Development of Gas Sensor Array for Methane Reforming Process Monitoring

The article presents a new method of monitoring and assessing the course of the dry methane reforming process with the use of a gas sensor array. Nine commercially available TGS chemical gas sensors were used to construct the array (seven metal oxide sensors and two electrochemical ones). Principal Component Regression (PCR) was used as a calibration method. The developed PCR models were used to determine the quantitative parameters of the methane reforming process: Inlet Molar Ratio (IMR) in the range 0.6–1.5, Outlet Molar Ratio (OMR) in the range 0.6–1.0, and Methane Conversion Level (MCL) in the range 80–95%. The tests were performed on model gas mixtures. The mean error in determining the IMR is 0.096 for the range of molar ratios 0.6–1.5. However, in the case of the process range (0.9–1.1), this error is 0.065, which is about 6.5% of the measured value. For the OMR, an average error of 0.008 was obtained (which gives about 0.8% of the measured value), while for the MCL, the average error was 0.8%. Obtained results are very promising. They show that the use of an array of non-selective chemical sensors together with an appropriately selected mathematical model can be used in the monitoring of commonly used industrial processes.

[1]  Stefan Spichiger,et al.  Process monitoring with disposable chemical sensors fit in the framework of process analysis technology (PAT) for innovative pharmaceutical development and quality assurance. , 2010, Chimia.

[2]  V Belgiorno,et al.  Instrumental characterization of odour: a combination of olfactory and analytical methods. , 2009, Water science and technology : a journal of the International Association on Water Pollution Research.

[3]  Rapid Quantification Analysis of Alcohol During the Green Jujube Wine Fermentation by Electronic Nose , 2019, IOP Conference Series: Earth and Environmental Science.

[4]  F. Farret,et al.  Use of infrared matrix sensor for temperature measurement and monitoring of PEM/FC stacks , 2019, Sensors and Actuators A: Physical.

[5]  Tomasz Dymerski,et al.  Identification of odor of volatile organic compounds using classical sensory analysis and electronic nose technique , 2014 .

[6]  Valeria Belikova,et al.  Continuous monitoring of water quality at aeration plant with potentiometric sensor array , 2019, Sensors and Actuators B: Chemical.

[7]  Himanshu K. Patel,et al.  The Electronic Nose: Artificial Olfaction Technology , 2013 .

[8]  I Lundström,et al.  Sensor fusion with on-line gas emission multisensor arrays and standard process measuring devices in baker's yeast manufacturing process. , 1997, Biotechnology and bioengineering.

[9]  Regine Eibl,et al.  Novel probes for pH and dissolved oxygen measurements in cultivations from millilitre to benchtop scale , 2016, Applied Microbiology and Biotechnology.

[10]  Jacek Gebicki,et al.  Measurement techniques for assessing the olfactory impact of municipal sewage treatment plants , 2015, Environmental Monitoring and Assessment.

[11]  Saeid Minaei,et al.  A portable electronic nose as an expert system for aroma-based classification of saffron , 2016 .

[12]  Juzhong Tan,et al.  Sensing fermentation degree of cocoa (Theobroma cacao L.) beans by machine learning classification models based electronic nose system , 2019, Journal of Food Process Engineering.

[13]  J. Koziel,et al.  Characterization of Livestock Odors Using Steel Plates, Solid-Phase Microextraction, and Multidimensional Gas Chromatography–Mass Spectrometry–Olfactometry , 2006, Journal of the Air & Waste Management Association.

[14]  R Lebrero,et al.  Monitoring techniques for odour abatement assessment. , 2010, Water research.

[15]  Beatrice Lazzerini,et al.  An electronic nose for odour annoyance assessment , 2001 .

[16]  Tim C. Pearce,et al.  Electronic nose for monitoring the flavour of beers , 1993 .

[17]  Nicolas Szita,et al.  Integration and application of optical chemical sensors in microbioreactors. , 2017, Lab on a chip.

[18]  Bartosz Szulczyński,et al.  Monitoring of n-butanol vapors biofiltration process using an electronic nose combined with calibration models , 2018, Monatshefte für Chemie - Chemical Monthly.

[19]  Ioannis Raptis,et al.  Wireless Sensor Network Based on a Chemocapacitive Sensor Array for the Real-time Monitoring of Industrial Pollutants☆ , 2014 .

[20]  E. Guichard,et al.  Determination of key odorant compounds in freshly distilled cognac using GC-O, GC-MS, and sensory evaluation. , 2004, Journal of agricultural and food chemistry.

[21]  Bipan Tudu,et al.  A recurrent Elman network in conjunction with an electronic nose for fast prediction of optimum fermentation time of black tea , 2017, Neural Computing and Applications.

[22]  J. M. Rees,et al.  Measuring Vapor and Liquid Concentrations for Binary and Ternary Systems in a Microbubble Distillation Unit via Gas Sensors , 2018, Chemosensors.

[23]  I. Lundström,et al.  On-line monitoring of a cultivation using an electronic nose , 1998 .

[24]  Tomasz Dymerski,et al.  Different Ways to Apply a Measurement Instrument of E-Nose Type to Evaluate Ambient Air Quality with Respect to Odour Nuisance in a Vicinity of Municipal Processing Plants , 2017, Sensors.

[25]  Simon A. Parsons,et al.  Sewage treatment works odour measurement , 2000 .

[26]  O. Wolfbeis,et al.  Optical methods for sensing and imaging oxygen: materials, spectroscopies and applications. , 2014, Chemical Society reviews.

[27]  L. McConnell,et al.  Evaluation of an electronic nose for odorant and process monitoring of alkaline-stabilized biosolids production. , 2017, Chemosphere.

[28]  Jesús Lozano,et al.  Electronic Nose Based on Independent Component Analysis Combined with Partial Least Squares and Artificial Neural Networks for Wine Prediction , 2012, Sensors.

[29]  K. Triyana,et al.  Electronic Nose Coupled with Chemometrics for Monitoring of Tempeh Fermentation Process , 2018, 2018 4th International Conference on Science and Technology (ICST).

[30]  J. Gardner,et al.  An electronic nose system for monitoring the quality of potable water , 2000 .

[31]  Chunjie Wu,et al.  Identification of Different Bile Species and Fermentation Times of Bile Arisaema Based on an Intelligent Electronic Nose and Least Squares Support Vector Machine. , 2018, Analytical chemistry.

[32]  Mustafa Odabasi,et al.  Chemical characterization of odorous gases at a landfill site by gas chromatography-mass spectrometry. , 2006, Journal of chromatography. A.

[33]  Roberto Giua,et al.  Synergistic approaches for odor active compounds monitoring and identification: State of the art, integration, limits and potentialities of analytical and sensorial techniques , 2018, TrAC Trends in Analytical Chemistry.

[34]  Jie Xu,et al.  Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review , 2020 .

[35]  T. Bachinger,et al.  Searching for process information in the aroma of cell cultures. , 2000, Trends in biotechnology.

[36]  Carl-Fredrik Mandenius,et al.  Physiologically Motivated Monitoring of Fermentation Processes by Means of an Electronic Nose , 2001 .