Application of computational intelligence in classifying chemical gases using electronic nose technology

Date of Publication

3-29-2023

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Electronics and Communications Engineering

Subject Categories

Electrical and Electronics | Systems and Communications

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Thesis Advisor

Elmer P. Dadios

Defense Panel Chair

Edwin Sybingco

Defense Panel Member

Argel A. Bandala
Ryan Rhay P. Vicerra
Laurence A. Gan Lim
Raouf N.G. Naguib

Abstract/Summary

The classification of chemical gases is a critical issue in various industries, such as food, healthcare, and environmental monitoring. The development of electronic nose (e-nose) technology has provided a cost-effective and non-invasive solution for gas detection. In this research need to know the effectiveness of using computational intelligence (CI) techniques for classifying chemical gases using e-nose technology. Developing an e-nose system consisting of a sensor array and an algorithm based on CI techniques. The sensor array was designed to detect volatile organic compounds (VOCs) in the gas samples, and the CI algorithm was used to classify the samples based on their gas concentration. SVM, FFNN, and GPR is use for regression analysis while LSTM, BILSTM, and GRU is used for classification task. The performance of the developed e-nose system was evaluated using a real-world dataset of six different chemical gases namely Carbon Monoxide Toluene, Methane, Ammonia, Ethanol, and Isobutylene. The results showed that the developed system achieved high classification accuracy, with LSTM and BILSTM, and GRU achieving accuracy rates of 97.14%, 98.29%, and 98.8%, respectively. E-nose technology has potential applications in various industries that require gas detection and classification. The results of this research demonstrate the effectiveness of using CI techniques in developing e-nose systems for gas classification. Future work can focus on improving the performance of the system by optimizing the sensor array design and exploring other machine learning algorithm.

Abstract Format

html

Language

English

Keywords

Computational intelligence; Olfactory sensors

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Embargo Period

6-6-2023

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