Search

A Trailing Suction Hopper Dredger (TSHD) in the sailing process, excavating seabed sediment with the drag head and pumping it through the pipeline to the spoil hopper sedimentation. The spoil hopper sedimentation process, as a critical factor affecting the loading yield and efficiency of a dredger, is susceptible to the influence of different average sand grain diameter d m . However, the soil type tends to change continuously during the loading process, making it difficult to obtain the d m by measurement or calculation. In this paper, combined with the existing simplified one-dimensional sedimentation model and simulated loading data, the Bootstrap Particle Filter (BPF), Gaussian Particle Swarm Optimized Particle Filter (GPSO-PF), Assisted Particle Filter (APF), Continuous-Discrete Feedback Particle Filter based on the constant feedback gain approximation (CD-FPF-CG) and the Galerkin method feedback gain approximation (CD-FPF-GL) are applied in the numerical simulations for online estimation of important variables such as the average sand grain diameter d m , the sand bed height h s and mass m s , respectively. The simulations results show that the CD-FPF-CG has a higher numerical accuracy and efficiency than the BPF, GPSO-PF, APF and CD-FPF-GL. Therefore, the CD-FPF-CG is used to estimate the above important variables for the actual vessel and the initial value of the average sand grain diameter d m for the CD-FPF-CG is determined via Simulated Annealing Multiple Population Genetic Algorithm (SAMPGA). [ABSTRACT FROM AUTHOR]

Trailing suction hopper dredger is a kind of hydraulic dredger, it has the characteristics of self-propelled, selfloading, self-dredging, self-unloading, it is the main force in dredging and blowing works, it is widely used in the world, it can be said that where there is a big dredging project where there is a trailing suction hopper dredger's figure. The loading optimization process of trailing suction hopper dredger contains a lot of dredging parameters related to soil type, and the soil type under different working conditions is not very clear. In this study, we present a hybrid optimization technique based on simulated annealing and multi-population genetic algorithm to enhance the loading efficiency of a trailing suction hopper dredger and to examine the variation of dredged soil parameters. The soil parameters of the spoil hopper deposition model were estimated using this hybrid optimization algorithm. The experimental results show that the soil parameters are successfully estimated and verified by our measured construction data of a trailing suction hopper dredger. In addition, our proposed method has the highest accuracy of soil parameter estimation, the fastest algorithm convergence, and excellent robustness compared to the other three intelligent optimization methods. In addition, our method successfully avoids the phenomenon of premature convergence that usually occurs in traditional genetic algorithms, and the parameters show strong adaptability to different vessels under the same dredging area. [ABSTRACT FROM AUTHOR]