Prediction of mud density of cutter suction dredger based on neural network optimized by SSA

Xu Yao; Menghong Yu

doi:10.1117/12.2687031

10 August 2023 Prediction of mud density of cutter suction dredger based on neural network optimized by SSA

Xu Yao, Menghong Yu

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127592X (2023) https://doi.org/10.1117/12.2687031
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

During the actual construction of a cutter suction dredger, the control of mud density and flow rate in the mud pipeline are two important parameters that affect the dredging output and the safe transportation of mud. Mud density is affected by many factors such as the speed of the cutter, the speed of the cutter and the speed of the submerged pump. Real-time prediction and control of mud density are important means to achieve efficient and safe dredging. Using actual construction data, an optimization method based on Sparrow Search Algorithm (SSA) is proposed to predict mud density. LSTM and ELMAN models are established and compared with the optimized SSA-LSTM and SSA-ELMAN models. The results show that the SSA optimization method can predict the density of cutter suction dredger with high prediction accuracy and stability.

Citation Download Citation

Xu Yao and Menghong Yu "Prediction of mud density of cutter suction dredger based on neural network optimized by SSA", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127592X (10 August 2023); https://doi.org/10.1117/12.2687031

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