Random gas mixtures for efficient gas sensor calibration

Abstract

Abstract. Applications like air quality, fire detection and detection of explosives require selective and quantitative measurements in an ever-changing background of interfering gases. One main issue hindering the successful implementation of gas sensors in real-world applications is the lack of appropriate calibration procedures for advanced gas sensor systems. This article presents a calibration scheme for gas sensors based on statistically distributed gas profiles with unique randomized gas mixtures. This enables a more realistic gas sensor calibration including masking effects and other gas interactions which are not considered in classical sequential calibration. The calibration scheme is tested with two different metal oxide semiconductor sensors in temperature-cycled operation using indoor air quality as an example use case. The results are compared to a classical calibration strategy with sequentially increasing gas concentrations. While a model trained with data from the sequential calibration performs poorly on the more realistic mixtures, our randomized calibration achieves significantly better results for the prediction of both sequential and randomized measurements for, for example, acetone, benzene and hydrogen. Its statistical nature makes it robust against overfitting and well suited for machine learning algorithms. Our novel method is a promising approach for the successful transfer of gas sensor systems from the laboratory into the field. Due to the generic approach using concentration distributions the resulting performance tests are versatile for various applications.

Measuring Hydrogen in Indoor Air with a Selective Metal Oxide Semiconductor Sensor

“…In a first step, the sensors were lab-calibrated inside a gas mixing system [38,39] using randomized gas mixtures [40], which means that all gases connected to the system, in this case six, are applied at once and their concentrations are randomly chosen from defined ranges (acetone 17-1000 ppb, carbon monoxide 150-2000 ppb, ethanol 4-1000 ppb, formaldehyde 1-400 ppb, hydrogen 400-4000 ppb, and toluene 4-1000 ppb). For the generation of this randomized mixtures Latin hypercube sampling is performed to ensure proper scanning of the full measurement range and the obtained concentration values are stored for training of the sensor models [41].…”

“…For the generation of this randomized mixtures Latin hypercube sampling is performed to ensure proper scanning of the full measurement range and the obtained concentration values are stored for training of the sensor models [41]. This method yields perfectly suited data for the training of machine learning algorithms as described before [40]. Each gas exposure has a duration of 20 min, 400 independent mixtures were measured during the calibration.…”

High-Performance VOC Quantification for IAQ Monitoring Using Advanced Sensor Systems and Deep Learning

“…The dataset was based on an SGP30 sensor (Sensirion AG, Stäfa, Switzerland) with four gas-sensitive layers [26], operated using TCO for improved selectivity, sensitivity, and stability. The sensor was lab-calibrated using complex random gas mixtures [27] and then tested during operation in a typical office environment with as little human presence as possible over several weeks. Several release tests of VOCs and hydrogen were performed to validate the sensor response and to compare the performance of the model predictions of the MOS sensor system to analytical instruments [13].…”

Field Study of Metal Oxide Semiconductor Gas Sensors in Temperature Cycled Operation for Selective VOC Monitoring in Indoor Air

“…The aim of the calibration is to achieve a reliable mathematical model for the prediction of different VOC, interfering gases, and sum signals, e.g., the sum of all VOCs in indoor air, in our field tests from the multi-dimensional gas sensor data. Analytic studies of VOCs in indoor air show that more than 400 different VOCs representing more than 14 chemical classes can be found in indoor air [22,23]. Studies on other substances besides VOC in indoor air are less diverse.…”

“…Table 1. 90th (P90) and 95th (P95) percentile sum concentration in µg/m 3 and ppb (calculated from the individual substances dominating for each chemical class) for the eight chemical classes with the highest sum concentrations as determined from analytical studies [22,23] in alphabetical order. The substance in parentheses is the representative with the highest concentration for this chemical class.…”