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Performance Comparison of Different Intelligent Techniques Applied on Detecting Proportion of Different Component in Manhole Gas Mixture

Performance Comparison of Different Intelligent Techniques Applied on Detecting Proportion of Different Component in Manhole Gas Mixture

Varun Kumar Ojha, Paramartha Dutta
Copyright: © 2013 |Pages: 28
ISBN13: 9781466625181|ISBN10: 146662518X|EISBN13: 9781466625198
DOI: 10.4018/978-1-4666-2518-1.ch030
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MLA

Ojha, Varun Kumar, and Paramartha Dutta. "Performance Comparison of Different Intelligent Techniques Applied on Detecting Proportion of Different Component in Manhole Gas Mixture." Handbook of Research on Computational Intelligence for Engineering, Science, and Business, edited by Siddhartha Bhattacharyya and Paramartha Dutta, IGI Global, 2013, pp. 758-785. https://doi.org/10.4018/978-1-4666-2518-1.ch030

APA

Ojha, V. K. & Dutta, P. (2013). Performance Comparison of Different Intelligent Techniques Applied on Detecting Proportion of Different Component in Manhole Gas Mixture. In S. Bhattacharyya & P. Dutta (Eds.), Handbook of Research on Computational Intelligence for Engineering, Science, and Business (pp. 758-785). IGI Global. https://doi.org/10.4018/978-1-4666-2518-1.ch030

Chicago

Ojha, Varun Kumar, and Paramartha Dutta. "Performance Comparison of Different Intelligent Techniques Applied on Detecting Proportion of Different Component in Manhole Gas Mixture." In Handbook of Research on Computational Intelligence for Engineering, Science, and Business, edited by Siddhartha Bhattacharyya and Paramartha Dutta, 758-785. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2518-1.ch030

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Abstract

This chapter deals with the comparison of performances of different intelligent techniques for detecting proportion of different component gases present in manhole gas mixture. Toxic gases found in manhole gas mixture are Hydrogen Sulfide (H2S), Ammonia (NH3), Methane (CH4), Carbon Dioxide (CO2), Nitrogen Oxide (NOx), Carbon Monoxide (CO), etcetera. Detection of these toxic gases is essential since these gases influence human health even due to very short exposure. This study is centered on design issues of an intelligent sensory system for detecting proportion of different components in manhole gas mixture and comparison of different intelligent techniques applied for this. The investigation encompasses linear regression based statistical technique, backpropagation algorithm, neuro genetic techniques (using genetic algorithm to train neural network), and neuro swarm techniques (using particle swarm optimization algorithm to train neural network).

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