Rapid and real-time detection of municipal sludge moisture content based on microwave reflection principle

Waste Disposal & Sustainable Energy Pub Date : 2024-01-22 DOI:10.1007/s42768-023-00179-7

Yan Zhang, Yanhong Jiao, Jun Li, Long Deng, Binqi Rao, Hao Xu, Peng Xu, Lijiang Hu, Chunping Li

{"title":"Rapid and real-time detection of municipal sludge moisture content based on microwave reflection principle","authors":"Yan Zhang, Yanhong Jiao, Jun Li, Long Deng, Binqi Rao, Hao Xu, Peng Xu, Lijiang Hu, Chunping Li","doi":"10.1007/s42768-023-00179-7","DOIUrl":null,"url":null,"abstract":"<div><p>The moisture content (MC) of municipal sludge is the key factor affecting sludge treatment and disposal technologies, while the vast majority of existing measurement methods are off-line and time-consuming. To realize rapid online detection for the MC of sludge, a detection method based on the microwave reflection principle is proposed: experiments are carried out and the MC computation model of the sludge is derived using the resonant frequency and the permittivity (<span>\\(\\varepsilon^{\\prime}\\)</span>). The results reveal that the detection accuracy of granular sludge with a thickness of 10 mm is higher. The theoretical model between the MC and the real part of <span>\\(\\varepsilon^{\\prime}\\)</span> is developed, and the relationship between the resonant frequency and <span>\\(\\varepsilon^{\\prime}\\)</span> is expressed by a cubic polynomial. The average error and the root mean square error (RMSE) of sludge are 2.06% and 2.49%, respectively. The prediction model for the MC of sludge is also given, and the determination coefficient and RMSE are 0.981 and 2.06%, respectively.</p><h3>Graphical abstract</h3>\n<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":807,"journal":{"name":"Waste Disposal & Sustainable Energy","volume":"6 2","pages":"259 - 269"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Waste Disposal & Sustainable Energy","FirstCategoryId":"6","ListUrlMain":"https://link.springer.com/article/10.1007/s42768-023-00179-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

The moisture content (MC) of municipal sludge is the key factor affecting sludge treatment and disposal technologies, while the vast majority of existing measurement methods are off-line and time-consuming. To realize rapid online detection for the MC of sludge, a detection method based on the microwave reflection principle is proposed: experiments are carried out and the MC computation model of the sludge is derived using the resonant frequency and the permittivity (\(\varepsilon^{\prime}\)). The results reveal that the detection accuracy of granular sludge with a thickness of 10 mm is higher. The theoretical model between the MC and the real part of \(\varepsilon^{\prime}\) is developed, and the relationship between the resonant frequency and \(\varepsilon^{\prime}\) is expressed by a cubic polynomial. The average error and the root mean square error (RMSE) of sludge are 2.06% and 2.49%, respectively. The prediction model for the MC of sludge is also given, and the determination coefficient and RMSE are 0.981 and 2.06%, respectively.

Graphical abstract

Abstract Image

查看原文本刊更多论文

基于微波反射原理的市政污泥含水率快速实时检测

市政污泥的含水率（MC）是影响污泥处理和处置技术的关键因素，而现有的测量方法绝大多数都是离线测量，耗时较长。为了实现污泥含水率的快速在线检测，本文提出了一种基于微波反射原理的检测方法：通过实验，利用共振频率和介电常数（(\(\varepsilon^{\prime}\)）推导出污泥的含水率计算模型。结果表明，厚度为 10 毫米的颗粒状污泥的检测精度更高。建立了 MC 与 \(\varepsilon^{prime}\)实部之间的理论模型，并用三次多项式表示了共振频率与 \(\varepsilon^{prime}\)之间的关系。污泥的平均误差和均方根误差（RMSE）分别为 2.06% 和 2.49%。同时给出了污泥 MC 的预测模型，其判定系数和均方根误差分别为 0.981% 和 2.06%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Waste Disposal & Sustainable Energy

自引率

0.00%

发文量