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

Abstract

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.

Investigating the dynamics of volatile sulfur compound emission from primary systems at a water resource recovery facility

“…Analysis of canisters was performed within 24 hr of sampling using an Entech Model 7500 Autosampler followed by a Model 7200 Preconcentrator coupled to an Agilent 5790N Gas Chromatographer – Mass Spectrometer (Agilent Technologies Inc., Wilmington, DE, USA) (Romero‐Flores, McConnell, Hapeman, Ramirez, & Torrents, 2017). Sample canisters were placed in the autosampler, and air samples of 10–500 ml were withdrawn for preconcentration using the following method: empty trap at −40°C to remove most water vapor; organic VSCs captured on Tenax trap at −40°C and flushed with helium to remove remaining water; Tenax trap heated to 10°C; organic VSCs trapped on fused silica tube at −150°C; and tube heated at to 50°C onto the GC‐MS column.…”

“…Tests were performed at constant stripping rate of 1.5 L/min of nitrogen gas, and gas samples were collected at regular intervals (every 10 min) over a period of 30 min. This method allowed for MM and H 2 S production rate calculation based on gas‐phase measurements for the given conditions and sludge/food mixture (Romero‐Flores et al, 2017). Additionally, soluble MM and H 2 S were estimated using a method described in Romero‐Flores et al (2017).…”

Definition and Application of a Protocol for Electronic Nose Field Performance Testing: Example of Odor Monitoring from a Tire Storage Area

“…The electronic nose training represents the most important phase of the instrumental odor monitoring process [42]. It consists of the creation of the dataset (i.e., the training set-TS) comprising the characteristic "patterns" [43] of the odors that the instrument will be exposed to during the monitoring phase. This database is used by the IOMS as a reference for the classification (i.e., the recognition of the odor quality) of the ambient air that it analyzes during the monitoring phase at the receptor or at the plant fence line.…”

Biosolids and Sludge Management

“…The feasibility of the use of electronic noses in odor reduction has been studied by Romero-Flores et al (2017). The approaches included forestation, extension of timber cycles, and geological sequestration with the ultimate goal of balancing the supply and demand of both ecosystem and technological systems (Gopalakrishnan et al, 2017).…”