堆肥
含水量
重量分析
工艺工程
水分
环境科学
电容感应
制浆造纸工业
废物管理
计算机科学
工程类
材料科学
化学
岩土工程
复合材料
有机化学
操作系统
作者
P C S Moncks,É K Corrêa,L L C Guidoni,R B Moncks,L B Corrêa,T Lucia,R M Araujo,A C Yamin,F S Marques
标识
DOI:10.1016/j.biortech.2022.127456
摘要
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.
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