Non-linear carbon dioxide determination using infrared gas sensors and neural networks with Bayesian regularization

Carbon dioxide gas concentration determination using infrared gas sensors combined with Bayesian regularizing neural networks is presented in this work. Infrared sensor with a measuring range of 0~5% was used to measure carbon dioxide gas concentration within the range 0~15000 ppm. Neural networks were employed to fulfill the nonlinear output of the sensor. The Bayesian strategy was used to regularize the training of the back propagation neural network with a Levenberg-Marquardt (LM) algorithm. By Bayesian regularization (BR), the design of the network was adaptively achieved according to the complexity of the application. Levenberg-Marquardt algorithm under Bayesian regularization has better generalization capability, and is more stable than the classical method. The results showed that the Bayesian regulating neural network was a powerful tool for dealing with the infrared gas sensor which has a large non-linear measuring range and provide precise determination of carbon dioxide gas concentration. In this example, the optimal architecture of the network was one neuron in the input and output layer and two neurons in the hidden layer. The network model gave a relationship coefficient of 0.9996 between targets and outputs. The prediction recoveries were within 99.9~100.0%

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

More and more metal oxide semiconductor (MOS) gas sensors with digital interfaces are entering the market for indoor air quality (IAQ) monitoring. These sensors are intended to measure volatile organic compounds (VOCs) in indoor air, an important air quality factor. However, their standard operating mode often does not make full use of their true capabilities. More sophisticated operation modes, extensive calibration and advanced data evaluation can significantly improve VOC measurements and, furthermore, achieve selective measurements of single gases or at least types of VOCs. This study provides an overview of the potential and limits of MOS gas sensors for IAQ monitoring using temperature cycled operation (TCO), calibration with randomized exposure and data-based models trained with advanced machine learning. After lab calibration, a commercial digital gas sensor with four different gas-sensitive layers was tested in the field over several weeks. In addition to monitoring normal ambient air, release tests were performed with compounds that were included in the lab calibration, but also with additional VOCs. The tests were accompanied by different analytical systems (GC-MS with Tenax sampling, mobile GC-PID and GC-RCP). The results show quantitative agreement between analytical systems and the MOS gas sensor system. The study shows that MOS sensors are highly suitable for determining the overall VOC concentrations with high temporal resolution and, with some restrictions, also for selective measurements of individual components

Dynamic operation, efficient calibration, and advanced data analysis of gas sensors : from modelling to real-world operation

This thesis demonstrates the use of dynamic operation, efficient calibration and advanced data analysis using metal oxide semiconductor (MOS) gas sensors as an example – from modeling to real-world operation. The necessary steps for an applicationspecific, selective indoor volatile organic compound (VOC) measurement system are addressed, analyzed and improved. Factors such as sensors, operation, electronics and calibration are considered. The developed methods and tools are universally transferable to other gas sensors and applications. The basis for selective measurement is temperature cyclic operation (TCO). The model-based understanding of a semiconductor gas sensor in TCO for the optimized development of operating modes and data evaluation is addressed and, for example, the tailored and stable detection of short gas pulses is developed. Two successful interlaboratory tests for the measurement of VOCs in independent laboratories are described. Selective measurements of VOCs in the laboratory and in the field are successfully demonstrated. Calibrations using the proposed techniques of randomized design of experiment (DoE), model-based data evaluation and calibration with machine learning methods are employed. The calibrated models are compared with analytical measurements using release tests. The high agreement of the results is unique in current research.Diese Thesis zeigt den Einsatz von dynamischem Betrieb, effizienter Kalibrierung, und fortschrittlicher Datenanalyse am Beispiel von Metalloxid Halbleiter (MOS) Gassensoren – von der Modellierung bis zum realen Betrieb. Die notwendigen Schritte für ein anwendungsspezifisches, selektives Messystem für flüchtige organische Verbindungen (VOC) im Innenraum werden adressiert, analysiert und verbessert. Faktoren wie z.B. Sensoren, Funktionsweise, Elektronik und Kalibrierung werden berücksichtigt. Die entwickelten Methoden und Tools sind universell auf andere Gassensoren und Anwendungen übertragbar. Grundlage für die selektive Messung ist der temperaturzyklische Betrieb (TCO). Auf das modellbasierte Verständnis eines Halbleitergassensors im TCO für die optimierte Entwicklung von Betriebsmodi und Datenauswertung wird eingegangen und z.B. die maßgeschneiderte und stabile Detektion von kurzen Gaspulsen entwickelt. Zwei erfolgreiche Ringversuche zur Messung von VOCs in unabhängigen Laboren werden beschrieben. Selektive Messungen verschiedener VOCs im Labor und im Feld werden erfolgreich demonstriert. Dabei kommen Kalibrierungen mit den vorgeschlagenen Techniken des randomisierten Design of Experiment (DoE), der modellbasierten Datenauswertung und Kalibrierung mit Methoden des maschinellen Lernens zum Einsatz. Die kalibrierten Modelle werden anhand von Freisetzungstests mit analytischen Messungen verglichen. Die hohe Übereinstimmung der Ergebnisse ist einzigartig in der aktuellen Forschung

Drift Correction Methods for gas Chemical Sensors in Artificial Olfaction Systems: Techniques and Challenges

In this chapter the authors introduce the main challenges faced when developing drift correction techniques and will propose a deep overview of state-of-the-art methodologies that have been proposed in the scientific literature trying to underlying pros and cons of these techniques and focusing on challenges still open and waiting for solution

Design Issues and Challenges of File Systems for Flash Memories

This chapter discusses how to properly address the issues of using NAND flash memories as mass-memory devices from the native file system standpoint. We hope that the ideas and the solutions proposed in this chapter will be a valuable starting point for designers of NAND flash-based mass-memory devices

An Approximation for Metal-Oxide Sensor Calibration for Air Quality Monitoring Using Multivariable Statistical Analysis

Good air quality is essential for both human beings and the environment in general. The three most harmful air pollutants are nitrogen dioxide (NO2), ozone (O-3) and particulate matter. Due to the high cost of monitoring stations, few examples of this type of infrastructure exist, and the use of low-cost sensors could help in air quality monitoring. The cost of metal-oxide sensors (MOS) is usually below EUR 10 and they maintain small dimensions, but their use in air quality monitoring is only valid through an exhaustive calibration process and subsequent precision analysis. We present an on-field calibration technique, based on the least squares method, to fit regression models for low-cost MOS sensors, one that has two main advantages: it can be easily applied by non-expert operators, and it can be used even with only a small amount of calibration data. In addition, the proposed method is adaptive, and the calibration can be refined as more data becomes available. We apply and evaluate the technique with a real dataset from a particular area in the south of Spain (Granada city). The evaluation results show that, despite the simplicity of the technique and the low quantity of data, the accuracy obtained with the low-cost MOS sensors is high enough to be used for air quality monitoring.The researchers would like to thank the University of Cadiz for the grant obtained through its "Programa de Fomento e Impulso de la actividad de Investigacion y Transferencia". The authors would also like to thank to the Environmental Technology researching group and Acoustic Engineering Laboratory researching group, TEP-181 and TEP-195, respectively, for the access to the devices and data of the EcoBici Project (number G-GI3002/IDIC). Alfonso J. Bello acknowledges the support received from the 2014-2020 ERDF Operational Program and by the Department of Economy, Knowledge, and Business and the University of the Regional Government of Andalusia, Spain, under grant: FEDER-UCA18-107519

Calibration transfer in temperature modulated gas sensor arrays

Shifts in working temperature are an important issue that prevents the successful transfer of calibration models from one chemical instrument to another. This effect is of special relevance when working with gas sensor arrays modulated in temperature. In this paper, we study the use of multivariate techniques to transfer the calibration model from a temperature modulated gas sensor array to another when a global change of temperature occurs. To do so, we built 12 identical master sensor arrays composed of three different types of commercial Figaro sensors and acquired a dataset of sensor responses to three pure substances (ethanol, acetone and butanone) dosed at 7 concentrations. The master arrays are then shifted in temperature (from −50 to 50 °C, ΔT = 10 °C) and considered as slave arrays. Data correction is performed for an increasing number of transfer samples with 4 different calibration transfer techniques: Direct Standardization, Piece-wise Direct Standardization, Orthogonal Signal Correction and Generalized Least Squares Weighting. In order to evaluate the performance of the calibration transfer, we compare the Root Mean Square Error of Prediction (RMSEP) of master and slave arrays, for each instrument correction. Best results are obtained from Piece-wise Direct standardization, which exhibits the lower RMSEP values after correction for the smaller number of transfer samples

Random gas mixtures for efficient gas sensor calibration

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