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<p>Textile wastewater, which contains persistent synthetic colours, causes environmental risks due to its toxicity and resistance to biodegradation. This study focused on ozone generation for dye removal utilizing a corona discharge plasma reactor, evaluating the effects of voltage (18.5–21&#xa0;kV), electrode spacing (3–6&#xa0;cm), and oxygen feed rate (0.5–1.5&#xa0;L/min). The experiments used a 20 × 12 × 12 cm3 acrylic reactor with non-coplanar copper electrodes. An XLA-3 analyzer was used to monitor ozone concentrations, and plasma emission durations were examined using ICY image processing software, which is a novel approach. The accuracy of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models in predicting ozone production was evaluated. Higher voltages and closer electrode spacing raised ozone yield to 60.9 ppm at 21&#xa0;kV and 4&#xa0;cm separation. The ANN yielded a significantly higher predictive accuracy than RSM, with R<sup>2</sup> values of 0.962 and 0.7825, respectively. The novel application of digital image processing explains plasma behaviour by relating emission length to ozone generation. These findings improve the efficiency of plasma-based wastewater treatment, providing an effective method for textile dye degradation while also promoting future environmental solutions.</p>

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Modeling ozone generation via non-thermal plasma using response surface methodology (RSM) and artificial neural network (ANN) for textile wastewater application

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Rofiq Iqbal,
  • Budy Handoko,
  • Suprihanto Notodarmojo,
  • Valentinus Galih Vidya Putra

摘要

Textile wastewater, which contains persistent synthetic colours, causes environmental risks due to its toxicity and resistance to biodegradation. This study focused on ozone generation for dye removal utilizing a corona discharge plasma reactor, evaluating the effects of voltage (18.5–21 kV), electrode spacing (3–6 cm), and oxygen feed rate (0.5–1.5 L/min). The experiments used a 20 × 12 × 12 cm3 acrylic reactor with non-coplanar copper electrodes. An XLA-3 analyzer was used to monitor ozone concentrations, and plasma emission durations were examined using ICY image processing software, which is a novel approach. The accuracy of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models in predicting ozone production was evaluated. Higher voltages and closer electrode spacing raised ozone yield to 60.9 ppm at 21 kV and 4 cm separation. The ANN yielded a significantly higher predictive accuracy than RSM, with R2 values of 0.962 and 0.7825, respectively. The novel application of digital image processing explains plasma behaviour by relating emission length to ozone generation. These findings improve the efficiency of plasma-based wastewater treatment, providing an effective method for textile dye degradation while also promoting future environmental solutions.