic Chemistry, 61(1-2), 53-60.

Todoran, R., Todoran, D., & Szakacs, Z. (2016). Optical luminescence studies of the ethyl xanthate adsorption layer on the surface of sphalerite minerals. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 152, 591-595.

Tomczak, E. (2011). Application of ANN and EA for description of metal ions sorption on chitosan foamed structure—Equilibrium and dynamics of packed column. Computers & chemical engineering, 35(2), 226-235.

Voyant, C., Nivet, M.-L., Paoli, C., Muselli, M., & Notton, G. (2015). Heterogeneous transfer functions multi-layer perceptron (MLP) for meteorological time series forecasting. International Journal of Modeling, Simulation, and Scientific Computing, 6(02), 1550013.

Yurtsever, U., Yurtsever, M., Şengil, İ. A., & Kıratlı Yılmazçoban, N. (2015). Fast artificial neural network (FANN) modeling of Cd (II) ions removal by valonia resin. Desalination and Water Treatment, 56(1), 83-96.

Zhao, D., SenGupta, A. K., & Stewart, L. (1998). Selective removal of Cr (VI) oxyanions with a new anion exchanger. Industrial & engineering chemistry research, 37(11), 4383-4387.

Kaynak Göster

Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi
  • ISSN: 1308-5514
  • Yayın Aralığı: Yılda 2 Sayı
  • Başlangıç: 2009