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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 Bonakdari
  • Isa Ebtehaj
  • Bahram Gharabaghi
  • Mohsen Vafaeifard
  • Azam Akhbari
Original Paper
  • 10 Downloads

Abstract

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

Keywords

Energy consumption Sensitivity analysis Biological treatment Formulation 

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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

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