Artificial Intelligence (TSHD)

Monitoring and automation options for improving the efficiency of trailing suction hopper dredgers were impossible until recently. Now available, these options depend on the online availability of important but immeasurable process parameters, such as grain size of pumped material. These parameters can be successfully estimated using Artificial Intelligence (AI) algorithms.

• Increases loading/unloading efficiency by 10-15%
• Enhances uptime, and reduces fuel consumption and emissions
• Prevents water hammer, clogging pipelines and cavitation
• Estimated value presentation for failing sensors
• Greater peace of mind for operators

Applications and main features

• Trail Speed Controller (TSC): maintains constant ground speed of draghead and enhances suction efficiency.
• Automatic Visor Controller (AVC): governs draghead visor to optimise cutting and mixture-shaping properties.
• Overflow Loss Estimator (OLE): advises on decision to stop loading at best efficiency point (see ‘Monitoring System’).
• ECO Pump Controller (EPC): optimises dredge pump to the most efficient speed. 
• Sensor Diagnostics (SD): replaces failing sensor by estimated signals.

Backgrounds of AI applications

The AI algorithms apply comprehensive physical models in which signals from all available dredge process instrumentation are coherently processed and filtered by Kalman and other techniques.

Therefore, AI applications work as long as high-quality STPM, DLM, TDSS, GPS, production, pump pressure and torque measurements have been installed. The more AI applications that are installed, the higher the operational efficiency. It also results in greater peace of mind for operators. The need to interfere in the process considerably decreases.