Gas sensor array exposed to turbulent gas mixtures

Donated on 10/9/2014

A chemical detection platform composed of 8 chemoresistive gas sensors was exposed to turbulent gas mixtures generated naturally in a wind tunnel. The acquired time series of the sensors are provided.

Dataset Characteristics

Multivariate, Time-Series

Subject Area

Computer Science

Associated Tasks

Classification, Regression

Feature Type


# Instances


# Features


Dataset Information

Additional Information

A chemical detection platform composed of 8 chemo-resistive gas sensors was exposed to turbulent gas mixtures generated naturally in a wind tunnel. The acquired time series of the sensors are provided. The experimental setup was designed to test gas sensors in realistic environments. Traditionally, chemical detection systems based on chemo-resistive sensors include a gas chamber to control the sample air flow and minimize turbulence. Instead, we utilized a wind tunnel with two independent gas sources that generate two gas plumes. The plumes get naturally mixed along a turbulent flow and reproduce the gas concentration fluctuations observed in natural environments. Hence, the gas sensors can capture the spatio-temporal information contained in the gas plumes. a) Chemical detection platform: The chemical detection platform was composed of 8 MOX gas sensors that generate a time-dependent multivariate response to the different gas stimuli. The utilized sensors were made commercially available by Figaro (TGS2611, TGS2612, TGS2610, TGS2600, TGS2602 TGS2620). The operating temperature of the sensors was controlled by the built-in heater, which was kept at a constant voltage of 5V. The detection platform also includes Temperature and Relative Humidity sensors. The generated sensors' responses were acquired at a sampling rate of 20 ms for the whole duration of the experiment. b) Wind tunnel: In order to generate two independent gas plumes in an open environment, we built a 2.5 m x 1.2 m x 0.4 m wind tunnel facility with two gas sources (labeled as source1 and source2). Each source was controlled independently to release the selected volatiles at different flow rates, which generated different concentration levels in the sensors' position. The wind generator created a turbulent flow that constantly displaced the introduced volatiles towards the exhaust outlet. c) Experimental protocol: We exposed the detection unit to mixtures of Ethylene with Methane or Carbon Monoxide. The mixtures were originated releasing Ethylene at source1 and releasing Methane / Carbon Monoxide at source2. Each volatile was released at four different flows (zero z, low l, medium m, and high h), providing up to 30 different mixture configurations: 15 mixtures of Ethylene with CO (h+h, h+m, h+l, …, z+h, z+m, z+l) and 15 mixtures of Ethylene with Methane. Each configuration was repeated 6 times. Hence, the complete dataset was composed of 180 measurements, which were performed in a random order. By means of a GCMS system, the mean concentration levels at the sensors' location were estimated: Ethylene (l: 31 ppm, m: 46 ppm, h: 96 ppm), CO (l: 270 ppm, m: 397 ppm, h: 460 ppm), Methane (l: 51 ppm, m: 115 ppm, h: 131 ppm). It is worth noting that GC-MS systems only provide the mean value of the concentration and are not sensitive to concentration fluctuations. Each measurement, which had a total duration of 300 seconds, was performed as follows: Initially no gas was released and clean air flowed along the wind tunnel. 60 seconds after, both sources started to release the corresponding volatile at the specified flow rate. The duration of the gas release was 180 s. Finally, the system acquired the recovery to the baseline for another 60 s.

Has Missing Values?


Variable Information

The dataset is presented in 180 text files, where each file corresponds to a different measurement. The filenames identify the measurements as follows: The first 3 characters of the filename are a local identifier, which is not related to the order of the measurements; characters 5-8 indicate the concentration level of Ethylene released at source2 (n: zero, L: Low, M: Medium, H: High); the last 4 characters indicate the gas released at source1 (Me: Methane, CO: Carbon Monoxide) and the concentration level. For example, file 007_Et_L_Me_H contains time series acquired when Ethylene was released at Low concentration (31 ppm, mean concentration) and Methane at High concentration (131 ppm, mean concentration). Each file includes the acquired time series, presented in 11 columns: Time (s), Temperature (oC), Relative Humidity (%), and the readings of the 8 gas sensors: TGS2600, TGS2602, TGS2602, TGS2620, TGS2612, TGS2620, TGS2611, TGS2610. The readings can be converted to sensor resistance by Rs(KOhm)=10*(3110-A)/A, where A is the acquired value. The raw acquired time series are provided, and also time series down sampled at 100 ms.


There are no reviews for this dataset yet.

Login to Write a Review
0 citations


Jordi Fonollosa


By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository.

Read Policy