Modeling of Removal of Chromium (VI) from Aqueous Solutions Using Artificial Neural Network

Document Type : Research Article


1 Department of Computer Engineering, Necmettin Erbakan University, Konya, TURKEY

2 Department of Chemical Engineering, Selcuk University, Konya, TURKEY


There is a need for knowledge, experience, laboratory, materials, and time to conduct chemical experiments. The results depend on the process and are also quite costly. For economic and rapid results, chemical processes can be modeled by utilizing data obtained in the past. In this paper, an artificial neural network model is proposed for predicting the removal efficiency of Cr (VI) from aqueous solutions with Amberlite IRA-96 resin, as being independent of chemical processes. Multiple linear regression, linear and quadratic particle swarm optimization are also used to compare prediction success. A total of 34 experimental data were used for training and validation of the model. pH, amount of resin, contact time, and concentration were used as input data. The removal efficiency is considered as output data for each model. The statistical methods of root-mean-square error, mean absolute percentage error, variance absolute relative error and the coefficient of determination were used to evaluate the performance of the developed models. The system has been analyzed using a feature selection method to assess the influence of input parameters on the sorption efficiency. The most significant factor was found in pH. The obtained results show that the proposed ANN model is more reliable than the other models for estimating removal efficiency. 


Main Subjects

[1] Kişi Ö., River Flow Modeling Using Artificial Neural Networks, Journal of Hydrologic Engineering, 9: 60-63 (2004).
[2] Tokar A., Markus M., Precipitation-Runoff Modeling Using Artificial Neural Networks and Conceptual Models, Journal of Hydrologic Engineering, 5: 156-161 (2000).
[3] Rajurkar M., Kothyari U., Chaube U., Modeling of the Daily Rainfall-Runoff Relationship with Artificial Neural Network, Journal of Hydrology, 285: 96-113 (2004).
[4] Movagharnejad K., Nikzad M., Modeling of Tomato Drying Using Artificial Neural Network, Computers and electronics in agriculture, 59: 78-85 (2007).
[5] Dawson C., Wilby R., Hydrological Modelling Using Artificial Neural Networks, Progress in Physical Geography, 25: 80-108 (2001).
[6] Esfe M.H., Saedodin S., Bahiraei M., Toghraie D., Mahian O., Wongwises S., Thermal Conductivity Modeling of MgO/EG Nanofluids Using Experimental Data and Artificial Neural Network, Journal of Thermal Analysis and Calorimetry, 118: 287-294 (2014).
[7] Esfe M.H., Wongwises S., Naderi A., Asadi A., Safaei M.R., Rostamian H., Dahari M., Karimipour A., Thermal Conductivity of Cu/TiO 2–Water/EG Hybrid Nanofluid: Experimental Data and Modeling Using Artificial Neural Network and Correlation, International Communications in Heat and Mass Transfer, 66: 100-104 (2015).
[8] Witek-Krowiak A., Chojnacka K., Podstawczyk D., Dawiec A., Pokomeda K., Application of Response Surface Methodology and Artificial Neural Network Methods in Modelling and Optimization of Biosorption Process, Bioresource Technology, 160: 150-160  (2014).
[10] Williams D., Modeling Business Failure among SMEs: An Artificial Neural Networks and Logistic Regression Analysis, United States Association for Small Business and Entrepreneurship. Conference Proceedings, 28(2):21-27 (2016).
[11] Ghaedi M., Ghaedi A., Ansari A., Mohammadi F., A Vafaei., Artificial Neural Network and Particle Swarm Optimization for Removal of Methyl Orange by Gold Nanoparticles Loaded on Activated Carbon and Tamarisk, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 132: 639-654(2014).
[12] McCulloch W.S., Pitts W., A Logical Calculus of the Ideas Immanent in Nervous Activity, The Bulletin of Mathematical Biophysics, 5: 115-133 (1943).
[14] Fanaie V.R., Karrabi M., Amin M.M., Shahnavaz B., Fatehizadeh A., Application of Response Surface Methodology and Artificial Neural Network for Analysis of p-Chlorophenol Biosorption by Dried Activated Sludge, Journal of Applied Chemical Research, 10: 25-37 (2016).
[15] Babaei A.A., Khataee A., Ahmadpour E., Sheydaei M., Kakavandi B., Alaee Z., Optimization of Cationic Dye Adsorption on Activated Spent Tea: Equilibrium, Kinetics, Thermodynamic and Artificial Neural Network Modeling, Korean Journal of Chemical Engineering, 33: 1352-1361 (2016).
[16] Alguacil F.J., Coedo A.G., Dorado T., Padilla I., Recovery of Chromium (VI) from Hydrochloric Acid Liquors Using the Resin Dowex 1x8, Journal of Chemical Research, 2002(3): 101-104 (2002).
[17] Wu Y., Ma X., Feng M., Liu M., Behavior of Chromium and Arsenic on Activated Carbon, Journal of Hazardous Materials, 159(2-3): 380-384 (2008).
[18] Lin S., Kiang C., Chromic Acid Recovery from Waste Acid Solution by an Ion Exchange Process: Equilibrium and Column Ion Exchange Modeling, Chemical Engineering Journal, 92(1-3): 193-199 (2003).
[19] Kocaoba S., Akcin G., Removal of Chromium (III) and Cadmium (II) from Aqueous Solutions, Desalination, 180(1-3): 151-156 (2005).
[20] Bai R.S., Abraham T.E., Studies on Chromium (VI) Adsorption–Desorption Using Immobilized Fungal Biomass, Bioresource Technology, 87(1): 17-26 (2003).
[21] Korngold E., Belayev N., Aronov L., Removal of Chromates from Drinking Water by Anion Exchangers, Separation and Purification Technology, 33(2): 179-187 (2003).
[22] Khezami L., Capart R., Removal of Chromium (VI) from Aqueous Solution by Activated Carbons: Kinetic and Equilibrium Studies, Journal of Hazardous Materials, 123(1-3): 223-231 (2005).
[25] Edebali S., Pehlivan E., Evaluation of Amberlite IRA96 and Dowex 1× 8 Ion-Exchange Resins for the Removal of Cr (VI) from Aqueous Solution, Chemical Engineering Journal, 161(1-2): 161-166 (2010).
[26] Abdul-Wahab S.A., Bakheit C.S., Al-Alawi S.M., Principal Component and Multiple Regression Analysis in Modelling of Ground-Level Ozone and Factors Affecting Its Concentrations, Environmental Modelling & Software, 20(10): 1263-1271 (2005).
[27] S Al-Alawi.M., S. Abdul-Wahab A., Bakheit C.S., Combining Principal Component Regression and Artificial Neural Networks for More Accurate Predictions of Ground-Level Ozone, Environmental Modelling & Software, 23(4): 396-403 (2008).
[28] Uyak V., Ozdemir K., Toroz I., Multiple Linear Regression Modeling of Disinfection by-Products Formation in Istanbul Drinking Water Reservoirs, Science of the Total Environment, 378(3): 269-280 (2007).
[29] Özbay B., Keskin G.A., Doğruparmak Ş.Ç., Ayberk S., Multivariate Methods for Ground-Level Ozone Modeling, Atmospheric Research, 102(1-2): 57-65 (2011).
[30] Kennedy J., Eberhart R., Particle Swarm Optimization, in:  IEEE International Conference on Neural Networks, 1942-1948 (1995).
[32] Gordan B., Armaghani D.J., Hajihassani M., Monjezi M., Prediction of Seismic Slope Stability Through Combination of Particle Swarm Optimization and Neural Network, Engineering with Computers, 32(1): 85-97 (2016).
[33] Mutluer M., Bilgin O., Design Optimization of PMSM by Particle Swarm Optimization and Genetic Algorithm, in:  Innovations in Intelligent Systems and Applications (INISTA), 2012 International Symposium on, IEEE, 2012: 1-4 (2012).
[34] Van Den Bergh F., An Analysis of Particle Swarm Optimizers, in, University of Pretoria, (2007).
[35] Shi Y., Eberhart R., A Modified Particle Swarm Optimizer, in: Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, IEEE, pp. 69-73 (1998).
[36] Xin, J., Chen, G., Hai, Y., A Particle Swarm Optimizer with Multi-Stage Linearly-Decreasing Inertia Weight, In Computational Sciences and Optimization, CSO 2009. International Joint Conference on 1: 505-508). IEEE (2009).
[37] Rezaee Jordehi, A., Jasni, J., Parameter Selection in Particle swarm Optimisation: A Survey, Journal of Experimental & Theoretical Artificial Intelligence, 25(4): 527-542 (2013).
[38] Chen S., Montgomery J., Bolufé-Röhler A., Measuring the Curse of Dimensionality and Its Effects on Particle Swarm Optimization and Differential Evolution, Applied Intelligence, 42(3): 514-526 (2015).
[40] Singh T.N., Singh V.K., Sinha S., Prediction of Cadmium Removal Using an Artificial Neural Network and a Neuro-Fuzzy Technique, Mine Water and the Environment, 25(4): 214-219 (2006).
[41] Arora J.K., Srivastava S., Neural Network Modeling and Simulation of Sorption of Cd (II) Ions from Waste Water using Agricultural Waste, In Proceedings of the World Congress on Engineering, 3: 1-4 (2010).
[42] Yurtsever,U., Yurtsever M., Şengil İ.A., Kıratlı Yılmazçoban N., Fast Artificial Neural Network (FANN) Modeling of Cd (II) Ions Removal by Valonia Resin, Desalination and Water Treatment, 56(1): 83-96 (2015).
[43] Kardam A., Raj, K.R., Arora J.K., Srivastava S., Simulation and Optimization of Artificial Neural Network Modeling for Prediction of Sorption Efficiency of Nanocellulose Fibers for Removal of Cd (II) Ions from Aqueous System, Walailak Journal of Science and Technology (WJST), 11(6): 497-508 (2013).
[44] Sarala Thambavani D., Kavitha B., Prediction and Simulation of Chromium (VI) Ions Removal Efficiency by Riverbed Sand Adsorbent Using Artificial Neural Networks, Internatıonal Journal Of Engıneerıng Scıences & Research Technology, 3(5): 906-913 2014.
[45] Kardam A., Raj K.R., Arora J.K., Srivastava, M.M., Srivastava S., Artificial Neural Network Modeling for Sorption of Cadmium from Aqueous System by Shelled Moringa Oleifera Seed Powder as an Agricultural Waste, Journal of Water Resource and Protection, 2(04): 339 (2010).