(523g) Data Integration for Proactive Abnormal Emission Identification Via Air-Quality Monitoring Network

Chemical facilities, where large amounts of chemicals and fuels are processed, manufactured, and housed, have high risks to originate air emission events, such as intensive flaring and toxic gas release caused by various uncertainties like equipment failure, false operation, nature disaster, or terrorist attack.  Based on an available air-quality monitoring network, to detect the possible emission source (chemical plant) for an observed emission event, so as to timely and effectively support diagnostic and prognostic decisions, a systematic method for abnormal emission identifications should be employed. 

In our research, a systematic methodology and preliminary data integration system design for such applications has been developed according to the .  It includes two stages of modeling and optimization work: i) the determination of background normal emission rates from multiple emission sources and ii) multi-objective optimization for emission source identification and quantification.  The target of this optimization is to identify the potential emission source for an air quality issue and its dynamic emission rate.  It simultaneously minimizes two objective functions. The first objective function is to minimize SSRE (sum of squared relative error) between the model calculated results and monitoring results from multiple monitoring stations in dynamic situations characterized by the dynamic state set while the second objective of the optimization model is to minimize SRR(sum of relative ratio) of the emission rate. This study can not only determine emission source location, starting time, and time duration responsible for an observed emission event, but also reversely estimate the dynamic emission rate and the total emission amount from the accidental emission source.  It provides valuable information for accidental investigations and root cause analysis for an emission event; meanwhile, it helps evaluate the regional air quality impact caused by such an emission event as well.  Case studies including the detection of a real SO₂ emission event are employed to demonstrate the efficacy of the developed methodology.