MACHINE LEARNING BASED INDOOR AIR POLLUTANT SOURCE RECOGNITION WITH GAS RESISTANCE AND MULTI-SENSOR ARRAY ELECTRONIC NOSES

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2022-2-10

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Yesilata, Mehmet Yigitcan

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Indoor Air Quality (IAQ) is closely linked to health and well-being. Humans spend the majority of their time indoors. Breathable air that is free of harmful pollutants can result in an improved quality of life, a decreased risk of respiratory infections, and a decreased risk of developing chronic conditions. Cleaning chemicals, construction operations, smoking, perfumes, building materials and outdoor pollutants can all contribute to indoor air pollution. Detecting the sources of pollution is essential in order to improve interior air quality. A sensing device known as an electronic nose collects various sensor data to detect scents or flavors. Two different types of electronic nose are used in this research to collect data. The first one has 8 VOC sensors that operate in response to the gas resistance of the MOX layer. Each sensor is simultaneously heated by a different heater profile, and their reaction to the gas produces an 8 dimensional sensitivity that is proportional to the sensors gas resistance at that temperature. The second one collects different types of parameters that affect indoor air quality like PM2.5, PM10, CO2, Formaldehyde, Ethanol, H2, TVOC, Temperature and Humidity with a multi sensor array. The experiment was conducted in a 130-liter box. Throughout the experiment, nine different materials were measured, including office air, smoking, cleaning materials, alcohol-sanitizer, curry, coffee, painted tile, stone wool, and varnished wood with two different electronic noses. Data has been modelled with Naive Bayes, kNN, Random Forest Classifiers to predict pollutant sources in the box. Random Forest algorithm with 10 trees gives the best result with data collected from multi sensor array based electronic nose. The algorithm gives 0.95 accuracy with 0.94 precision on the sensor data.

Subject Keywords

Indoor Air Quality, Electronic Nose, Odor Detection, Machine Learning

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https://hdl.handle.net/11511/96757

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

M. Y. Yesilata, “MACHINE LEARNING BASED INDOOR AIR POLLUTANT SOURCE RECOGNITION WITH GAS RESISTANCE AND MULTI-SENSOR ARRAY ELECTRONIC NOSES,” M.S. - Master of Science, Middle East Technical University, 2022.