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<p>A study on electrocoagulation (EC) was conducted to remove boron from boron-containing wastewater using two different electrodes: aluminum (Al) and stainless steel (SS). The process was tested at current densities of 3, 5, and 7&#xa0;mA/cm<sup>2</sup>, at pH levels of 4, 7, and 9, for 120&#xa0;min treatment. Three EC systems with different anode–cathode configurations: Al–Al, SS–SS, and a combined Al–Al + SS–SS setup were compared to assess their effectiveness on boron removal. The best results, with an 87% boron removal rate, were achieved at a current density of 7&#xa0;mA/cm<sup>2</sup>, and pH 7 in the Al–Al + SS–SS system for 120&#xa0;min. Artificial neural network (ANN) was employed to model the experimental data for the three different EC systems, resulted in different ANN structures: 3-9-1 (Al–Al system), 3-9-1 (SS–SS system), and 3-7-1 (Al–Al + SS–SS system), corresponding to numbers of neuron in input layer, hidden layer, and output layer, respectively. The mean squared error (MSE) and correlation coefficient (<i>R</i>) values were 68.4 and 0.989 for Al–Al system, 15.2 and 0.992 for SS–SS system, and 10.0 and 0.992 for Al–Al + SS–SS system, demonstrating good model performance in identifying optimal conditions. Boron adsorption during EC process followed pseudo-second-order kinetic (with <i>R</i><sup>2</sup> values of 0.985, 0.940, and 0.875 for Al–Al, SS–SS, and Al–Al + SS–SS systems, respectively) and Langmuir isotherm (with <i>R</i><sup>2</sup> values of 0.989, 0.997, 0.968 for Al–Al, SS–SS, and Al–Al + SS–SS systems, respectively). This study confirms that EC is an effective method for boron removal.</p>

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Operational Parameters Affecting the Electrocoagulation Efficiency in Boron Removal from Boron-Containing Wastewater: A Predictive Model Using Artificial Neural Networks and Adsorption Study

  • 期刊论文

  • 发表日期：

    2025年8月22日

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  • Maulana Yusup Rosadi,
  • Reni Desmiarti,
  • Nofri Naldi,
  • Fusheng Li,
  • Nofrizon Rahman,
  • Lisa Aprilia

摘要

A study on electrocoagulation (EC) was conducted to remove boron from boron-containing wastewater using two different electrodes: aluminum (Al) and stainless steel (SS). The process was tested at current densities of 3, 5, and 7 mA/cm2, at pH levels of 4, 7, and 9, for 120 min treatment. Three EC systems with different anode–cathode configurations: Al–Al, SS–SS, and a combined Al–Al + SS–SS setup were compared to assess their effectiveness on boron removal. The best results, with an 87% boron removal rate, were achieved at a current density of 7 mA/cm2, and pH 7 in the Al–Al + SS–SS system for 120 min. Artificial neural network (ANN) was employed to model the experimental data for the three different EC systems, resulted in different ANN structures: 3-9-1 (Al–Al system), 3-9-1 (SS–SS system), and 3-7-1 (Al–Al + SS–SS system), corresponding to numbers of neuron in input layer, hidden layer, and output layer, respectively. The mean squared error (MSE) and correlation coefficient (R) values were 68.4 and 0.989 for Al–Al system, 15.2 and 0.992 for SS–SS system, and 10.0 and 0.992 for Al–Al + SS–SS system, demonstrating good model performance in identifying optimal conditions. Boron adsorption during EC process followed pseudo-second-order kinetic (with R2 values of 0.985, 0.940, and 0.875 for Al–Al, SS–SS, and Al–Al + SS–SS systems, respectively) and Langmuir isotherm (with R2 values of 0.989, 0.997, 0.968 for Al–Al, SS–SS, and Al–Al + SS–SS systems, respectively). This study confirms that EC is an effective method for boron removal.