System and method for optimizing dredging overflow efficiency of trailing suction dredger

Abstract

The invention provides a system and a method for optimizing dredging overflow efficiency of a trailing suction dredger, and relates to the technical field of dredging engineering equipment.The system comprises a sensor group, a control module, a driving module and a man-machine interaction unit, the sensor group is used for monitoring data of overflow density, dredge pump pressure, in-cabin liquid level and overflow cylinder height in real time, and the control module is used for controlling the driving module; historical ship number data are fused to construct a dynamic model; the control module is used for realizing an overflow control algorithm which comprises an overflow segmentation strategy and overflow cylinder height PID control; the driving module is used for controlling the electric adjusting valve, the frequency converter and the electromagnetic valve set and adjusting overflow parameters in real time. By means of intelligent dynamic adjustment and multi-parameter cooperative control, the sand storage rate can be increased, the earthwork loss rate can be reduced, and the problems that parameter adjustment lags behind and the intelligent level is insufficient are solved.

Description

Technical Field

[0001] This invention relates to the field of dredging equipment technology, and more specifically, to a system and method for optimizing the dredging overflow efficiency of a trailing suction hopper dredger. Background Technology

[0002] A trailing suction hopper dredger is a large, efficient, self-propelled dredger. It is mainly used for dredging operations along the coast and deep-water channels. Its core working principle is as follows: During navigation, the dredger, equipped with a rake head, contacts the seabed mud surface via a rake arm (lowered from the stern or side). A high-powered mud pump on board creates a vacuum, drawing the mud and sand mixture from the seabed into a large mud tank (mud bin) inside the hull through the rake arm and suction pipe. The mud slurry settles in the mud bin, allowing solid particles to settle, while excess water is discharged. Once the mud bin is full, the dredger navigates to a designated dumping area, where the mud is discharged through a valve at the bottom of the hull or by using the mud pump again.

[0003] Existing dredging vessels, especially trailing suction hopper dredgers, have the following defects during the overflow phase: the traditional overflow weir height is fixed and cannot be dynamically adjusted according to the concentration and soil quality inside the hopper, resulting in a decrease in the ratio of dry soil increment to overflow soil volume. The separation efficiency of the mixture (water and mud) during the overflow process is insufficient, and the effective soil loss is serious. The reliance on manual experience to adjust parameters such as mud pump speed and flushing pump pressure results in a slow response speed. Furthermore, the lack of a dynamic optimization model based on real-time data makes it impossible to achieve automated and precise overflow control, thus resulting in problems such as fixed parameters, poor adaptability, and high maintenance costs. Summary of the Invention

[0004] The purpose of this invention is to address the problems of fixed parameters, poor adaptability, and high maintenance costs in existing trailing suction hopper dredger overflow efficiency optimization systems.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0006] A system for optimizing the dredging overflow efficiency of a trailing suction hopper dredger includes a sensor array for real-time monitoring of overflow density, sludge pump pressure, liquid level inside the hopper, and overflow cylinder height, and integrates historical dredging data to construct a dynamic model.

[0007] The control module is used to implement the overflow control algorithm, including the overflow segmentation strategy and the overflow cylinder height PID control;

[0008] The drive module is used to control the electric regulating valve, frequency converter, and solenoid valve group to adjust the overflow parameters in real time.

[0009] The human-computer interaction unit is used to display data parameters and alarms.

[0010] As a preferred technical solution of this application, the sensor group includes a mud density meter for monitoring overflow density, a pressure sensor for monitoring mud pressure, a liquid level sensor for monitoring the liquid level height inside the chamber, and a position sensor for monitoring the height of the overflow cylinder.

[0011] As a preferred technical solution of this application, the control module includes an embedded controller for running a real-time operating system, a storage unit for storing historical overflow data, and a communication interface for data interaction with PLC, HMI and cloud platform.

[0012] As a preferred technical solution of this application, the drive module includes an electric regulating valve for controlling the height of the overflow cylinder, a frequency converter for driving the mud pump and the flushing pump, and a solenoid valve group for loading and switching the overflow pipeline.

[0013] As a preferred technical solution of this application, the human-computer interaction unit includes a touch screen for displaying data parameters and an alarm indicator light for issuing alarms.

[0014] This application also discloses a method for optimizing the dredging overflow efficiency of a trailing suction hopper dredger, applied to the aforementioned trailing suction hopper dredger dredging overflow efficiency optimization system, comprising the following steps:

[0015] The sensor array collects data in real time on overflow density, mud pump pressure, tank liquid level and overflow cylinder height, and integrates historical ship data to build a dynamic model.

[0016] Based on the rate of change of liquid level in the chamber and the operating status of the mud pump, the system automatically determines the start and stop of the overflow stage.

[0017] First, optimize the sand storage rate: adjust the overflow cylinder height using a PID algorithm; then, suppress soil loss: adjust the mud pump speed and flushing pump pressure based on fuzzy control theory.

[0018] The optimization instructions are sent to the electric regulating valve and frequency converter to adjust the overflow parameters in real time, monitor the status of the actuator, and form a closed-loop feedback.

[0019] As a preferred technical solution of this application, the core algorithm of the dredging overflow efficiency optimization method for trailing suction hopper dredgers includes a sand storage rate prediction model, a segmented strategy control algorithm, and a collaborative optimization strategy for mud pumps and flushing pumps. The calculation formula for the sand storage rate prediction is as follows:

[0020]

[0021] As a preferred technical solution of this application, the segmented strategy control algorithm includes rapid classification of working conditions, coarse-grained control instructions, and safety boundary protection.

[0022] As a preferred technical solution of this application, the calculation formula for the mud pump speed control is as follows:

[0023]

[0024] The calculation formula for regulating the flushing pump pressure is as follows:

[0025]

[0026] As a preferred technical solution of this application, the overflow cylinder height adjustment strategy adopts a hierarchical control architecture that combines segmented strategy and PID control, specifically including segmented strategy as the main component, PID fine adjustment, and a collaborative mechanism.

[0027] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0028] By using a set of sensors to collect data on overflow density, mud pump pressure, tank liquid level, and overflow cylinder height in real time, and integrating historical ship data to build a dynamic model, the overflow stage start and stop can be automatically determined based on the tank liquid level change rate and mud pump operating status. First, the overflow cylinder height is adjusted through a PID algorithm, and then the mud pump speed and flushing pump pressure are adjusted based on fuzzy control theory. The optimization instructions are sent to the electric regulating valve and frequency converter to adjust the overflow parameters in real time and monitor the status of the actuators, forming a closed-loop feedback. This can improve sand storage rate, reduce soil loss rate, and avoid the problems of parameter adjustment lag and insufficient intelligence level. Attached Figure Description

[0029] Figure 1 This is a system structure block diagram of the present invention;

[0030] Figure 2 This is a flowchart of the dredging overflow efficiency optimization method of the present invention;

[0031] Figure 3 This is a flowchart of the overflow control process of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0033] like Figures 1 to 3As shown, this embodiment proposes a dredging overflow efficiency optimization system for trailing suction hopper dredgers, including a sensor group, a control module, a drive module, and a human-machine interface unit. The sensor group is used to monitor overflow density, sludge pump pressure, tank liquid level, and overflow cylinder height in real time, and integrates historical dredging data to construct a dynamic model. The control module implements overflow control algorithms, including overflow segmentation strategies and overflow cylinder height PID control. The drive module controls electric regulating valves, frequency converters, and solenoid valve groups to adjust overflow parameters in real time. The human-machine interface unit displays data parameters and alarms. Through intelligent dynamic adjustment and multi-parameter collaborative control, this system solves the problems of low sand storage rate, high soil loss rate, delayed parameter adjustment, and insufficient intelligence level in the overflow stage of existing dredging vessels.

[0034] Specifically, the sensor group includes a mud density meter for monitoring overflow density, a pressure sensor for monitoring mud pressure, a liquid level sensor for monitoring the liquid level in the chamber, and a position sensor for monitoring the height of the overflow cylinder.

[0035] The mud density meter can use an ultrasonic density sensor, the pressure sensor can use a miniature pressure transmitter, and the level sensor can use a radar level gauge. The ultrasonic density sensor is installed at the overflow outlet to monitor the overflow density in real time. The miniature pressure transmitter is installed at the mud pump outlet and the overflow inlet to monitor the mud pressure. The radar level gauge is installed on the inner wall of the mud tank to monitor the liquid level in real time. Finally, the position sensor is installed on the outside of the overflow tank to monitor the overflow height in real time. This completes the real-time monitoring of overflow density, mud pump pressure, liquid level in the tank, and overflow height.

[0036] Accurate and reliable data sensing is the foundation of all automatic control. Dredging overflow processes involve complex conditions such as solid-liquid two-phase flow and dynamic settling. Traditional single or low-precision sensors cannot provide stable, multi-dimensional real-time data, leading to a "blind control" state for the control system. This invention integrates a sensor network composed of multiple high-performance sensors to capture key physical parameters of the overflow process comprehensively and with high precision. This provides accurate and reliable input for upper-level intelligent control algorithms, forming the "sensory nerves" of the entire optimization system.

[0037] The control module includes an embedded controller for running a real-time operating system, a storage unit for storing historical overflow data, and a communication interface for data interaction with PLC, HMI, and cloud platform.

[0038] The embedded controller can be configured to run a real-time operating system based on an industrial-grade controller with an ARM Cortex-M7 architecture (such as the STM32H7 series). The storage unit can be an eMMC memory, which can store historical overflow data (≥1000 ship records). In addition, the communication interface can support RS485 and EtherCAT protocols and interact with PLCs, HMIs and cloud platforms.

[0039] To implement the aforementioned advanced closed-loop control algorithms (PID, fuzzy control) and meet the demands of industrial sites for reliability, real-time performance, and data interconnection, a powerful hardware platform is required. Traditional solutions using a single PLC or industrial computer have limitations in complex calculations, data persistence, and open interconnection. This invention designs a dedicated intelligent control module integrating an embedded real-time system, edge storage, and multi-protocol communication. This module serves as the core decision-making unit of the entire overflow optimization system, ensuring the accurate and stable operation of the control algorithm and providing support for data traceability and remote interaction.

[0040] The drive module includes an electric regulating valve for controlling the height of the overflow cylinder, a frequency converter for driving the mud pump and the flushing pump, and a solenoid valve group for loading and switching the overflow pipeline.

[0041] The electric regulating valve has a DN100 diameter and an adjustment accuracy of ±0.5°, used to control the height of the overflow cylinder (adjustment range 0.5-3.5m); the frequency converter belongs to the ABB ACS580 series and can be used to drive mud pumps and flushing pumps, with a frequency adjustment range of 20-60Hz; the solenoid valve group is a 4-way 2-position 3-way valve, which can realize rapid switching between the loading and overflow pipelines (response time ≤200ms).

[0042] The optimized instructions generated by intelligent control algorithms require high-precision and high-reliability actuators for implementation. Traditional dredging systems suffer from slow response, low control precision, and low automation, failing to meet the requirements of modern efficient dredging operations for "precise control and rapid response" of the overflow process. This invention integrates a high-performance drive module consisting of an electric regulating valve, a frequency converter, and a solenoid valve assembly. This module acts as the "hands and feet" of the control system, precisely executing various operational instructions to achieve rapid and accurate control of the overflow cylinder height, pump speed, and process pipeline status.

[0043] The human-computer interaction unit includes a touch screen for displaying data parameters and an alarm indicator light for issuing alarms;

[0044] The touchscreen uses a 10-inch industrial-grade HMI, which can display real-time data and process parameter recommendations; while the alarm indicator has red / yellow / green LEDs to indicate the overflow status. It should be noted that the red light represents an abnormal state, the yellow light represents a warning state, and the green light represents a normal state.

[0045] In highly automated dredging operations, operators require a centralized and intuitive interface to monitor system status, obtain critical information, and receive rapid alerts when anomalies occur. Traditional dashboards and scattered indicator lights are redundant and inefficient. This invention designs a human-machine interface unit integrating a high-resolution touchscreen and multi-functional alarm indicator lights, serving as a bridge between the intelligent control system and on-site operators. This enables transparent monitoring of system status and efficient, accurate alarm management, improving operational safety and decision-making efficiency.

[0046] like Figures 1 to 3 As shown, this application also discloses a method M10 for optimizing the dredging overflow efficiency of a trailing suction hopper dredger, which is applied to the trailing suction hopper dredger dredging overflow efficiency optimization system mentioned in any of the above embodiments, and the method M10 includes S100, S200, S300 and S400.

[0047] Specifically, S100: Real-time data on overflow density, mud pump pressure, tank liquid level, and overflow cylinder height are collected through a sensor array, and a dynamic model is constructed by integrating historical ship data.

[0048] First, data acquisition and fusion are performed. Sensors collect real-time data on overflow density, mud pump pressure, and tank level, and this data is then fused with historical vessel data (≥20 times) to construct a dynamic model. Traditional dredging operations rely on manual experience to judge overflow status, resulting in inaccurate overflow density control and high sediment loss rates. This invention proposes a multi-sensor fusion and dynamic modeling method. By collecting key operational parameters in real-time and fusing historical vessel data, an adaptive overflow operation model is constructed, achieving an overflow density control accuracy of ±0.05 g / cm³. 3 The sediment loss rate has been reduced by more than 15%.

[0049] S200: Automatically determines the start and stop of overflow stage based on the rate of change of liquid level in the tank and the operating status of the mud pump;

[0050] The overflow stage is determined automatically based on the rate of change of the liquid level in the dredging chamber and the operating status of the dredging pump (determination time ≤ 1 second). In traditional dredging operations, the determination of the start and stop of the overflow stage often relies on manual experience or fixed liquid level thresholds, which is difficult to adapt to complex working conditions (such as fluctuations in sediment concentration and changes in dredging pump power), resulting in overflows that occur too early or too late, causing a decrease in production efficiency or sediment loss. This invention proposes a collaborative determination mechanism based on the real-time rate of change of liquid level and the operating status of the dredging pump (such as pump speed and power) to achieve precise automatic start and stop of the overflow stage (determination time ≤ 1 second), minimizing sediment loss while ensuring overflow efficiency.

[0051] S300: First, optimize sand storage rate: adjust overflow height through PID algorithm, then suppress soil loss: adjust mud pump speed and flushing pump pressure based on fuzzy control theory;

[0052] During the overflow phase of dredging operations, a fixed overflow cylinder height cannot cope with the dynamic changes in material concentration and flow rate, resulting in large fluctuations in sand storage rate and low production efficiency. Traditional manual adjustment suffers from problems such as response lag and insufficient precision. This invention introduces real-time sand storage rate feedback and a PID (proportional-integral-derivative) control algorithm to dynamically adjust the overflow cylinder height, stabilizing the sand storage rate at a level ≥92% of the target value, effectively reducing the loss of fine-particle sediment and improving harvesting efficiency.

[0053] In dredging operations, the overflow soil loss rate is a key indicator for measuring economic benefits and environmental impact. Traditional control methods, which use independent PID or empirical rules to adjust the mud pump speed or flush pump pressure, struggle to cope with the nonlinear and time-delay challenges brought about by drastic changes in the characteristics of dredged materials (such as viscosity and particle size), resulting in persistently high soil loss rates. This invention introduces multivariate fuzzy control theory, dynamically and collaboratively adjusting the mud pump speed and flush pump pressure by simulating expert operating experience, to intelligently suppress the overflow soil rate and ensure its stability of ≤3%.

[0054] S400: Sends optimization commands to the electric regulating valve and frequency converter, adjusts overflow parameters in real time, monitors the status of the actuator, and forms a closed-loop feedback;

[0055] If the optimization commands generated by advanced control algorithms (such as PID and fuzzy control) cannot be reliably and accurately sent to the actuators, and their states cannot be effectively monitored, the entire system will become an open-loop control, failing to achieve the expected optimization goals. Traditional systems suffer from problems such as command transmission delays, actuator response deviations, and missing state feedback, leading to decreased control accuracy. This invention designs a highly reliable closed-loop link for command issuance and state monitoring, ensuring that optimization commands are faithfully executed and that the execution results are fed back to the controller in real time, forming a closed-loop adaptive adjustment to guarantee the stability and accuracy of the entire overflow optimization system.

[0056] The core algorithm of the method for optimizing the dredging overflow efficiency of trailing suction hopper dredgers includes a sand storage rate prediction model, a segmented strategy control algorithm, and a collaborative optimization strategy for mud pumps and flushing pumps. The calculation formula for the sand storage rate prediction is as follows:

[0057]

[0058] In the formula:

[0059] SR: Sand storage rate, reflecting the rate of soil loss.

[0060] ρ in Density of mud entering the chamber (t / m³) 3 )

[0061] ρ out Overflow mud density (t / m³) 3 )

[0062] Q in Inlet flow rate (m³) 3 / s)

[0063] Q out Overflow flow rate (m³) 3 / s)

[0064] t1: Overflow start time (s)

[0065] t2: Overflow end time (s).

[0066] It should be noted that the segmented strategy control algorithm is as follows: In the overflow height control system, the segmented strategy is a coarse-grained control logic based on rules or experience. It directly generates instructions to "raise, lower, or maintain" the overflow height based on preset operating conditions (such as concentration, density, liquid level, etc.). Its core purpose is to reduce the frequent adjustments of the PID controller, improve the system response speed, and provide initial direction for subsequent fine-tuning.

[0067] In addition, the segmented strategy control algorithm includes rapid working condition classification, coarse-grained control commands, and safety boundary protection;

[0068] ① Rapid classification of operating conditions: Based on real-time sensor data (such as concentration, density, liquid level), the overflow stage is divided into several typical operating conditions (such as "high concentration and high density" and "low concentration and low density"), and a fixed overflow cylinder adjustment direction is assigned to each operating condition.

[0069] ② Coarse-grained control instructions: Directly output three types of instructions: "raise, lower, or maintain", avoiding repeated fine-tuning of the PID controller under complex operating conditions and reducing the computational burden on the system;

[0070] ③ Safety boundary protection: Combining physical constraints (such as upper / lower limits of overflow cylinder height) and process requirements (such as minimum liquid level limits) to prevent adjustment actions from exceeding the safe range.

[0071] Furthermore, the typical rules of the segmentation strategy are as follows:

[0072] When the concentration entering the chamber is >1.5t / m³ 3 Overflow density > 2.2 t / m³ 3 At this time, the overflow cylinder adjustment command is to raise the overflow cylinder, which can suppress the mud from flowing out too quickly and improve the sand storage rate;

[0073] When the inlet concentration is <1.2t / m³ 3 Overflow density <1.8t / m³ 3 At this time, the overflow adjustment command is to lower the overflow cylinder, which can accelerate the overflow speed and avoid mud retention;

[0074] When the concentration entering the chamber is >1.5t / m³ 3 Overflow density > 2.2 t / m³ 3 At this time, the overflow cylinder adjustment command is to maintain the current height, which can maintain a stable overflow state;

[0075] When the liquid level is <5m (the value varies depending on the dredger), the overflow cylinder adjustment command is to prohibit lowering the overflow cylinder, which can prevent mud from overflowing or the dredger from emptying.

[0076] The collaborative relationship between the segmented strategy and the PID controller is as follows:

[0077] ① Division of labor and cooperation

[0078] Segmentation strategy: Responsible for "strategic decisions" (such as "the overflow pipe should be raised now").

[0079] PID controller: responsible for "tactical execution" (such as "raising the overflow tube at a rate of 0.05m per second").

[0080] ② Complementary advantages

[0081] Segmented strategy: Quickly respond to changes in macroscopic operating conditions and reduce the adjustment frequency of PID.

[0082] PID controller: Eliminates static errors of segmented strategies and achieves fine-tuning.

[0083] ③ Practical application scenarios

[0084] High-concentration mud: A segmented strategy is used to directly raise the overflow cylinder, and PID fine-tuning is used to avoid excessive raising.

[0085] Low liquid level conditions: The segmented strategy prohibits lowering the overflow cylinder, and the PID maintains the current height to prevent empty tanks.

[0086] It is worth mentioning that the advantages of the segmentation strategy are as follows:

[0087] To improve response speed, under complex operating conditions (such as sudden changes in mud concentration), the segmented strategy can output commands immediately, avoiding PID lag.

[0088] Reduce control complexity and decrease the real-time calculation burden on PID controllers for multi-variable coupling (such as concentration, density, and liquid level).

[0089] Enhance robustness by covering typical operating conditions through preset rules, thereby reducing the impact of abnormal scenarios (such as sensor noise) on the system.

[0090] Therefore, it can be concluded that the segmented strategy is the "intelligent navigation" of the overflow pipe height control system. Through rule-based decision-making, it quickly locates the adjustment direction and provides clear initial instructions to the PID controller. Its collaborative working mode with the PID controller ensures both system response speed and fine-tuning, making it a key technology for balancing efficiency and accuracy in dredging projects.

[0091] Furthermore, the collaborative optimization strategy for mud pumps and flushing pumps is as follows:

[0092] Based on multivariate coupling analysis, the mud pump speed and flushing pump pressure are dynamically adjusted. The calculation formula for the mud pump speed control is as follows:

[0093]

[0094] In the formula:

[0095] f pump Mud pump speed (rpm / min)

[0096] ρ in : Concentration of mud entering the chamber (t / m³) 3 )

[0097] Rate of change of liquid level in the tank (m / s)

[0098] H: Overflow cylinder height (m)

[0099] α, β, γ: Weighting coefficients, regression coefficients calibrated based on historical data, used to balance the influence weights of each variable on the rotational speed.

[0100] The calculation formula for regulating the flushing pump pressure is as follows:

[0101]

[0102] In the formula:

[0103] P flush Flushing pump pressure (MPa)

[0104] ρ out Overflow mud density (t / m³) 3 )

[0105] P max Maximum flushing pressure (MPa)

[0106] P min Minimum flushing pressure (MPa).

[0107] The overflow cylinder height adjustment strategy adopts a hierarchical control architecture that combines segmented strategy and PID control, specifically including segmented strategy as the main force, PID fine adjustment, and a collaborative mechanism.

[0108] The overflow cylinder height adjustment strategy adopts a hierarchical control architecture that combines a segmented strategy with PID control, as detailed below:

[0109] (1) Segmentation strategy is dominant

[0110] Based on real-time sensor data (concentration, density, liquid level, etc.), overflow conditions are classified into typical categories such as "high concentration and high density" and "low concentration and low density," with preset fixed adjustment directions (raise / lower / maintain). Physical constraints (such as prohibiting cylinder lowering when liquid level is <5m) and process limitations (overflow timeout triggers shutdown) are embedded to prevent adjustment actions from exceeding safe ranges. Millisecond-level condition determination is achieved through a rule engine, reducing the frequent calculations of the PID controller under complex conditions.

[0111] (2) PID fine-tuning

[0112] With the goal of improving sand storage rate, the precise movement of the overflow cylinder (e.g., ±0.05m / s) is calculated using a PID algorithm based on the adjustment direction output by the segmented strategy (e.g., "raising"). This eliminates the static error of the segmented strategy and, in conjunction with parameters such as mud pump speed and flushing pump pressure, corrects the height deviation in real time to ensure a stable overflow process.

[0113] (3) Collaboration Mechanism

[0114] The segmented strategy is responsible for macro-level decision-making ("strategic layer"), while the PID is responsible for micro-level action execution ("tactical layer"), forming a "decision-execution" closed loop; it shares sensor data and historical records, and optimizes and adjusts parameters through dynamic models, balancing efficiency and accuracy.

[0115] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described herein. Although the present invention has been described in detail with reference to the above embodiments, the present invention is not limited to the specific embodiments described above. Therefore, any modifications or equivalent substitutions to the present invention, as well as all technical solutions and improvements that do not depart from the spirit and scope of the invention, are covered within the scope of the claims of the present invention.

Claims

1. A system for optimizing dredge overflow efficiency of a trailing suction hopper dredger, characterized in that, The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger.

2. The trailing suction hopper dredger efficiency optimization system according to claim 1, characterized in that, The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger.

3. The trailing suction hopper dredge efficiency optimization system of claim 1, wherein, The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger.

4. The trailing suction hopper dredge efficiency optimization system of claim 1, wherein, The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger.

5. The trailing suction hopper dredge efficiency optimization system of claim 1, wherein, The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger.

6. The method of optimizing the dredging overflow efficiency of a trailing suction hopper dredger as claimed in any one of the claims 1-5, applied to the system for optimizing the dredging overflow efficiency of a trailing suction hopper dredger as claimed in any one of the claims 1-5, characterized in that, The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger.

7. The trailing suction hopper dredger dredging overflow efficiency optimization method according to claim 6, characterized in that, The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger.

8. The trailing suction hopper dredger dredging overflow efficiency optimization method according to claim 7, characterized in that, The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger.

9. The trailing suction hopper dredger dredging overflow efficiency optimization method according to claim 6, characterized in that, The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger.

10. The trailing suction hopper dredge vessel dredging overflow efficiency optimization method of claim 6, wherein, The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. The application relates to a dredging overflow efficiency optimization method for a cutter suction dredger. 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