Evaluation of an electronic nose for odorant and process monitoring of alkaline-stabilized biosolids production

Autor: Mark Ramirez, Alba Torrents, Cathleen J. Hapeman, Laura L. McConnell, Adrian Romero-Flores
Rok vydání: 2017
Předmět:
Environmental Engineering
Biosolids
Health
Toxicology and Mutagenesis
Analytical chemistry
02 engineering and technology
010501 environmental sciences
01 natural sciences
Waste Disposal
Fluid
Gas Chromatography-Mass Spectrometry
Discriminant function analysis
Environmental Chemistry
Electronic Nose
0105 earth and related environmental sciences
Mahalanobis distance
Air Pollutants
Principal Component Analysis
Electronic nose
business.industry
Public Health
Environmental and Occupational Health
Process (computing)
Discriminant Analysis
Pattern recognition
Oxides
General Medicine
General Chemistry
Calcium Compounds
021001 nanoscience & nanotechnology
Linear discriminant analysis
Pollution
Principal component analysis
Pattern recognition (psychology)
Odorants
Environmental science
Artificial intelligence
0210 nano-technology
business
Environmental Monitoring
Zdroj: Chemosphere. 186
ISSN: 1879-1298
Popis: Electronic noses have been widely used in the food industry to monitor process performance and quality control, but use in wastewater and biosolids treatment has not been fully explored. Therefore, we examined the feasibility of an electronic nose to discriminate between treatment conditions of alkaline stabilized biosolids and compared its performance with quantitative analysis of key odorants. Seven lime treatments (0-30% w/w) were prepared and the resultant off-gas was monitored by GC-MS and by an electronic nose equipped with ten metal oxide sensors. A pattern recognition model was created using linear discriminant analysis (LDA) and principal component analysis (PCA) of the electronic nose data. In general, LDA performed better than PCA. LDA showed clear discrimination when single tests were evaluated, but when the full data set was included, discrimination between treatments was reduced. Frequency of accurate recognition was tested by three algorithms with Euclidan and Mahalanobis performing at 81% accuracy and discriminant function analysis at 70%. Concentrations of target compounds by GC-MS were in agreement with those reported in literature and helped to elucidate the behavior of the pattern recognition via comparison of individual sensor responses to different biosolids treatment conditions. Results indicated that the electronic nose can discriminate between lime percentages, thus providing the opportunity to create classes of under-dosed and over-dosed relative to regulatory requirements. Full scale application will require careful evaluation to maintain accuracy under variable process and environmental conditions.
Databáze: OpenAIRE
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