Polymers, coke, oil dirt, limescale, sediments and rust corrosives, which are adhered to pipe wall, will be generated during the oil pipeline transport, thus leading to degradation of production efficiency, increasing the energy consumption and seriously impacting oil transport quality, efficiency and safety. What’s worse, this will result in blockage and thus interrupt the flow and suspend the production. Based on modern artificial intelligence (AI) and new-type environmental protection technologies, a self-propelled robot of intelligent manipulation is proposed and enters oil pipeline to conduct environmentally friendly cleaning using high-pressure water jet, and the cleaning wastewater can be recycled. This environmentally friendly intelligent pipeline robot is applicable to detection and high-pressure water jet cleaning of circular variable-sectional-diameter pipelines in closed environment. Furthermore, it is capable of online pipe wall quality detection for horizontal, vertical and turning circular pipelines, and also cleaning effect evaluation. According to the operating principle, an environmentally friendly oil pipeline cleaning technology based on intelligent robot is proposed by combining the technical characteristics in the existing oil pipeline design and construction, in an effort to provide a reference for intelligent cleaning and automatic visual inspection and evaluation of oil pipelines.
Ultra-high-performance concrete with coarse aggregate (UHPC-CA) is a newly developed material. To explore the influence of aggregate volume fraction and maximum aggregate particle size on the compressive strength of UHPC-CA, a batch of UHPC-CA numerical samples as well as some experiments is carried out. The phase-field method is introduced to simulate the uniaxial compressive strength, and the simulation results show good agreement with experimental results. Both the simulation results and experiment results show that the compressive strength decreases with the aggregate volume fraction and increases with the maximum aggregate particle size. A size effect model of UHPC-CA is established by using its aggregate volume fraction and the maximum aggregate particle size.
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