Gas concentration detection is a vital aspect of environmental monitoring, industrial safety, and various other applications. Metal Oxide (MOX) sensors have gained considerable attention in this context due to their low cost and ability to detect several gases, although their selectivity is a potential drawback. In this paper, a data-driven approach for the detection and estimation of ethanol concentration using MOX sensors in the presence of interfering gases is presented, with a dual emphasis on the impact of delay between gas transmission and sensor detection and the incorporation of heater current and voltage, alongside sensor current readings. A delay in sensor response can lead to erroneous readings and potentially compromise safety and environmental assessments. To tackle this issue, an incremental method for the identification of the delay length is proposed, and its effectiveness in improving the estimations of gas concentration is demonstrated by implementing three distinct regression techniques: Linear Regression (LR), Support Vector Regression (SVR), and Random Forest (RF). The analysis is then extended to incorporate the utilization of heater current and voltage, alongside sensor current readings. The experimental results demonstrate the effectiveness of the proposed method in handling measurement delay processing and its robustness to the presence of interfering gases.

中文翻译：

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在存在干扰气体和测量延迟的情况下利用人工智能估算乙醇浓度的方法


气体浓度检测是环境监测、工业安全和各种其他应用的重要方面。金属氧化物 (MOX) 传感器由于其成本低廉且能够检测多种气体，因此在这方面获得了相当多的关注，尽管其选择性是一个潜在的缺点。本文提出了一种在存在干扰气体的情况下使用 MOX 传感器检测和估计乙醇浓度的数据驱动方法，重点关注气体传输和传感器检测之间的延迟以及加热器电流的影响和电压，以及传感器电流读数。传感器响应延迟可能导致读数错误，并可能影响安全和环境评估。为了解决这个问题，提出了一种用于识别延迟长度的增量方法，并通过实施三种不同的回归技术证明了其在改进气体浓度估计方面的有效性：线性回归（LR）、支持向量回归（SVR）、和随机森林（RF）。然后将分析扩展到结合加热器电流和电压以及传感器电流读数的利用。实验结果证明了该方法在处理测量延迟方面的有效性及其对干扰气体存在的鲁棒性。