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 (Lamar University, USA)
DOI: 10.4018/978-1-5225-0788-8.ch047
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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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Biological inspired data mining techniques have been intensively used in different data mining applications such as data clustering, classification, association rules mining, sequential pattern mining, outlier detection, feature selection, and information extraction in healthcare and bioinformatics. The techniques include neural networks, fuzzy logic system, genetic algorithms, ant colony optimization, particle swarm optimization, artificial immune system, culture algorithm, social evolution, and artificial bee colony optimization. A huge increase in the number of papers and citations in the area has been observed in the previous decade, which is clear evidence of the popularity of these techniques. These have included the adoption of such kind of methodologies in the research field of polarization-difference imaging for observation through scattering media (Rowe, Pugh, Tyo, & Engheta, 1995), biologically inspired self-adaptive multi-path routing in overlay networks (Leibnitz, Wakamiya, & Murata, 2006), a biologically inspired system for action recognition (Jhuang, Serre, Wolf, & Poggio, 2007), programmable self-assembly using biologically-inspired multiagent control (Nagpal, 2007), biologically inspired growth of hydroxyapatite nanocrystals inside self-assembled collagen fibers (Roveri, Falini, Sidoti, Tampieri, Landi, Sandri, & Parma, 2003), biologically inspired cognitive radio engine model utilizing distributed genetic algorithms for secure and robust wireless communications and networking(Rieser, 2004), biomimetics of biologically inspired technologies (Bar-Cohen, 2005), biologically inspired computing (DeCastro & von Zuben, 2005), and biologically inspired algorithms for financial modeling (Brabazon & O'Neill, 2006). Before we start to give the introduction of these techniques in the research field of industrial operation and environment sustainability, the brief introduction will be given for these techniques.

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