Prediction of the SnO2-based sensor response for hydrogen detection by artificial intelligence techniques |
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| Affiliation: | 1. Department of Engineering Management, Hubei University of Economics, Research Center of Hubei Logistics Development, Hubei, China;2. College of Energy and Electrical Engineering, Hohai University, Engineering Research Center of Renewable Power Generation Technologies, Ministry of Education, Nanjing, China;3. Hubei Geological Research Laboratory, Hubei, China;4. Department of Civil Engineering, College of Engineering, Cihan University-Erbil, Erbil, Iraq;5. Department of Signal Analysis, Advanced Computation Technical Center, Tehran, Iran;1. School of Physics and Technology, University of Jinan, Jinan 250022, Shandong Province, PR China;2. School of Mathematics and Physics, Anhui University of Technology, Ma''anshan 243032, Anhui Province, PR China;1. Design Factory Melbourne, Swinburne University of Technology, Hawthorn, Victoria, 3122, Australia;2. Victorian Hydrogen Hub, Swinburne University of Technology, Hawthorn, Victoria, 3122, Australia;1. Jiangsu Provincial Key Laboratory of Solar Energy Science and Technology/Energy Storage Research Center, School of Energy and Environment, Southeast University, No. 2 Si Pai Lou, Nanjing, Jiangsu 210096, PR China;2. College of Material Science and Technology, Southeast University, No. 2 Si Pai Lou, Nanjing, Jiangsu 210096, PR China;3. Engineering Research Center of Nano-Geo Materials of Ministry of Education, Department of Materials Science and Chemistry, China University of Geosciences, 388 Lumo Road, Wuhan, 430074, China;1. Department of Mechanical Engineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal;2. MARETEC/DEM - Marine, Environment and Technology Centre, LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal;3. Center IN+, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal |
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| Abstract: | SnO2-based nanocomposites are reliable sensors to detect hydrogen leakage and satisfy safety protocols. Although the hydrogen detection response (HDR) of these sensors has been deeply studied in the laboratory, there are no models to estimate this parameter. Consequently, this study uses three machine learning classes (i.e., gene expression programming, support vector regression, and artificial neural network) to calculate the HDR of pure and Ag-, Co-, Pd-, Pt-, and Ru-decorated SnO2 nanostructures. These models only need nanocomposite chemistry and operating conditions to estimate the HDR of SnO2-based sensors. Comparing these models’ performance by the ranking analysis and spider-graph indicates the multilayer perceptron neural network is superior to the other models. This model shows the highest accuracy (regression coefficient = 0.9882, average absolute deviation = 2.74, and root mean squared errors = 8.05) for estimating the HDR of SnO2-based sensors. This model also anticipates that Pd and Ru are the best and worst dopants to decorate the SnO2-based sensors. |
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| Keywords: | Hydrogen sensing device Modeling the sensor response Machine learning methods Tuning the sensor chemistry |
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