Calculating the energy consumption of electrocoagulation using a generalized structure group method of data handling integrated with a genetic algorithm and singular value decomposition

  • Hossein BonakdariEmail author
  • Isa Ebtehaj
  • Bahram Gharabaghi
  • Mohsen Vafaeifard
  • Azam Akhbari
Original Paper


In this study, a hybrid data mining method for predicting energy consumption is proposed, namely the group method of data handling integrated with a genetic algorithm and singular value decomposition (GMDH-GA/SVD). As the randomness of renewable sources influences prediction methods, prediction model improvements are necessary for further development. Thus, GMDH-GA/SVD is introduced to model energy consumption as the primary criterion for process evaluation in finding the optimum condition to achieve the least energy consumption process. The parameters include the initial pH, the initial dye concentration, the applied voltage, the initial electrolyte concentration and the treatment time. The uncertainty analysis is applied to survey the quantitative performance of the new proposed model compared to existing popular reduced quadratic multiple regression models and two recently published models in the form of a Taylor diagram, indicating the proposed model is the most accurate. Moreover, partial derivative sensitivity analysis was done on the key parameters in the new model to provide insight into the calibration process of the new model.

Graphical abstract


Energy consumption Sensitivity analysis Biological treatment Formulation 


  1. Abdolrahimi S, Nasernejad B, Pazuki G (2014) Prediction of partition coefficients of alkaloids in ionic liquids based aqueous biphasic systems using hybrid group method of data handling (GMDH) neural network. J Mol Liq 191:79–84CrossRefGoogle Scholar
  2. Aber S, Amani-Ghadim AR, Mirzajani V (2009) Removal of Cr (VI) from polluted solutions by electrocoagulation: modeling of experimental results using artificial neural network. J Hazard Mater 171(1):484–490CrossRefGoogle Scholar
  3. Adhoum N, Monser L, Bellakhal N, Belgaied JE (2004) Treatment of electroplating wastewater containing Cu 2+, Zn 2+ and Cr (VI) by electrocoagulation. J Hazard Mater 112(3):207–213CrossRefGoogle Scholar
  4. Ahlawat R, Srivastava VC, Mall ID, Sinha S (2008) Investigation of the electrocoagulation treatment of cotton blue dye solution using aluminium electrodes. CLEAN–Soil, Air. Water 36(10–11):863–869Google Scholar
  5. Akbal F, Camcı S (2011) Copper, chromium and nickel removal from metal plating wastewater by electrocoagulation. Desalination 269(1):214–222CrossRefGoogle Scholar
  6. Akhbari A, Bonakdari H, Ebtehaj I (2017) Evolutionary prediction of electrocoagulation efficiency and energy consumption probing. Desalin Water Treat 64:54–63CrossRefGoogle Scholar
  7. Aleboyeh A, Daneshvar N, Kasiri MB (2008) Optimization of CI Acid Red 14 azo dye removal by electrocoagulation batch process with response surface methodology. Chem Eng Process 47(5):827–832CrossRefGoogle Scholar
  8. Azadeh A, Narimani A, Nazari T (2014) Estimating household electricity consumption by environmental consciousness. Int J Prod Qual Manage 15(1):1–19Google Scholar
  9. Azimi H, Bonakdari H, Ebtehaj I, Gharabaghi B, Khoshbin F (2018) Evolutionary design of generalized group method of data handling-type neural network for estimating the hydraulic jump roller length. Acta Mech 229(3):1197–1214CrossRefGoogle Scholar
  10. Bhatti MS, Reddy AS, Thukral AK (2009) Electrocoagulation removal of Cr (VI) from simulated wastewater using response surface methodology. J Hazard Mater 172(2):839–846CrossRefGoogle Scholar
  11. Bhatti MS, Reddy AS, Kalia RK, Thukral AK (2011) Modeling and optimization of voltage and treatment time for electrocoagulation removal of hexavalent chromium. Desalination 269(1):157–162CrossRefGoogle Scholar
  12. Bonakdari H, Ebtehaj I, Akhbari A (2017) Multi-objective evolutionary polynomial regression-based prediction of energy consumption probing. Water Sci Technol. CrossRefGoogle Scholar
  13. Chen G (2004) Electrochemical technologies in wastewater treatment. Sep Purif Technol 38(1):11–41CrossRefGoogle Scholar
  14. Chou WL, Wang CT, Huang KY (2010) Investigation of process parameters for the removal of polyvinyl alcohol from aqueous solution by iron electrocoagulation. Desalination 251(1):12–19CrossRefGoogle Scholar
  15. Corchado E, Abraham A, SnášEl V (2013) Editorial: new trends on soft computing models in industrial and environmental applications. Neurocomputing 109:1–2CrossRefGoogle Scholar
  16. Daneshvar N, Khataee AR, Djafarzadeh N (2006) The use of artificial neural networks (ANN) for modeling of decolorization of textile dye solution containing CI Basic Yellow 28 by electrocoagulation process. J Hazard Mater 137(3):1788–1795CrossRefGoogle Scholar
  17. De Giorgi MG, Malvoni M, Congedo PM (2016) Comparison of strategies for multi-step ahead photovoltaic power forecasting models based on hybrid group method of data handling networks and least square support vector machine. Energy 107:360–373CrossRefGoogle Scholar
  18. Do JS, Chen ML (1994) Decolourization of dye-containing solutions by electrocoagulation. J Appl Electrochem 24(8):785–790CrossRefGoogle Scholar
  19. Ebtehaj I, Bonakdari H, Khoshbin F, Azimi H (2015a) Pareto genetic design of GMDH-type neural network for predict discharge coefficient in rectangular side orifices. Flow Meas Instrum 41:67–74CrossRefGoogle Scholar
  20. Ebtehaj I, Bonakdari H, Zaji AH, Azimi H, Khoshbin F (2015b) GMDH-type neural network approach for modeling the discharge coefficient of rectangular sharp-crested side weirs. Eng Sci Technol Int J 18(4):746–757CrossRefGoogle Scholar
  21. Ebtehaj I, Sattar AM, Bonakdari H, Zaji AH (2016) Prediction of scour depth around bridge piers using self-adaptive extreme learning machine. J Hydroinfo 19(2):207–224. CrossRefGoogle Scholar
  22. Ebtehaj I, Bonakdari H, Gharabaghi B (2018) Development of more accurate discharge coefficient prediction equations for rectangular side weirs using adaptive neuro-fuzzy inference system and generalized group method of data handling. Measurement 116:473–482CrossRefGoogle Scholar
  23. El-Ashtoukhy ES, Zewail TM, Amin NK (2010) Removal of heavy metal ions from aqueous solution by electrocoagulation using a horizontal expanded Al anode. Desalin Water Treat 20(1–3):72–79CrossRefGoogle Scholar
  24. Escobar C, Soto-Salazar C, Toral MI (2006) Optimization of the electrocoagulation process for the removal of copper, lead and cadmium in natural waters and simulated wastewater. J Environ Manage 81(4):384–391CrossRefGoogle Scholar
  25. Farlow SJ (1984) Self-organizing methods in modeling: GMDH type algorithms, vol 54. CrC Press, Boca RatonGoogle Scholar
  26. Feng Y, Barr W, Harper WF (2013) Neural network processing of microbial fuel cell signals for the identification of chemicals present in water. J Environ Manage 120:84–92CrossRefGoogle Scholar
  27. Ghanbari F, Moradi M (2015) A comparative study of electrocoagulation, electrochemical Fenton, electro-Fenton and peroxi-coagulation for decolorization of real textile wastewater: electrical energy consumption and biodegradability improvement. J Environ Chem Eng 3(1):499–506CrossRefGoogle Scholar
  28. Ghanbari F, Moradi M, Manshouri M (2014) Textile wastewater decolorization by zero valent iron activated peroxymonosulfate: compared with zero valent copper. J Environ Chem Eng 2(3):1846–1851CrossRefGoogle Scholar
  29. Ghasemiasl R, Hoseinzadeh S, Javadi MA (2017) Numerical analysis of energy storage systems using phase-change materials with nanoparticles. J Thermophys Heat Transf 32(2):440–448CrossRefGoogle Scholar
  30. Ghosh D, Solanki H, Purkait MK (2008) Removal of Fe (II) from tap water by electrocoagulation technique. J Hazard Mater 155(1):135–143CrossRefGoogle Scholar
  31. Giustolisi O, Savic DA (2006) A symbolic data-driven technique based on evolutionary polynomial regression. J Hydroinf 8(3):207–222CrossRefGoogle Scholar
  32. Golub GH, Reinsch C (1970) Singular value decomposition and least squares solutions. Numer Math 14(5):403–420CrossRefGoogle Scholar
  33. Hattab N, Hambli R, Motelica-Heino M, Mench M (2013) Neural network and Monte Carlo simulation approach to investigate variability of copper concentration in phytoremediated contaminated soils. J Environ Manage 129:134–142CrossRefGoogle Scholar
  34. Heidmann I, Calmano W (2008) Removal of Zn (II), Cu (II), Ni (II), Ag (I) and Cr (VI) present in aqueous solutions by aluminium electrocoagulation. J Hazard Mater 152(3):934–941CrossRefGoogle Scholar
  35. Hoseinzadeh S, Sahebi AR, Ghasemiasl R (2017) Effect of Al2O3/water nanofluid on thermosyphon thermal performance. The Eur Phys J Plus 132:197CrossRefGoogle Scholar
  36. Hoseinzadeh S, Ghasemiasl R, Bahari A, Ramezani AH (2018) Effect of post-annealing on the electrochromic properties of layer-by-layer arrangement FTO-WO 3-Ag-WO 3-Ag. J Electron Mater 47(7):3552–3559CrossRefGoogle Scholar
  37. Hunsom M, Pruksathorn K, Damronglerd S, Vergnes H, Duverneuil P (2005) Electrochemical treatment of heavy metals (Cu 2+, Cr 6+, Ni 2+) from industrial effluent and modeling of copper reduction. Water Res 39(4):610–616CrossRefGoogle Scholar
  38. Ikeda S, Ochiai M, Sawaragi Y (1976) Sequential GMDH algorithm and its application to river flow prediction. IEEE Trans Syst Man Cybern 7:473–479CrossRefGoogle Scholar
  39. Inan H, Dimoglo A, Şimsek F, Karpuzcu M (2004) Olive oil mill wastewater treatment by means of electro-coagulation. Sep Purif Technol 36:23–31CrossRefGoogle Scholar
  40. Ivakhnenko AG (1971) Polynomial theory of complex systems. IEEE Trans Syst Man Cybern 4:364–378CrossRefGoogle Scholar
  41. Kabdaşlı I, Vardar B, Arslan-Alaton I, Tünay O (2009) Effect of dye auxiliaries on color and COD removal from simulated reactive dyebath effluent by electrocoagulation. Chem Eng J 148(1):89–96CrossRefGoogle Scholar
  42. Kalyani KP, Balasubramanian N, Srinivasakannan C (2009) Decolorization and COD reduction of paper industrial effluent using electro-coagulation. Chem Eng J 151(1):97–104CrossRefGoogle Scholar
  43. Katal R, Pahlavanzadeh H (2011) Influence of different combinations of aluminum and iron electrode on electrocoagulation efficiency: application to the treatment of paper mill wastewater. Desalination 265(1):199–205CrossRefGoogle Scholar
  44. Körbahti BK, Artut K (2010) Electrochemical oil/water demulsification and purification of bilge water using Pt/Ir electrodes. Desalination 258(1):219–228CrossRefGoogle Scholar
  45. Körbahti BK, Tanyolaç A (2008) Electrochemical treatment of simulated textile wastewater with industrial components and Levafix Blue CA reactive dye: optimization through response surface methodology. J Hazard Mater 151(2):422–431CrossRefGoogle Scholar
  46. Lakshmanan D, Clifford DA, Samanta G (2010) Comparative study of arsenic removal by iron using electrocoagulation and chemical coagulation. Water Res 44(19):5641–5652CrossRefGoogle Scholar
  47. Malakootian M, Mansoorian HJ, Moosazadeh M (2010) Performance evaluation of electrocoagulation process using iron-rod electrodes for removing hardness from drinking water. Desalination 255(1):67–71CrossRefGoogle Scholar
  48. Maleki A, Daraei H, Shahmoradi B, Razee S, Ghobadi N (2014) Electrocoagulation efficiency and energy consumption probing by artificial intelligent approaches. Desalin Water Treat 52(13–15):2400–2411CrossRefGoogle Scholar
  49. Malhotra R, Chug A (2014) Application of group method of data handling model for software maintainability prediction using object oriented systems. Int J Syst Assur Eng Manage 5(2):165–173CrossRefGoogle Scholar
  50. Merzouka B, Gourichb B, Sekki A, Madani K, Vial Ch, Barkaoui M (2009) Studies on the decolorization of textile dye wastewater by continuous electrocoagulation process. Chem Eng J 149:207–214CrossRefGoogle Scholar
  51. Mohapatra S, Dandapat SJ, Thatoi H (2017) Physicochemical characterization, modelling and optimization of ultrasono-assisted acid pretreatment of two Pennisetum sp. using Taguchi and artificial neural networking for enhanced delignification. J Environ Manage 187:537–549CrossRefGoogle Scholar
  52. Mollah MY, Morkovsky P, Gomes JA, Kesmez M, Parga J, Cocke DL (2004) Fundamentals, present and future perspectives of electrocoagulation. J Hazard Mater 114(1):199–210CrossRefGoogle Scholar
  53. Moussa DT, El-Naas MH, Nasser M, Al-Marri MJ (2017) A comprehensive review of electrocoagulation for water treatment: potentials and challenges. J Environ Manage 186:24–41CrossRefGoogle Scholar
  54. Ölmez T (2009) The optimization of Cr (VI) reduction and removal by electrocoagulation using response surface methodology. J Hazard Mater 162(2):1371–1378CrossRefGoogle Scholar
  55. Ramezani AH, Hoseinzadeh S, Bahari A (2018) The effects of nitrogen on structure, morphology and electrical resistance of tantalum by ion implantation method. J Inorg Organomet P 28(3):847–853CrossRefGoogle Scholar
  56. Sayiner G, Kandemirli F, Dimoglo A (2008) Evaluation of boron removal by electrocoagulation using iron and aluminum electrodes. Desalination 230(1):205–212CrossRefGoogle Scholar
  57. Sefeedpari P, Rafiee S, Akram A, Komleh SHP (2014) Modeling output energy based on fossil fuels and electricity energy consumption on dairy farms of Iran: application of adaptive neural-fuzzy inference system technique. Comput Electron Agr 109:80–85CrossRefGoogle Scholar
  58. Taheri M, Moghaddam MA, Arami M (2013) Techno-economical optimization of Reactive Blue 19 removal by combined electrocoagulation/coagulation process through MOPSO using RSM and ANFIS models. J Environ Manage 128:798–806CrossRefGoogle Scholar
  59. Tak BY, Tak BS, Kim YJ, Park YJ, Yoon YH, Min GH (2015) Optimization of color and COD removal from livestock wastewater by electrocoagulation process: application of Box-Behnken design (BBD). J Ind Eng Chem 28:307–315CrossRefGoogle Scholar
  60. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106(7):7183–7192CrossRefGoogle Scholar
  61. Thella K, Verma B, Srivastava VC, Srivastava KK (2008) Electrocoagulation study for the removal of arsenic and chromium from aqueous solution. J Environ Sci Health, Part A 43(5):554–562CrossRefGoogle Scholar
  62. Vafaeifard M, Lee G, Akib S, Ibrahim S, Yoon Y, Jang M (2016) Facile and economic one-pot synthesis of rigid functional-polyurethane for the effective treatment of heavy metal-contaminated urban storm water run-off. Desalin Water Treat 57:26114–26129CrossRefGoogle Scholar
  63. Wan W, Pepping TJ, Banerji T, Chaudhari S, Giammar DE (2011) Effects of water chemistry on arsenic removal from drinking water by electrocoagulation. Water Res 45(1):384–392CrossRefGoogle Scholar
  64. Yari A, Hosseinzadeh S, Golneshan AA, Ghasemiasl R (2017) Numerical simulation for thermal design of a gas water heater with turbulent combined convection. ASME PS Appl CFD, ASMEGoogle Scholar
  65. Yılmaz AE, Boncukcuoğlu R, Kocaker MM, Kocadağistan E (2008) An empirical model for kinetics of boron removal from boroncontaining wastewaters by the electrocoagulation method in a batch reactor. Desalination 230(1):288–297CrossRefGoogle Scholar
  66. Yousef Nezhad ME, Hoseinzadeh S (2017a) Mathematical simulation and optimization of a solar water heater for an aviculture unit using MATLAB/SIMULINK. J Renew Sustain Energy 9(6):10. 063702CrossRefGoogle Scholar
  67. Yousef Nezhad ME, Hoseinzadeh S (2017b) Simulation and optimization of a solar-assisted heating and cooling system for a house in Northern of Iran. J Renew Sustain Energy 9(4):045101–045113CrossRefGoogle Scholar
  68. Zaroual Z, Chaair H, Essadki AH, El Ass K, Azzi M (2009) Optimizing the removal of trivalent chromium by electrocoagulation using experimental design. Chem Eng J 148(2):488–495CrossRefGoogle Scholar
  69. Zodi S, Potier O, Lapicque F, Leclerc JP (2010) Treatment of the industrial wastewaters by electrocoagulation: optimization of coupled electrochemical and sedimentation processes. Desalination 261(1):186–190CrossRefGoogle Scholar
  70. Zongo I, Maiga AH, Wéthé J, Valentin G, Leclerc JP, Paternotte G, Lapicque F (2009) Electrocoagulation for the treatment of textile wastewaters with Al or Fe electrodes: compared variations of COD levels, turbidity and absorbance. J Hazard Mater 169(1):70–76CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringRazi UniversityKermanshahIran
  2. 2.Enviromental Research CenterRazi UniversityKermanshahIran
  3. 3.School of EngineeringUniversity of GuelphGuelphCanada
  4. 4.Department of Civil Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia

Personalised recommendations