Concentration prediction of binary-component gases based on SnO2 sensor array with machine learning algorithms

Yunlong Gu; Meihua Li; Xiaodong Gao; Yunfan Zhang; Shikun Ge; Chao Mou

doi:10.1117/12.2673999

9 March 2023 Concentration prediction of binary-component gases based on SnO₂ sensor array with machine learning algorithms

Yunlong Gu, Meihua Li, Xiaodong Gao, Yunfan Zhang, Shikun Ge, Chao Mou

Author Affiliations +

Proceedings Volume 12600, International Conference on Optoelectronic Materials and Devices (ICOMD 2022); 126001X (2023) https://doi.org/10.1117/12.2673999
Event: 2022 International Conference on Optoelectronic Materials and Devices, 2022, Chongqing, China

Abstract

Ammonia (NH₃) and formaldehyde (HCHO) are common and highly toxic indoor gases, which are released into the environment through furniture, decorative materials, etc. When the human body is in the environment of ammonia and formaldehyde for a long time, it will cause irreversible harm to the human body. Therefore, it is of great significance for human health to detect low concentration NH₃ and HCHO mixtures efficiently. The gas sensor based on SnO₂ (tin oxide) has the characteristics of fast response, fast recovery and good selectivity, so it has a broad application prospect in detecting indoor toxic gases. In this paper, tin oxide and copper-doped tin oxide gas-sensing materials synthesized by bio-template and hydrothermal methods are introduced for self-made gas-sensing sensor arrays. The sensor array combines SSA-BPNN (Sparrow search algorithm optimized Back-propagation neural network) algorithm to predict and analyze indoor toxic gas concentration. The elemental composition of SnO₂ nanomaterials was characterized and analyzed by XRD (X-ray diffraction). The gas sensing characteristics of the sensor array were tested. The sensor array was combined with a neural network algorithm to successfully predict the concentration information of mixed toxic gases.

Citation Download Citation

Yunlong Gu, Meihua Li, Xiaodong Gao, Yunfan Zhang, Shikun Ge, and Chao Mou "Concentration prediction of binary-component gases based on SnO₂ sensor array with machine learning algorithms", Proc. SPIE 12600, International Conference on Optoelectronic Materials and Devices (ICOMD 2022), 126001X (9 March 2023); https://doi.org/10.1117/12.2673999

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
5 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Sensors

Detector arrays

Gases

Gas sensors

Mixtures

Neural networks

Toxic gases

Show All Keywords

Keywords/Phrases

Search In:

Publication Years