Andrzej Szczurek

APPLICATION OF SENSOR ARRAY AND NEURAL NETWORKS FOR QUANTIFICATION OF ORGANIC SOLVENT VAPOURS IN AIR

Abstract Organic solvents represent significant class of air pollutants. Mixtures of butanol and aromatic compounds are classified among the most commonly used there. The concept of two sensor systems capable of measurements and analysis of butanol/xylene and butanol/toluene mixtures is presented. Sensor array consists of four commercial, tin oxide based, semiconductor type gas sensors (TGS 800 series). For the sensors characterisation manually operated gas installation is constructed. Feedforward neural networks are developed for appropriate analysis of sensors responses. Both systems provide measurements of several single compounds concentrations with inaccuracy not exceeding 15% of range. Humidity influence on system responses is rejected. Possibility of portable realisation make the systems potential replacement of traditional gas analysing devices, especially in off-laboratory applications.

Recognition of benzene, toluene and xylene using TGS array integrated with linear and non-linear classifier

Three volatile organic compounds (VOCs): benzene, toluene and xylene were measured with an array of six Taguchi gas sensors in the air with variable humidity content. The recognition of single compounds was performed, based on measurement results. The principal component analysis (PCA) pointed at humidity as the main classification factor in the measurement data set. The linear discriminant analysis (LDA) was applied to overcome this drawback and enforce classification with respect to benzene, toluene or xylene. It was shown that discriminant function analysis (DFA), which is an LDA method allowed for 100% success rate in test samples recognition of benzene. It did not allow for accurate recognition of test samples of toluene or xylene. Following, the non-linear classifier, radial basis function neural network (RBFNN) was applied. A specific configuration of input s was found, which provided for successful recognition of each single compound: benzene, toluene or xylene in air with variable humidity content.

Method of gas mixtures discrimination based on sensor array, temporal response and data driven approach.

This work presents a method of gas mixtures discrimination. The principal concept of the method is to apply measurement data provided by a combination of sensors at single time point of their temporal response as input of the discrimination models. The pattern data combinations are selected for classes of target gases based on the criterion of 100% efficient discrimination. Combinations of sensors and time points, which provide pattern data combinations in course or repeated measurements, are encoded in the form of addresses. The designer of sensor system is responsible for their selection and they are included in the software of the final instrument. The study of the method involved the discrimination of gas mixtures composed of air and single chemical: hexane, ethanol, acetone, ethyl acetate and toluene. Two sensor arrays were utilized. Each consisted of six TGS sensors of the same type. The dynamic operation of sensors was employed. As an example the stop-flow mode was chosen. The work provides the evidence of the existence of sensor combinations and time points, which are successful in discrimination of studied classes of target gases. The persistence of addresses was discussed considering the ability of sensor array to recognize analytes, variability of repeated measurement results, number of repeated measurements and a twin sets of sensors. Altogether, the validity of the method was demonstrated.

Gas Sensor Array with Broad Applicability

In recent years, effort has been made to develop instruments for rapid, inexpensive analysis of volatile chemical species that do not require trained personnel. This demand has been mainly driven by a variety of real life applications. Indeed, the problem of classifying and further quantifying chemical substances on a real-time basis is very critical for a broad range of activities in various fields, like: industrial (Garrigues et al.,2004), agricultural (Berna, 2010), medical (Byun et al., 2010), domestic (Zampolli et al., 2004) and environmental (Bourgeois et al., 2003).

Copper Phthalocyanine Layer as an Organic Semiconductor Sensor of NO2 in Air

Abstract The applicability of copper phthalocyanine (CuPc) as an organic semiconductor gas sensor for the detection of high concentrations of NO2 in air is the aim of this study. Thin films of CuPc were deposited by sublimation. Measurements were carried out to determine the effect of NO2 on the conductivity of a CuPc film. A detection method for NO2 in air is proposed.

An organic semiconductor as gas detector

Abstract The objective was to investigate the application of copper phthalocyanine (CuPc) films to the detection of air-borne NH3. The phenomenon of gas adsorption on the available surface area of an organic semiconductor was studied. Repeatable results can be obtained relatively quickly provided that a certain state of the semiconductor (which may be adopted as a reference system) will be re-established prior to each measurement of air-borne NH3. The development of an appropriate method for the regeneration of the CuPc film following completion of the detection procedure is described. A linear relation between minimum current intensity and partial pressure (concentration) of NH3 in the gas sample is obtained. The method proposed in this study for the measurement of atmospheric NH3 has analytical potential.

Determination of benzene, toluene and xylene concentration in humid air using differential ion mobility spectrometry and partial least squares regression

Benzene, toluene and xylene (BTX compounds) are chemicals of greatest concern due to their impact on humans and the environment. In many cases, quantitative information about each of these compounds is required. Continuous, fast-response analysis, performed on site would be desired for this purpose. Several methods have been developed to detect and quantify these compounds in this way. Methods vary considerably in sensitivity, accuracy, ease of use and cost-effectiveness. The aim of this work is to show that differential ion mobility spectrometry (DMS) may be applied for determining concentration of BTX compounds in humid air. We demonstrate, this goal is achievable by applying multivariate analysis of the measurement data using partial least squares (PLS) regression. The approach was tested at low concentrations of these compounds in the range of 5-20 ppm and for air humidity in a range 0-12 g/kg. These conditions correspond to the foreseeable application of the developed approach in occupational health and safety measurements. The average concentration assessment error was about 1 ppm for each: benzene, toluene and xylene. We also successfully determined water vapor content in air. The error achieved was 0.2 g/kg. The obtained results are very promising regarding further development of DMS technique as well as its application.

Dynamics of carbon dioxide concentration in indoor air

Abstract Carbon dioxide is an indicator of indoor air quality. A number of factors influence its concentration. Due to the fact that they all present time variability, CO2 concentration indoors considerably varies over time. In this work we focus on the dynamics of indoor CO2 concentration changes. We examine the dynamics of CO2 variation and use it as a source of information on the character of collective impact of factors on indoor air. The proposed method is based on mean square displacement analysis (MSD) applied to the segments of the time series of CO2 monitoring data. The segments are determined based on the introduced criterion for optimal sample size selection. The method was validated by showing that it reproduces the known stochastic dynamics of the simulated data set properly. From the real data analysis, we found that indoors the stochastic dynamics of CO2 concentration in time was mainly nonlinear. Moreover, it exhibited a cycle of change which could be associated with the daily variation of the collective influence of factors on indoor air. We intend to apply the method to other parameters of indoor air, aiming at developing a capability of describing the dynamics of indoor air as a complex system.

Gas Sensing Device Based on Phthalocyanine LB Films

Abstract An investigation of the electrical conductivity of films consisting of multilayers of tetra-t−butyl-substituted copper phthalocyanine (TTBCuPc) films exposed to various levels of nitrogen oxides in air (within the ppm range) was carried out. The TTBCuPc films were deposited, using the Langmuir-Blodgett (LB) technique, onto glass substrates. Gold interdigital electrodes (ca. 2μm thick, 100 μm wide and 100 μm spaced) were evaporated on the top of the films. High reproducibility and reasonable sensitivity of the sensors to the nitrogen oxide at a relatively low temperature (313 K) suggest that TTBCuPc may be a viable material for gas sensing devices.

Sensor array data profiling for gas identification

The paper presents a new method of qualitative identification of gas. It is based on a dynamic response of sensor array with the emphasis on the processing of discrete measurement data. The information needed for identification of test samples is obtained in course of profiling the data from calibration measurements. This operation consists of the following steps: classification of data sets, selection of representative data sets, parameterization of classifiers associated with representative data sets and determination of data records. In our work Discriminant Function Analysis was used for data classification. The information saved in data record describes: the sequential number of discrete measurement, combination of gas sensors in this measurement which are best for classification of calibration samples, and the parameters of associated classifier. They are identifiers of gas class. The procedure of data record determination itself is time consuming. However this operation will be performed only at the stage of the development of the measurement instrument and when its malfunction is diagnosed. The routine use of the instrument will be restricted to gas identification task, which only utilizes the results of profiling. The identification of unknown gas is performed on the base of data records and measurement data obtained for this gas. Data records guide the preparation of data sets, separately for each class of gases. These data sets are used as input of the discriminant functions which have parameter values also indicated by data records. It was shown in the present contribution, that the qualitative identification of nine test gas samples (vapors of ethanol, acetic acid and ethyl acetate in air) with our method was very accurate and fast.