Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (1): 382-388.doi: 10.13229/j.cnki.jdxbgxb20181069

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Oil and gas detection method and experimental new technology based on bionic nasal chamber optimization

Xiao-hui WENG1,2,3(),You-hong SUN4,Shu-jun ZHANG1,2,5,Jun XIE1,2,Zhi-yong CHANG1,2,4()   

  1. 1. Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
    2. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
    3. College of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China
    4. National?Local Joint Engineering Laboratory of In?situ Conversion, Drilling and Exploitation Technology for Oil Shale, Jilin University, Changchun 130021, China
    5. School of Computing and Technology, University of Gloucestershire, The Park, Cheltenham GL50 2RH, UK
  • Received:2018-10-09 Online:2020-01-01 Published:2020-02-06
  • Contact: Zhi-yong CHANG E-mail:wengxiaohui@jlu.edu.cn;zychang@jlu.edu.cn

Abstract:

A small bionic electronic nose system for oil and gas detection was designed. By analyzing the structural characteristics and flow field distribution inside the nasal passage of the dog's nasal chamber, an optimized bionic electronic nasal chamber simulating the nasal chamber of the dog was proposed, which effectively solved the problems of bloated and inflexible operation of the oil and gas detection device. The 3-D printed chamber sample was used to evaluate the detection effect of hydrocarbon mixed gas. The 10-fold cross-validation method was used to compare the recognition effects of random forest, naive Bayes, extreme learning machine and support vector machine algorithms. The results show that the sensitivity of the electronic nose system after optimizing the detection chamber is obviously higher than that of the ordinary chamber electronic nose system, and the recognition rate is increased by 5.5%. The CFD analysis results of the 3D simulation chamber also verify the validity of the bionic nasal chamber.

Key words: technology of instruments and meter, electronic nose, dog nasal chamber, bionic design, oil and gas detection, sensor

CLC Number: 

  • TH763

Table 1

Factor-level table for common nasal chamber design"

水 平 因 素
A/mm B/mm C/(m?s-1 D/s
1 20 75 0.5 60
2 30 85 0.8 90
3 40 95 1.2 120
离差平方和 0.183 0.052 0.101 0.001
均方 0.115 0.061 0.072 0.021
F 4.241 1.034 3.124 0.021

Fig.1

Common nasal chamber structure of electronic nose"

Fig.2

Intranasal fluid dynamics of canine olfaction"

Fig.3

Bionic chamber structure of electronic nose"

Table 2

Factor?level table for bionic nasal chamber design"

水平 因素
E F G H I
1 30 10 10 20 10
2 40 15 15 30 15
3 50 20 20 40 20
离差平方和 0.002 0.073 0.192 0.012 0.051
均方 0.005 0.063 0.131 0.011 0.026
F 0.014 2.245 4.713 0.021 1.134

Fig.4

3D printed electronic nose"

Table 3

Concentration ratio of sample gas components"

种 类 体积浓度/%
甲烷 乙烷 二氧化碳 氮气
1 1 1 8 90
2 2 1.5 6.5 90
3 3 2 5 90

Fig.5

Flow chart of electronic nose system"

Fig.6

Composition diagram of electronic nose system"

Fig.7

"

Fig.8

Comparison of recognition rates by different algorithms"

Fig.9

Eddy strength comparison of sensor surface"

1 李樟云 . 随钻气体分离先导技术研究[D]. 厦门: 厦门大学化学化工学院, 2011.
Li Zhang-yun . Study on pilot technology of gas separation while drilling[D]. Xiamen: College of Chemistry and Chemical Engineering,Xiamen University, 2011.
2 Sun Xi-yang , Liu Lin-feng , Wang Zhan . An optimized multi-classifiers ensemble learning for identification of ginsengs based on electronic nose[J]. Sensors and Actuators A: Physical, 2017, 266: 135-144.
3 张哲 . 仿生电子鼻传感器阵列设计及其在牛肉品质检验中的应用[D]. 长春: 吉林大学生物与农业工程学院, 2008.
Zhang Zhe . Designs of sensor matrixes for biomimetic electronic nose and their application in detection of beef quality[D]. Changchun: College of Biological and Agricultural Engineering,Jilin University, 2008.
4 常志勇, 陈东辉, 佟月英, 等 . 基于人体嗅觉特征的猪肉新鲜度仿生电子鼻检测技术[J]. 吉林大学学报:工学版, 2012, 42(增刊1): 131-134.
Chang Zhi-yong , Chen Dong-hui , Tong yue-ying , et al . Human olfactory feature based bionic electronic nose technology for pork freshness detection[J]. Journal of Jilin University(Engineering and Technology Edition), 2012, 42(Sup.1): 131-134.
5 Zhou H , Luo D , Gholam H H , et al . Identification of Chinese herbal medicines with electronic nose technology: applications and challenges[J]. Sensors, 2017, 17(5):No.1073.
6 Li Shang-zhen , Zeng Su-ling , Wu Yan , et al . Cultivar differentiation of Citri Reticulatae Pericarpium by a combination of hierarchical three-step filtering metabolomics analysis, DNA barcoding and electronic nose[J]. Analytica Chimica Acta, 2019, 1056: 62-69.
7 Sun H , Tian F , Liang Z , et al . Sensor array optimization of electronic nose for detection of bacteria in wound infection[J]. IEEE Transactions on Industrial Electronics, 2017, 64(9): 7350-7358.
8 Domenico Cipriano , Laura Capelli . Evolution of electronic noses from research objects to engineered environmental odour monitoring systems: a review of standardization approaches[J]. Biosensors, 2019, 92(2): No.75.
9 Zarra T , Cimatoribus C , Naddeo V , et al . Environmental odour monitoring by electronic nose[J]. Global Nest Journal, 2019, 20(3): 664-668.
10 Yang H , Nguyen Q T , Ping Z , et al . Desorption and pervaporation properties of zeolite-filied poly(dimethylsiloxane) membranes[J]. Material Research Innovations, 2001, 5(2): 101-106.
11 Wen J , Inthavong K , Tu J , et al . Numerical simulations for detailed airflow dynamics in a human nasal chamber[J]. Respiratory Physiology & Neurobiology, 2008, 161(2): 125-135.
12 Falcitelli M , Benassi A , Francesco F D , et al . Fluid dynamic simulation of a measurement chamber for electronic noses[J]. Sensors and Actuators B: Chemical, 2002, 85(1/2): 166-174.
13 Waldrop L D , Hann M , Henry A K , et al . Ontogenetic changes in the olfactory antennules of the shore crab, Hemigrapsus Oregonensis, maintain sniffing function during growth[J]. Journal of the Royal Society Interface, 2015, 102 (12):1-19.
14 Che H F K , Taylor J E , Covington J A , et al . An electronic nose employing dual-channel odour separation columns with large chemosensor arrays for advanced odour discrimination[J]. Sensors and Actuators B: Chemical, 2009, 141(1): 134-140.
15 Giacomo Viccione , Daniele Spiniello , Tiziano Zarra , et al . Fluid dynamic simulation of odour measurement chamber[J]. Chemical Engineering Transactions, 2014, 40: 109-114.
16 魏荣兴 . 影响犬嗅觉能力的相关因素[J]. 黑龙江畜牧兽医, 2016(16): 189-190.
Wei Rong-xing . Related factors affecting dogs'olfactory ability[J]. Heilongjiang Animal Science and Veterinary Medicine, 2016(16): 189-190.
17 Craven Brent A , Paterson Eric G , Settles Gary S . The fluid dynamics of canine olfaction: unique nasal airflow patterns as an explanation of macrosmia[J]. Journal of the Royal Society Interface, 2009, 47(7): 933-943.
18 Craven Brent A , Paterson Eric G , Settles Gary S . Development and verification of a high-fidelity computational fluid dynamics model of canine nasal airflow[J]. Journal of Biomechanical Engineering, 2009, 131(9): 091002.
19 Zhao K , Dalton P , Yang G C , et al . Numerical modeling of turbulent and laminar airow and odorant transport during snifng in the human and rat nose[J]. Chemical Senses, 2006, 31(2): 107-118.
20 Francesco F D , Falcitelli M , Marano L , et al . A radially symmetric measurement chamber for electronic noses[J]. Sensors and Actuators B: Chemical, 2005, 105(2): 295-303.
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