Gas Sensor Calibration Research Articles

Implementation of An Application-Based Motor Vehicle Exhaust Emission Level Detection System

Currently, air conditions on earth are getting worse over time due to the impact of air pollution. One example of air pollution is motor vehicle exhaust emissions. Exhaust gas emissions are the result of combustion residue in motor vehicle engines that use fuel. Motor vehicle exhaust emissions contain carbon monoxide (CO), hydrocarbons (HC), carbon dioxide gas (CO2) which have a negative impact on the environment and living creatures. This research will create a motor vehicle exhaust emission detection device. In this design, an Arduino microcontroller was used and the manufacture of this tool used an MQ-7 gas sensor to detect carbon monoxide (CO) gas, an MQ-2 sensor to detect hydrocarbon gas (HC), and an MQ-135 to detect carbon dioxide (CO2) gas. The emission test results will be sent to the application, this data includes plate number, vehicle type, vehicle brand, vehicle year and emission test results. The results of the carbon monoxide (CO) gas sensor calibration test taken from 5 data showed an error of 5.51%. The results of the hydrocarbon (HC) gas sensor calibration test taken from 5 data showed an error of 4.23%. The results of the carbon dioxide (CO2) gas sensor calibration test taken from 5 data showed an error of 1.06%. In the implementation of the tool and application, the test results were obtained for 15 vehicles. Where the highest hydrocarbon (HC) gas content value was 412 ppm, the highest carbon monoxide (CO) gas content value was 1.48%, and the highest carbon dioxide (CO2) gas content value was 21.4%.

Strategy to expedite gas sensor calibration based on cyclic temperature operation and data augmentation

The gas sensor calibration using data-driven approach requires a large volume of training data. The conventional method demands numerous measurements of response data, consuming substantial manpower and time resources. To expedite and streamline the sensor calibration and model deployment, this study proposes a cost-effective and practical calibration strategy combining data augmentation and data preprocessing. This strategy aims to minimize both the collection time and amount of measurement data necessary while ensuring high identification accuracy and generalization performance. The random cropping technique based on sliding sampling window is developed to expand the sample size by randomly and consecutively resampling new data from the limited raw response data. This approach allows any fixed-length sequence from the raw response curve to serve as the training and test sample, facilitating fast and real-time measurement for online detection and dynamic analysis. Furthermore, data preprocessing techniques using sequence normalization and fast Fourier transform are employed to extract the species and concentration features, thereby mitigating drift-like effects in the response data. The effectiveness of this strategy is demonstrated with real measurement data, showcasing significant improvements in identification accuracy and generalization performance compared to conventional methods.

Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network.

The advent of cost-effective sensors and the rise of the Internet of Things (IoT) presents the opportunity to monitor urban pollution at a high spatio-temporal resolution. However, these sensors suffer from poor accuracy that can be improved through calibration. In this paper, we propose to use One Dimensional Convolutional Neural Network (1DCNN) based calibration for low-cost carbon monoxide sensors and benchmark its performance against several Machine Learning (ML) based calibration techniques. We make use of three large data sets collected by research groups around the world from field-deployed low-cost sensors co-located with accurate reference sensors. Our investigation shows that 1DCNN performs consistently across all datasets. Gradient boosting regression, another ML technique that has not been widely explored for gas sensor calibration, also performs reasonably well. For all datasets, the introduction of temperature and relative humidity data improves the calibration accuracy. Cross-sensitivity to other pollutants can be exploited to improve the accuracy further. This suggests that low-cost sensors should be deployed as a suite or an array to measure covariate factors.

Decision-Making Optimization of Mine Gas Monitoring Based on Gauss Process Regression and Interval Number Correlation Analysis

For the subjective limitation of gas sensor calibration in coal mines, a decision-making method for gas sensor calibration under monitoring failure was studied based on the Gauss process regression (GPR) and the correlation analysis of interval numbers. Based on the correlation characteristics of gas monitoring data of each monitoring point in the work face area in coal mine, the initial confidence interval of gas concentration in monitoring failure period was obtained by GPR, and then the confidence interval was further optimized by the correlation analysis of interval numbers. According to the correlation characteristics of monitoring data of each monitoring point, its similarity of dynamic variation tendency was measured by using Euclidean distance of interval numbers, and the optimal confidence interval was determined by calculating the correlation degree of interval numbers. The case study shows that making full use of the effective monitoring information of multiple monitoring points ensures the reliability of the initial confidence interval; the dynamic adjustment of model parameters in correlation analysis of interval number avoids the subjectivity defect of similar methods and further obtains the consistency between interval numbers’ reliability and correlation degree, which can ensure the effectiveness of the application of this method.

Field Tests of Smart Mos Gas Sensor Systems for Selective Quantification of VOCs

Field Tests of Smart Mos Gas Sensor Systems for Selective Quantification of VOCs

Random gas mixtures for efficient gas sensor calibration

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.

Efficient and Effective Gas Sensor Calibration with Randomized Gas Mixtures

Efficient and Effective Gas Sensor Calibration with Randomized Gas Mixtures

Gas sensor calibration based on linear optimization

Gas sensor calibration based on linear optimization

Calibration Design and Test of Photoionization Gas Sensor

A dynamic gas-mixing system and PID measurement system were designed. Three VOCs of series concentration were obtained through the controlling on-off of the three-way solenoid valve, and the corresponding circuit design and test were completed. The result shows that the output voltage of the system has a good linearity with the concentration of the gas in the concentration range from 0 to 20ppm, which indicates that it is effective to the dynamic mixing of the gas by the controlling on-off of the solenoid valve. The response sensitivities of the system to three VOCs are different, the sensitivity to butane is the highest, and the sensitivity to xylene is the lowest. The dynamic gas-mixing system can be used in gas sensor calibration.

A novel approach towards calibrated measurement of trace gases using metal oxide semiconductor sensors

We present a method for quantitative measurements of metal oxide semiconductor gas sensors (MOS) which is based on relaxation of surface states using temperature cycled operation (TCO). The method provides sensor response to toluene in form of a power law and a reliable quantification in the concentration range of about 10ppb to 10ppm. For the calibration of the sensor, a method is developed based on the equilibrium vapour headspace over a liquid solution of toluene and squalane yielding reliable concentrations in the range of 10ppb to 600ppm. The calibration method was shown to be stable under ambient conditions and thus to be applicable for field calibration of gas sensors.

Flexible and Autonomous Integrated System for Characterizing Metal Oxide Gas Sensor Response in Dynamic Environment

Characterization and calibration of gas sensor is a complex problem due to the dynamic behavior of gases and the limitations of current technology. This article reports a flexible, robust, and autonomous integrated system that is able to perform characterization on metal oxide-based gas sensors in dynamic environments. The system controls the concentration and flow of the relevant gases into the gas chamber and simultaneously measuring the sensor response. This feature allows the characterization of the sensor under continuous dynamic flow of gases similar to conditions on a robot or flow pipes. Several experiments have been performed on the system using hydrogen sulfide. The results provide information on the general characteristics of the sensor as well as its sensitivity. The noise levels were studied with different reference voltages. Overall, the results verify that the system is reliable and able to produce repeatable measurements.

A Survey on Gas Sensing Technology

Sensing technology has been widely investigated and utilized for gas detection. Due to the different applicability and inherent limitations of different gas sensing technologies, researchers have been working on different scenarios with enhanced gas sensor calibration. This paper reviews the descriptions, evaluation, comparison and recent developments in existing gas sensing technologies. A classification of sensing technologies is given, based on the variation of electrical and other properties. Detailed introduction to sensing methods based on electrical variation is discussed through further classification according to sensing materials, including metal oxide semiconductors, polymers, carbon nanotubes, and moisture absorbing materials. Methods based on other kinds of variations such as optical, calorimetric, acoustic and gas-chromatographic, are presented in a general way. Several suggestions related to future development are also discussed. Furthermore, this paper focuses on sensitivity and selectivity for performance indicators to compare different sensing technologies, analyzes the factors that influence these two indicators, and lists several corresponding improved approaches.

System for dosing formaldehyde vapor at the ppb level

Formaldehyde is one of the most relevant compounds for indoor air pollution. It is toxic, allergenic and carcinogenic and acts already at the ppb level. State-of-the-art detection methods are based on the wet chemical analysis of formaldehyde derivates. This is a complex and time-consuming approach and hinders the collection of real-time data. However, the use of wet chemistry allows for the simple calibration based on formalin solutions. By using gas sensors, online monitoring of indoor air quality is, in principle, possible. To find out whether their performance is good enough, calibration is the first issue to be resolved. Formaldehyde vapor at low concentrations has to be used, and temperature, humidity and flow rate have to be kept constant. This paper discusses the different possibilities of dosing formaldehyde and how to better meet the gas sensor calibration demands. The authors favor the use of an aqueous formaldehyde solution obtained by the depolymerization of paraformaldehyde in combination with a permeation tube used as external reference. Moreover, in the paper it is demonstrated that metal oxide sensors are appropriate detectors to calibrate the system for concentrations even down to 20 ppb. Consequently, the presented system is able to characterize gas sensors and can be used for the development of new devices which monitor indoor air quality.

NEURAL METHODS OF CALIBRATION OF SENSORS FOR GAS MEASUREMENTS AND AROMA IDENTIFICATION SYSTEM

ABSTRACT The article presents the neural methods of calibration of gas sensors for use in an artificial electronic nose for gas measurements. Different neural network solutions will be presented and compared. They include the classical multilayer perceptron, neuro‐fuzzy networks and support vector machines. The other aspect illustrated in the article is the introductory preprocessing of the measured sensor signals in order to attain the highest possible efficiency of the gas measuring system. The theoretical considerations will be supported by the numerical experiments concerning the application of the electronic nose. The first practical aspect is concerned with the application of the developed system for classification problems and will be illustrated in the examples of the recognition of the biocomponents in the gasoline and the recognition of smells of cosmetic cream at the aging process. The second one belongs to the estimation problem and is concerned with the determination of the concentration of the particular gas components in the mixture of gases. PRACTICAL APPLICATIONSThe results presented in the article may find practical application for calibration of the electronic nose in gas measurements. The electronic nose is widely used for smell recognition. It may find practical application in the petroleum, cosmetics or food industry for the assessment of the quality of their products. Military application is also of great interest.

Calibration methods of carbon nanotube gas sensor for partial discharge detection in SF/sub 6/

The authors proposed a new type of gas sensor for high sensitive detection of decomposition products generated by partial discharge (PD) in SF/sub 6/ gas. The sensor employed carbon nanotubes (CNTs) as gas sensing transducer and was fabricated by electrokinetic manipulation of CNTs using dielectrophoresis. Due to complicated gas decomposition process of SF/sub 6/ gas, calibration of the CNT gas sensor is an essential and challenging subject in order to realize reliable and stable detection of PD under practical conditions. In this paper, two methods for the CNT gas sensor calibration were proposed and tested. The first method was based on initial conductance dependence of the CNT gas sensor response. The CNT gas sensor response to PD increased almost linearly with the amount of CNT trapped onto the sensor electrode, which could be quantified by the initial conductance of the sensor. However, the calibration accuracy of this method was about 50 % and was not high enough for practical use. The second method employed NO/sub 2/ (nitrogen dioxide) as calibration gas because of the similarity in the CNT sensor response to PD decomposition products and NO2. It was found that the accuracy of the NO/sub 2/ calibration was about 10 % and far better than the first method. Finally, effectiveness of the NO/sub 2/ calibration was demonstrated by a PD monitoring test in which two CNT sensors were simultaneously exposed to SF/sub 6/ decomposition products with identical concentration.

Fabrication and Characterization of a Sensing Device Based on Porous Silicon

In this work we have fabricated a simple gas-sensing device based on porous silicon. Starting from the well-known porous silicon photoluminescence quenching due to oxygen, we have optimized the material and the device design, obtaining a reversible and stable O2 gas sensor. A simple and cheap detector of air leaks in inert environment is of great interest for the alimentary industry. Full characterization of the device has been carried out in a gas sensor calibration apparatus, showing even a promising sensitivity at room temperature to a toxic gas like NO2.

A fast and reproducible method for gas sensor screening to flavour compounds

Abstract The use of gas sensor arrays has taken a growing place in the field of gas sensor research. One of the main drawbacks of this method is the large amount of time necessary to collect the amount of calibration data, for a convenient task [1]. The purpose of this paper is to present a brand new method for fast and reproducible characterization and calibration of gas sensors. Surface acoustic wave (SAW) gas sensors are very sensitive [2,3], nevertheless their chemical adsorbant layer must still be optimized with respect to the gas species to be detected. For food control purposes, it has been shown that SnO2 sensors may be useful [4–6]. This paper therefore deals with integrated semiconducting sensors and their ability to detect flavours. Experimental results will be presented and discussed and finally some aspects of sensor array processing, using artificial neural networks, will be explored.

Trends in Intelligent Instrumentation

Nowadays there are several application fields where the effective price per unit is the main criterion for instrumentation solutions. Especially the topological structure of such a system is appropriate to design suitable systems: Whereas in former times each (analog) sensor and actuator was connected to a central controlling computer by an own multi-wire cable there is an obvious trend towards fieldbus concepts with one ore more planes. The bus participants are smart sensors and actuators being possible to perform signal processing in a decentralized manner. One of the main reasons for this development is the progressing decrease of costs in the field of microelectronic components caused by improved technological methods. Examples of various spheres (monitoring of concrete constructions, decentralized air-condition, calibration of gas-sensors, instrumentation in automobiles) demonstrate promising realizations which are able to take the part of a pioneer in futural low cost automation techniques.