Proactive Abnormal Emission Identification by Air-Quality-Monitoring Network
2013
Cai, Tianxing | Wang, Sujing | Xu, Qiang | Ho, Thomas C.
Chemical facilities, where large amounts of chemicals and fuels are processed, manufactured, and housed, are at high risk to originate air emission events, including intensive flaring and toxic gas release caused by various uncertainties such as equipment failure, false operation, natural disaster, or terrorist attack. Based on an available air-quality-monitoring network, to detect the possible emission sources (chemical plants) for an observed emission event, so as to support diagnostic and prognostic decisions in a timely and effective manner, a systematic method for abnormal emission identifications should be employed. In this article, a systematic methodology for such applications has been developed. It includes two stages of modeling and optimization work: (1) the determination of background normal emission rates from multiple emission sources and (2) multiobjective optimization for emission-source identification and quantification. This method not only can determine emission source location, starting time, and time duration responsible for an observed emission event, but can also estimate in reverse the dynamic emission rate and total emission amount from an accidental emission source. It provides valuable information for investigations of accidents and root-cause analysis for emission events; meanwhile, it helps evaluate the regional air-quality impact caused by such emission events as well. Case studies including the detection of a real SO₂ emission event are employed to demonstrate the efficacy of the developed methodology.
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