Identifying the leakage source in industrial factories as soon as an incident happens is crucial for controlling the leak in time and safeguarding lives and property. This study developed a rapid source identification method for factories, aiming to identify the characteristics of leakage sources at the early stage of the leakage event. The approach integrates pollutant diffusion theory, sliding window method, correlation coefficient method, and data fitting method. Based on an experimental cabin, 162 verification conditions were established, encompassing constant sources as well as two time-varying sources: linear and non-linear ones. Additionally, various aspects of the method were evaluated. It was found that considering the error and noise in sensor monitoring data, the method exhibits identification accuracies of 83.3%, 79.6%, and 74.1% for constant sources, linear sources, and non-linear sources, respectively. In the case of non-linear sources, the coefficient “b” must be taken into account when generating the dimensionless concentration vector. A sliding window containing 50 to 60 valid data points can achieve the highest source identification accuracy. This research can provide help in quickly locating leakage sources in factories, help with the timely control of leaks, and protect workers’ health and property safety.

中文翻译：

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工厂室内泄漏源的早期识别


在事故发生时，立即确定工业工厂的泄漏源，对于及时控制泄漏和保护生命财产安全至关重要。本研究开发了一种工厂快速源识别方法，旨在识别泄漏事件早期的泄漏源特征。该方法融合了污染物扩散理论、滑窗法、相关系数法和数据拟合法。基于实验舱，建立了 162 个验证条件，包括常数源以及两个时变源：线性和非线性源。此外，还评估了该方法的各个方面。研究发现，考虑到传感器监测数据中的误差和噪声，该方法对常数源、线性源和非线性源的识别准确率分别为 83.3%、79.6% 和 74.1%。在非线性源的情况下，在生成无量纲浓度矢量时必须考虑系数 “b”。包含 50 到 60 个有效数据点的滑动窗口可以实现最高的源识别精度。这项研究可以帮助快速定位工厂的泄漏源，有助于及时控制泄漏，保护工人的健康和财产安全。