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

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Moisture content monitoring in industrial-scale composting systems using low-cost sensor-based machine learning techniques
Bioresource Technology ( IF 9.7 ) Pub Date : 2022-06-11 , DOI: 10.1016/j.biortech.2022.127456
P C S Moncks ₁ , É K Corrêa ₂ , L L C Guidoni ₃ , R B Moncks ₄ , L B Corrêa ₂ , T Lucia ₅ , R M Araujo ₁ , A C Yamin ₁ , F S Marques ₁

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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.

中文翻译：

使用基于传感器的低成本机器学习技术监测工业规模堆肥系统中的水分含量

水分是正确堆肥的一个关键因素，可以提高效率并降低对环境的影响。低成本的实时水分测定方法仍然是工业堆肥过程中的一个挑战。本研究的目的是设计一种硬件和软件模型，允许低成本电容式湿度传感器的自我调节。使用具有不同废物成分和来自不同堆肥阶段的有机堆肥样品。机器学习技术用于传感器的自我调整。为了验证该模型，使用了在实验室通过重量分析法获得的结果。事实证明，所提出的模型在测量堆肥中的水分方面是高效可靠的，通过重量分析验证的水分含量与传感器节点获得的预测之间的相关系数达到0.9939。

更新日期：2022-06-11

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