• Seddon, Richard
• Koivisto, Joonas
• Ipiña, Jesus M. Lopez De
• Ipiña, Karmele Lopez De
• Florez, Sonia
• Costa, Anna
• Aznar, Gabriel
• Gazulla, Alejandro
• Lopez, Alberto
• Durałek, Paweł
• Vavouliotis, Antonios
• Koutsoukis, Grigorios
• Belosi, Franco

Abstract

<jats:title>Abstract</jats:title><jats:p>The EU-project ASINA is testing Low-Cost Particulate Matter Sensors (LCPMS) for industrial monitoring of the concentration of airborne particles, with the purpose of integrating this sensor technology within the data collection layer of Digital Twins (DTs) for manufacturing.</jats:p><jats:p>This paper shows the results of field performance evaluations carried out with five LCPMS from different manufacturers (<jats:italic>Alphasense OPC-N3, Plantower 9003, Sensirion SPS30, Sensirion SEN55 and Tera Sensor NetxPM</jats:italic>), during several field sampling campaigns, conducted in four pre-commercial and commercial pilot lines (PLs) that manufacture nano-enabled products, belonging to the ASINA and OASIS H2020 EU-projects [2,28]. Field tests consisted of deploying LCPMS in manufacturing process, measuring in parallel with collocated reference and informative instruments (OPS TSI 3330/CPC TSI 3007), to enable intercomparison.</jats:p><jats:p>The results show the complexity and differential response of the LCPMS depending on the characteristics of the monitored scenario (PL). Overall, they exhibit uneven precision and linearity and significant bias, so their use in industrial digital systems without proper calibration can lead to uncertain and biased measurements. In this sense, simple linear models are not able to capture the complexity of the problem (non-linear systems) and advanced calibration schemes (e.g. based on machine learning), applied “scenario by scenario” and in operating conditions as close as possible to the final application, are suggested to achieve reliable measurements with the LCPMS.</jats:p>

Topics

• impedance spectroscopy
• machine learning