Ultrasonic and Impact-Echo Testing for the Detection of Scaling in Geothermal Pipelines
Source abstract
Abstract
Abstract The efficiency and longevity of components in geothermal plants are significantly reduced by mineral deposition in pipes, also known as scaling [1]. A number of methods are used to monitor fouling (a broader term for all types of organic and inorganic deposition) in other industries, such as the oil, gas and food industries. These include direct and indirect, offline, in-situ and online, as well as inline methods, each of which have pros and cons [2, 3]. An effective monitoring solution specific to the high temperature conditions in geothermal plants, which optimises maintenance and cleaning measures in dependency of scaling, has yet to be developed. In this paper, two non-destructive testing techniques: contact ultrasonic testing (UT) and impact-echo (IE) testing are investigated in their viability as methods to detect scaling growth in geothermal plants. A descaling measurement was conducted on a heavily scaled segment of an obsolete pipeline from the production well of the geothermal power plant in Sauerlach, Germany. The pipeline segment was inserted in a test rig and the scaling was gradually etched away using an acidic solution. At regular time intervals, the scaling thickness was measured mechanically and contact UT as well as IE measurements were carried out. The testing apparatus was designed to withstand high temperatures ($${140}\,\circ {\text {C}}$$at the inlet pipe surface) and to be easy to install whilst being cost-efficient. Both techniques yielded usable results with submillimeter resolution. The advantages and limitations of the two methods are discussed. Impact-echo testing, in particular, can be automated and presents a simple and cost-efficient scaling monitoring option.
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Research context
OpenAlex topics: Non-Destructive Testing Techniques · Ultrasonics and Acoustic Wave Propagation · Flow Measurement and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (29)
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