Improving recognition of odors in a waste management plant by using electronic noses with different technologies, gas chromatography–mass spectrometry/olfactometry and dynamic olfactometry

Giungato, Pasquale; Gennaro, Gianluigi de; Barbieri, Pierluigi; Briguglio, Sara Carmela; Amodio, M.; Gennaro, L. De; Lasigna, Francesco

doi:10.1016/j.jclepro.2016.05.148

Cited by 55 publications

(36 citation statements)

References 27 publications

(28 reference statements)

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“…Model calibration and validation were performed with dynamic elemental sulfur and sulfate profiles, and gas-liquid oxygen transfer was crucial to avoid sulfur accumulation in the packed bed. Zhou et al (2016) compared the stability of odorants of concern from the two WWTPs at the Orange (Giungato et al, 2016). Results of linear discrimination analysis and cross validation showed 86.7% successful recognition for metal oxide semiconductors, 53.3% for Nano Composite Array.…”

Section: Gómezmentioning

confidence: 99%

Gaseous Emissions from Wastewater Facilities

Koh¹,

Shaw²

2017

Water Environment Research

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A review of the literature published in 2016 on topics relating to gaseous emissions from wastewater facilities is presented. This review is divided into the following sections: odorant emissions from wastewater treatment plants (WWTPs); greenhouse gas (GHG) emissions from WWTPs; gaseous emissions from wastewater collection systems; physiochemical odor/emissions control methods; biological odor/emissions control methods; odor characterization/monitoring; and odor impacts/ risk assessments.

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Section: Gómezmentioning

confidence: 99%

Gaseous Emissions from Wastewater Facilities

Koh¹,

Shaw²

2017

Water Environment Research

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“…The revision of multiple approaches [13,[21][22][23] are considered to establish an innovative method and solution for this research, a modification of approach needs to take place to conform this proposal and solution. The approach is some kind of generalization of the Learning Management System (LMS) approach using the same performance index, which is represented by the mean square error, calculated by comparing the approach output with the target output.…”

Section: Advised Approachmentioning

confidence: 99%

Classification via an Embedded Approach

Rubio

Avila

Melendez

et al. 2017

Designs

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This paper presents the results of an automated volatile organic compound (VOC) classification process implemented by embedding a machine learning algorithm into an Arduino Uno board. An electronic nose prototype is constructed to detect VOCs from three different fruits. The electronic nose is constructed using an array of five tin dioxide (SnO 2 ) gas sensors, an Arduino Uno board used as a data acquisition section, as well as an intelligent classification module by embedding an approach function which receives data signals from the electronic nose. For the intelligent classification module, a training algorithm is also implemented to create the base of a portable, automated, fast-response, and economical electronic nose device. This solution proposes a portable system to identify and classify VOCs without using a personal computer (PC). Results show an acceptable precision for the embedded approach in comparison with the performance of a toolbox used in a PC. This constitutes an embedded solution able to recognize VOCs in a reliable way to create application products for a wide variety of industries, which are able to classify data acquired by an electronic nose, as VOCs. With this proposed and implemented algorithm, a precision of 99% for classification was achieved into the embedded solution.

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“…This is particularly true for the Italian scenario, where odor pollution is the object of growing attention from the Public Administration and the Local Authorities [20]. In Italy, electronic noses are more and more used for the assessment of odor impacts [21][22][23], and there is an increasing number of Atmosphere 2020, 11, 426 3 of 21 situations for which the installation of electronic noses is prescribed in the permits of new or existing plants [24].…”

Section: Introductionmentioning

confidence: 99%

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

2020

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Odor pollution is nowadays recognized as a serious environmental concern. Italy still lacks a national regulation about odors, but several regions issued specific guidelines and regulations regarding odor emissions management, which combine olfactometric measurements with dispersion modeling for assessing odor impacts and verifying compliance with acceptability criteria. However, in cases of variable or diffuse sources, this approach is sometimes hardly applicable, because odor emission rates can hardly be estimated. In such cases, electronic noses, or more generally, Instrumental Odor Monitoring Systems (IOMS), represent a suitable solution for direct odor measurement. Accordingly, IOMS are explicitly mentioned in the most recent regional regulations as advanced tools for odor impact assessment. In Italy, data from instrumental odor monitoring have started to have regulatory value; thus the need arises to have specific quality programs to ensure and verify the reliability of IOMS outcomes. This paper describes the monitoring by a commercial electronic nose (EOS507F) of odors from an area dedicated to tire storage, a diffuse source with variable emissions over time, for which dispersion modeling is not applicable. The paper proposes also a protocol for IOMS performance testing in the field, to provide experimental data to support technical groups working on standardization both on the national and European level.

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Improving recognition of odors in a waste management plant by using electronic noses with different technologies, gas chromatography–mass spectrometry/olfactometry and dynamic olfactometry

Cited by 55 publications

References 27 publications

Gaseous Emissions from Wastewater Facilities

Gaseous Emissions from Wastewater Facilities

Classification via an Embedded Approach

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

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