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Application of Biologically Inspired Techniques for Industrial and Environmental Research via Air Quality Monitoring Network

Application of Biologically Inspired Techniques for Industrial and Environmental Research via Air Quality Monitoring Network

Tianxing Cai
Copyright: © 2014 |Pages: 27
ISBN13: 9781466660786|ISBN10: 1466660783|EISBN13: 9781466660793
DOI: 10.4018/978-1-4666-6078-6.ch013
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MLA

Cai, Tianxing. "Application of Biologically Inspired Techniques for Industrial and Environmental Research via Air Quality Monitoring Network." Biologically-Inspired Techniques for Knowledge Discovery and Data Mining, edited by Shafiq Alam, et al., IGI Global, 2014, pp. 272-298. https://doi.org/10.4018/978-1-4666-6078-6.ch013

APA

Cai, T. (2014). Application of Biologically Inspired Techniques for Industrial and Environmental Research via Air Quality Monitoring Network. In S. Alam, G. Dobbie, Y. Koh, & S. ur Rehman (Eds.), Biologically-Inspired Techniques for Knowledge Discovery and Data Mining (pp. 272-298). IGI Global. https://doi.org/10.4018/978-1-4666-6078-6.ch013

Chicago

Cai, Tianxing. "Application of Biologically Inspired Techniques for Industrial and Environmental Research via Air Quality Monitoring Network." In Biologically-Inspired Techniques for Knowledge Discovery and Data Mining, edited by Shafiq Alam, et al., 272-298. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-6078-6.ch013

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Abstract

Industrial and environmental research will always involve the study of the cause-effect relationship between the emissions and the surrounding environment. Qualitative and mixed methods researchers have employed a variety of Information and Communication Technology (ICT) tools, simulated or virtual environments, information systems, information devices, and data analysis tools in this field. Machine-enhanced analytics has enabled the identification of aspects of interest such as correlations and anomalies from large datasets. Chemical facilities have high risks to originate air emission events. Based on an available air-quality monitoring network, the data integration technologies are applied to identify the scenarios of the possible emission source and the dynamic pollutant monitor result, so as to timely and effectively support diagnostic and prognostic decisions. In this chapter, the application of artificial neural networks for such applications have been developed according to the real application purpose. It includes two stages of modeling and optimization work: 1) the determination of background normal emission rates from multiple emission sources and 2) single-objective or multi-objective optimization for impact scenario identification and quantification. They have the capability to identify the potential emission profile and spatial-temporal characterization of pollutant dispersion for a specific region, including reverse estimation of the air quality issues. The methodology 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 are employed to demonstrate the efficacy of the developed methodology.

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