Research On Optimization Of Extraction Process Of Trailing Suction Dredger

Trailing suction dredger is one of the important ships in dredging operations.It has the functions of autonomous navigation,mud loading and mud unloading,and the advantages of high mobility and high anti-interference.With its remarkable advantages,it is widely used in many fields such as dredging channels and port excavation.With the rapid progress of artificial intelligence technology and the rapid development of equipment manufacturing industry,it is urgent to seize the opportunity to improve the digitalization and intelligence of the dredging industry,and intelligent ships and intelligent dredging have become the mainstream development direction of the industry.Compared with traditional construction operations,intelligent ships realize the comprehensive automation of dredging ships,significantly improve the efficiency of dredging,and change the disadvantages of construction personnel focusing on experience.Through the collection,analysis and optimization of construction parameters,an intelligent dredging mode is formed,so as to flexibly adjust construction parameters and select the most appropriate and economical operation mode.This thesis is based on the intelligent ship dredging project of Ministry of Industry and Information Technology,aiming at the pipeline mud and sand conveying process of the bank blowing in the suction dredger’s pumping chamber,the pipeline mud and sand conveying model is established to predict the mud and sand conveying characteristics online,and the relationship between sand particles and grading,pipeline resistance,critical flow rate of conveying mud and sand,and the method of obtaining the optimal conveying concentration are studied to complete the online optimization based on the pumping chamber model.Improve the efficiency of the overall pumping tank work.The main working points are as follows:(1)Research on the bank Blowing Mechanism Model of the suction cabin of a harrow suction dredger.Aiming at the pumping process of a harrow suction dredger,the mechanism of bank blowing is modeled and analyzed.The mud tank model and pumping tank pipeline model are established,and the key parameters such as mud pump,pumping tank pipeline,particle size and critical velocity are studied and analyzed based on the mathematical model.(2)Simulation study on mud and sand Transportation of dredger suction tank pipeline.Aiming at the safety problem of mud flow in the pumping chamber pipeline,the Fluent software in ANSYS was used to simulate the mud and sand under the condition of non-silting critical velocity,and the empirical formula of non-silting critical velocity was verified.In addition,the simulation is carried out according to specific working conditions to analyze the influence of particle size on sand concentration distribution,which provides theoretical guidance for the sand transportation process of pumping chamber pipeline.(3)Prediction of shore-blowing Yield of dredger Pumping chamber based on data drive.In view of the complicated mud and sand transportation in pumping chamber pipeline,it is impossible to obtain the instantaneous output of pumping chamber shoreblowing through pure mechanism model.The associated data of pumping chamber are acquired and analyzed,and the flow rate and concentration of mud and sand in pipeline are predicted by BP neural network combined with PSO and GA optimization algorithms,so as to obtain the output prediction model of pumping chamber shoreblowing.Experimental results show that GA-BP neural network model has less estimation error and can predict the output of pumping chamber better.driven method is adopted to predict the flow rate and concentration of mud in the pipeline through BP neural network combined with PSO and GA optimization algorithms,so as to obtain the output prediction model of pumping chamber shoreblowing.The experiment shows that GA-BP neural network model has less error and can predict the output of pumping chamber better.(4)Estimation and optimization of suction and shore blowing parameters for trailing suction dredgers based on EKF.In view of the actual mud and sand transportation process in the pumping tank pipeline,the parameters such as the discharge pressure of the mud pump,the pressure loss of the pipeline and the particle size are estimated through the extended Kalman filter method.Based on the mud pump pipeline model,design an average particle size estimator and a maximum allowable density optimizer to optimize the parameters with the goal of optimal transport concentration.The simulation results show that under the constraint conditions,the concentration of mud and sand in the pipeline reaches the optimal transport concentration,which improves the pumping efficiency of the trailing suction dredger.