TSHD Loading System Optimization And Research

The modern TSHD is a complex system with several automation systems. These automatic systems can be controlled in the cab by integrated computer system. Operators need to set the correct settings for these systems. The decision-making information which is needed by settings is not readily available.The paper studied the two most important dredging processes in the TSHD, which is occupies an important position in the dredging industry--mud pump pipeline process and deposition process hopper. It established models such as soil parameters of mud pump pipeline model, deposition model and the overflow density model which is used for estimating overflow losses. And verified these models were correct. It introduced the parameter estimation method based on theory of optimization. It estimated and measured the parameters in the model through the measured data. It estimated the parameters about soil online or offline. The mud pump pipeline model estimated the solid effect on the pump behavior; which depends on the grain size diameter of the sand in the mixture. In the hopper sedimentation models, it estimated the density of sand bed in the hopper, undisturbed settling velocity. The online estimation used recursive least squares method, the offline estimation used pattern search method. The paper proposed the variance and squares to estimate accuracy and applicability of models.The research of the article made the dredging process from black box to white box. It could provide the necessary information in decision-making for the operator, and it provided possibility for designing a predictive control system based on models. In order to reduce the overflow losses, shorten the dredging cycle, increase productivity, we adjust the parameters on the soil to achieve optimal performance on the dredging.