Metrological Characterization of a Novel Microsensor Platform for Activated Carbon Filters Monitoring

Cerro, Gianni; Ferdinandi, Marco; Ferrigno, Luigi; Laracca, Marco; Molinara, Mario

doi:10.1109/tim.2018.2843218

Cited by 27 publications

(5 citation statements)

References 13 publications

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“…Endowed with a versatile analog front end and different internal and external ports, it allows to perform electrical impedance measurements both on internal and external sensors. It has been already adopted in other works, as in [7], [30], [31].…”

Section: Proposed Integrated System a Overviewmentioning

confidence: 99%

An End to End Indoor Air Monitoring System Based on Machine Learning and SENSIPLUS Platform

et al. 2020

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In the framework of indoor air monitoring, this paper proposes an Internet of Things ready solution to detect and classify contaminants. It is based on a compact and low-power integrated system including both sensing and processing capabilities. The sensing is composed of a sensor array on which electrical impedance measurements are performed through a microchip, named SENSIPLUS, while the processing phase is mainly based on Machine Learning techniques, embedded in a low power and low resources micro controller unit, for classification purposes. An extensive experimental campaign on different contaminants has been carried out and raw sensor data have been processed through a lightweight Multi Layer Perceptron for embedded implementation. More complex and computationally costly Deep Learning techniques, as Convolutional Neural Network and Long Short Term Memory, have been adopted as a reference for the validation of Multi Layer Perceptron performance. Results prove good classification capabilities, obtaining an accuracy greater than 75% in average. The obtained results, jointly with the reduced computational costs of the solution, highlight that this proposal is a proof of concept for a pervasive IoT air monitoring system.

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Section: Proposed Integrated System a Overviewmentioning

confidence: 99%

An End to End Indoor Air Monitoring System Based on Machine Learning and SENSIPLUS Platform

et al. 2020

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“…Activated carbon is a set of carbon derivatives with a high absorption capacity, highly crystalline, and highly developed internal porosity (Cerro et al, 2018). It is a material characterized by having a considerable amount of micropores (pores less than 2 nanometers radius), which is why it can have an area of 50 m 2 /g or more, reaching values of more than 2500 m 2 /g.…”

Section: Introductionmentioning

confidence: 99%

A Deep Neuronal Network-Based Sensor for the Detection of Oxygen inside the Carbonization Furnace

Sarmiento

2022

etj

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Agricultural production generates biological surpluses that can be used to produce additional products through thermal and/or chemical transformations, such as the carbonization of vegetables. The oxygen on the vegetable material during the carbonization process inside a furnace is an important parameter that determines not only the success of the process but also the quality of the final product. It is difficult to measure the oxygen inside the furnace in real time, partly because of the working environment of the sensor, and partly because of the operating characteristics of the furnace (continuous rotation on its axis). The goal of this project is to develop a reliable measurement system capable of operating in real-time. For the continuous and precise detection of the temperature inside the furnaces of a carbonization plant, we propose a method based on the characterization of the image inside the furnace using a deep neuronal network. First of all, the images of the interior of the furnace are captured through a digital camera in front of the material and the axis of rotation of the furnace. Then, the area of interest in each frame of the video is determined by image processing. Oxygen content information is then obtained using a deep model trained for the same furnace with pattern oxygen level sensors. Experiments conducted on actual operating conditions of the furnace demonstrate that the proposed method for estimating the working oxygen provides reliable data for the control of plant operation. The proposed measurement scheme demonstrated high reliability against the many changes present inside the furnace, also, its low computational consumption makes it a viable strategy for embedded implementation and operation in real-time.

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“…The filtering technologies so far assessed, such as the Activated Carbon Filters (ACFs), have been demonstrated to be reliable and effective in removing or reducing a large variety of contaminants from air and water [4]. This technology was also proven to be suitable for effective real-time monitoring during the use, as recently shown by the Authors [5]. However, ACFs present three weak points: (i) a limited range of adsorbed pollutants (e.g.…”

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confidence: 99%

“…Thanks to its outstanding properties [4], graphene has been proposed for the realization of new filters for pollution remediation [6], [7]. These filters have shown excellent adsorbing capacities also towards dyes, antibiotics, and heavy metals [8]- [12] and present very good performance if compared to the ACFs [5], [13].…”

mentioning

confidence: 99%

“…The latter technique has been recently assessed by the Authors and has been used to produce the pollutant filters adopted in this paper [15], [16]. In addition, the Authors have successfully presented a methodology based on the Electrical Impedance Spectroscopy (EIS), for the realtime monitoring of the State of Health (SoH) of both ACF filters [5], [17], and GNP ones [18].…”

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confidence: 99%

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Electrical Impedance Spectroscopy for Real-Time Monitoring of the Life Cycle of Graphene Nanoplatelets Filters for Some Organic Industrial Pollutants

Miele

Bellucci

Cataldo

et al. 2021

IEEE Trans. Instrum. Meas.

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The paper proposes an approach for a smart monitoring of the life-cycle of innovative graphene-based filters for water remediation in presence of pollutants. The measurement technique is based on suitable figures of merit that analyze the time variation of the electrical impedance frequency spectrum. The proposed study considers the remediation of two toxic industrial pollutants, such as the acetonitrile and the 2,4dichlorophenol. The contribution of the paper is twofold. The first is the demonstration of a reliable monitoring set-up that is able, for the selected use cases, to correlate in real-time the behavior of the electrical impedance of the filter to its status, defined as "absence of pollutants" and/or "saturation". The second contribution is the proposal of suitable figures of merit, based on measurement of the impedance frequency spectrum, able to increase the measurement sensitivity and the reliability and to mitigate some sources of uncertainty typically associated to these kinds of set-ups and measurements.Results show that the proposed graphene-based filters combine very good filtering capability and high sensitivity of the electrical impedance to the considered pollutants. These results suggest further investigations with other pollutants and the potential use of this technique for the predictive maintenance of the water filters in industrial applications, by endowing the graphene filters of smart sensing devices.

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Metrological Characterization of a Novel Microsensor Platform for Activated Carbon Filters Monitoring

Cited by 27 publications

References 13 publications

An End to End Indoor Air Monitoring System Based on Machine Learning and SENSIPLUS Platform

An End to End Indoor Air Monitoring System Based on Machine Learning and SENSIPLUS Platform

A Deep Neuronal Network-Based Sensor for the Detection of Oxygen inside the Carbonization Furnace

Electrical Impedance Spectroscopy for Real-Time Monitoring of the Life Cycle of Graphene Nanoplatelets Filters for Some Organic Industrial Pollutants

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