Wu Chenning, Hutton Martin, Soleimani Manuchehr
Engineering Tomography Laboratory (ETL), Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK.
Ashridge Engineering Ltd, Okehampton EX20 1BQ, UK.
Sensors (Basel). 2019 Jul 10;19(14):3043. doi: 10.3390/s19143043.
Smart flow monitoring is critical for sewer system management. Obstructions and restrictions to flow in discharge pipes are common and costly. We propose the use of electrical resistance tomography modality for the task of smart wastewater metering. This paper presents the electronics hardware design and bespoke signal processing to create an embedded sensor for measuring flow rates and flow properties, such as constituent materials in sewage or grey water discharge pipes of diameters larger than 250 mm. The dedicated analogue signal conditioning module, zero-cross switching scheme, and real-time operating system enable the system to perform low-cost serial measurements while still providing the capability of real-time capturing. The system performance was evaluated via both stationary and dynamic experiments. A data acquisition speed of 14 frames per second (fps) was achieved with an overall signal to noise ratio of at least 59.54 dB. The smallest sample size reported was 0.04% of the domain size in stationary tests, illustrating good resolution. Movements have been successfully captured in dynamic tests, with a clear definition being achieved of objects in each reconstructed image, as well as a fine overall visualization of movement.
智能流量监测对于下水道系统管理至关重要。排放管道中的堵塞和流量限制很常见且成本高昂。我们建议使用电阻层析成像技术来完成智能废水计量任务。本文介绍了电子硬件设计和定制信号处理，以创建一个嵌入式传感器，用于测量直径大于250毫米的污水或灰水排放管道中的流速和流量特性，如组成材料。专用模拟信号调理模块、过零切换方案和实时操作系统使系统能够进行低成本的串行测量，同时仍具备实时捕获能力。通过静态和动态实验对系统性能进行了评估。实现了每秒14帧（fps）的数据采集速度，整体信噪比至少为59.54分贝。在静态测试中报告的最小样本大小为域大小的0.04%，显示出良好的分辨率。在动态测试中成功捕获了运动，每个重建图像中的物体都有清晰的定义，并且运动的整体可视化效果良好。
