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<p>The identification of gas mixtures is critical in chemical engineering, food safety, environmental science, and medical applications. This study employs a four-sensor array to classify ethylene-methane and ethylene-carbon monoxide (CO) mixtures, with concentrations ranging from 0 to 20 ppm for ethylene, 0–600 ppm for CO, and 0–300 ppm for methane. To enhance robustness, mean sensor response over time is used to mitigate noise, ensuring high classification accuracy. Each sample is evaluated using sixteen features, including temporal dynamics (e.g., rise time) and statistical metrics (e.g., baseline variance). Tree-based machine learning models—Decision Tree (DT), Random Forest (RF), and Extra Trees (ET)—are developed for gas classification, significantly reducing training set size (to 60%) and prediction time. The proposed ET model achieves superior classification accuracy (99.15%) compared to RF (95.86%) and DT (93.89%) on the UCI dataset, offering an efficient and accurate method for experimental gas identification.</p>

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Fast and robust mixed gas identification and recognition using tree-based machine learning and sensor array response

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月3日

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  • Ghazala Ansari,
  • Rupali Singh,
  • Sachin Kumar,
  • Naglaa F. Soliman

摘要

The identification of gas mixtures is critical in chemical engineering, food safety, environmental science, and medical applications. This study employs a four-sensor array to classify ethylene-methane and ethylene-carbon monoxide (CO) mixtures, with concentrations ranging from 0 to 20 ppm for ethylene, 0–600 ppm for CO, and 0–300 ppm for methane. To enhance robustness, mean sensor response over time is used to mitigate noise, ensuring high classification accuracy. Each sample is evaluated using sixteen features, including temporal dynamics (e.g., rise time) and statistical metrics (e.g., baseline variance). Tree-based machine learning models—Decision Tree (DT), Random Forest (RF), and Extra Trees (ET)—are developed for gas classification, significantly reducing training set size (to 60%) and prediction time. The proposed ET model achieves superior classification accuracy (99.15%) compared to RF (95.86%) and DT (93.89%) on the UCI dataset, offering an efficient and accurate method for experimental gas identification.