Effect of drag-reducing additives on particle collection performance in hydraulic collectors

IF 5.5 2区 工程技术 Q1 ENGINEERING, CIVIL
Xian Zhang , Qingqing Lu , Xuguang Chen , Chaoqun Liu , Shenpeng Tian , Xixi Liu
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引用次数: 0

Abstract

In hydraulic collectors, the properties of the jet fluid play a crucial role in both operational efficiency and nodule collection performance. To explore the relationship between fluid characteristics and the transport behavior of nodule particles, this study introduces a surfactant as a drag-reducing additives (DRA) into the jet fluid and investigates its effects on particle detachment and collection performance. The results show that under the same jet conditions, adding a DRA significantly reduces nodule collection time, and a DRA concentration of 1000 mg/L is a suitable choice based on overall considerations. Under the coupled effect of jet velocity and travel velocity, DRA enhances jet energy to improve nodule detachment efficiency and effectively reduces nodule aggregation caused by vortices, facilitating collection. In addition, the addition of DRA to the jet does not significantly promote the suspension of sediment particles. Finally, a neural network model optimized by a genetic algorithm is employed to further predict the nodule collection performance under DRA jetting conditions, providing a scientific basis for efficient and intelligent deep-sea mining.
减阻添加剂对液压集尘器颗粒收集性能的影响
在水力集热器中,射流特性对作业效率和集管性能起着至关重要的作用。为了探索流体特性与结核颗粒输运行为之间的关系,本研究将表面活性剂作为减阻添加剂(DRA)引入射流流体,并研究其对颗粒剥离和收集性能的影响。结果表明,在相同射流条件下,添加DRA可显著缩短结核收集时间,综合考虑,DRA浓度为1000 mg/L是较合适的选择。在射流速度和行进速度的耦合作用下,DRA增强射流能量,提高了结核脱离效率,有效减少了涡流引起的结核聚集,便于收集。此外,射流中DRA的加入对泥沙颗粒的悬浮没有显著的促进作用。最后,利用遗传算法优化的神经网络模型,进一步预测DRA射流条件下结核收集性能,为高效智能深海采矿提供科学依据。
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来源期刊
Ocean Engineering
Ocean Engineering 工程技术-工程:大洋
CiteScore
7.30
自引率
34.00%
发文量
2379
审稿时长
8.1 months
期刊介绍: Ocean Engineering provides a medium for the publication of original research and development work in the field of ocean engineering. Ocean Engineering seeks papers in the following topics.
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