Moisture content monitoring in industrial-scale composting systems using low-cost sensor-based machine learning techniques.

Autor: Moncks PCS; PPGC, Programa de Pós-Graduação em Computação, CDTec, Centro de Desenvolvimento Tecnológico, Brazil., Corrêa ÉK; NEPERS, Centro de Engenharias, Brazil. Electronic address: ericokundecorrea@yahoo.com.br., L C Guidoni L; NEPERS, Centro de Engenharias, Brazil; PPGB, Programa de Pós-Graduação em Biotecnologia, Universidade Federal de Pelotas, Pelotas, RS, Brazil., Moncks RB; PPGI, Programa de Pós-Graduação em Inglês, Universidade Federal de Santa Catarina, Florianópolis, SC, Brazil., Corrêa LB; NEPERS, Centro de Engenharias, Brazil., Lucia T Jr; ReproPel, Faculdade de Veterinária, Brazil; PPGB, Programa de Pós-Graduação em Biotecnologia, Universidade Federal de Pelotas, Pelotas, RS, Brazil., Araujo RM; PPGC, Programa de Pós-Graduação em Computação, CDTec, Centro de Desenvolvimento Tecnológico, Brazil., Yamin AC; PPGC, Programa de Pós-Graduação em Computação, CDTec, Centro de Desenvolvimento Tecnológico, Brazil., Marques FS; PPGC, Programa de Pós-Graduação em Computação, CDTec, Centro de Desenvolvimento Tecnológico, Brazil.
Jazyk: angličtina
Zdroj: Bioresource technology [Bioresour Technol] 2022 Sep; Vol. 359, pp. 127456. Date of Electronic Publication: 2022 Jun 11.
DOI: 10.1016/j.biortech.2022.127456
Abstrakt: Moisture is a key aspect for proper composting, allowing greater efficiency and lower environmental impact. Low-cost real-time moisture determination methods are still a challenge in industrial composting processes. The aim of this study was to design a model of hardware and software that would allow self-adjustment of a low-cost capacitive moisture sensor. Samples of organic composts with distinct waste composition and from different composting stages were used. Machine learning techniques were applied for self-adjustment of the sensor. To validate the model, results obtained in a laboratory by the gravimetric method were used. The proposed model proved to be efficient and reliable in measuring moisture in compost, reaching a correlation coefficient of 0.9939 between the moisture content verified by gravimetric analysis and the prediction obtained by the Sensor Node.
(Copyright © 2022. Published by Elsevier Ltd.)
Databáze: MEDLINE
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