Research on the prediction method of yield in extraction process of trailing suction dredger

Jie Guo, Mengxi Yu, Bowen Zhou
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Abstract

In the process of pumping and shore blowing of trailing suction dredger, the mud and sand movement mechanism of mud tank and sludge discharge pipeline is complex and strongly coupled. It is difficult to obtain the relationship between mud transportation concentration and pumping hatch, mud pump, high-pressure flushing, submarine diversion valve and pipeline through mechanism analysis. Aiming at this problem, this paper proposes a prediction method of instantaneous output of trailing suction dredger pumping and bank blowing based on BP neural network. Through the training of historical construction data, PSO and GA algorithms are used to optimize respectively, and the prediction model of instantaneous output of trailing suction dredger pumping and bank blowing is established. The simulation results show that this method can effectively predict the production of mud from the suction dredger.
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耙吸式挖泥船抽采过程产量预测方法研究
在拖吸式挖泥船的抽运和吹岸过程中,泥槽与排泥管道的泥沙运动机理复杂且强耦合。通过机理分析,很难得到泥浆输送浓度与抽吸舱口、泥浆泵、高压冲洗、海底导流阀和管道之间的关系。针对这一问题,本文提出了一种基于BP神经网络的耙吸式挖泥船抽沙吹岸瞬时输出量预测方法。通过对历史施工数据的训练,分别采用粒子群算法和遗传算法进行优化,建立了耙吸式挖泥船抽水和吹岸瞬时输出的预测模型。仿真结果表明,该方法能有效地预测抽吸式挖泥船的出泥量。
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