Prediction of Two-phase Taylor Flow Characteristics in a Rectangular Micro-channel

Lee, Jun Kyoung; Lee, Kwan Geun

doi:10.3795/ksme-b.2015.39.7.557

Cited by 1 publication

(1 citation statement)

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“…When matters such as gas and air go through the ultrasonic sensor of an existing sludge density meter, the measured value can be overstated or can fluctuate like a flow measurement [ 7 , 8 ]. When a sludge feeding pump is operated, initially the sludge suddenly moves in a congested state, thus, time is needed for it to stabilize and to get an exact value.…”

Section: Introductionmentioning

confidence: 99%

An Ultrasonic Multi-Beam Concentration Meter with a Neuro-Fuzzy Algorithm for Water Treatment Plants

Lee

Jang

Shin

et al. 2015

Sensors

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Ultrasonic concentration meters have widely been used at water purification, sewage treatment and waste water treatment plants to sort and transfer high concentration sludges and to control the amount of chemical dosage. When an unusual substance is contained in the sludge, however, the attenuation of ultrasonic waves could be increased or not be transmitted to the receiver. In this case, the value measured by a concentration meter is higher than the actual density value or vibration. As well, it is difficult to automate the residuals treatment process according to the various problems such as sludge attachment or sensor failure. An ultrasonic multi-beam concentration sensor was considered to solve these problems, but an abnormal concentration value of a specific ultrasonic beam degrades the accuracy of the entire measurement in case of using a conventional arithmetic mean for all measurement values, so this paper proposes a method to improve the accuracy of the sludge concentration determination by choosing reliable sensor values and applying a neuro-fuzzy learning algorithm. The newly developed meter is proven to render useful results from a variety of experiments on a real water treatment plant.

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Section: Introductionmentioning

confidence: 99%