Data-driven source term estimation of hazardous gas leakages under variable meteorological conditions

Abstract

Source term estimation (STE) of hazardous gas leakages in chemical industrial parks (CIPs) is important for addressing environmental pollution and improving engineering safety and reliability. For this purpose, some least squares-based STE methods have recently been developed, which performs the real-time STE using an off-line determined response matrix that represents the relationship between the sensor measurements and strengths of hazardous gas leakages. However, these methods require the number and locations of potential hazardous gas leakage sources are known a priori, which is difficult in many practical applications. To resolve this issue, in the present study, a novel multi-sensor data-driven STE (MSDD-STE) approach is proposed, which overcomes the difficulties with existing least squares-based STE methods and can, for the first time, address the MSDD-STE problems in complicated scenarios where meteorological conditions such as wind directions change over a considerable range. A detailed analysis is introduced to evaluate the performance of the proposed approach. The effectiveness of the proposed approach is verified by comprehensive numerical simulation studies.