MIIP001 Container Risk Early Warning system
Onboard prediction of the chance of a riskfull situation and proof of concept of a Container Risk Early Warning (CREW) system.
Container ships regularly lose several containers at sea. Although the direct damage due to the loss of containers may be limited (certainly in view of the total container transport and generally well-arranged cargo insurance), the environmental damage can be significant and long-lasting.
The consequence of this is clear with the incident with the MSC Zoe in January 2019.
The ship lost 342 containers over the Wadden Islands. Follow-up research has shown that these problems can occur not only with container ships such as the MSC Zoe (ULCC), but also with the smaller variants (Panamax) and container feeders. One of the recommendations from the MARIN report is:
”Good seamanship is essential to keep actual loads on cargo inside the limitations of the securing arrangements. However, there is at present no mandatory equipment on board to measure actual ship motions and accelerations. So ship crews do not always have means to relate actual vessel response to design points that are used in the lashing calculations. Also, the crew often doesn’t know the rule design values that were used in lashing calculations. It is therefore recommended to support the crews of containerships in a better way with the decision processes on board, so that they can recognize developing problems during operations and react”.
It is (probably) very difficult to properly recognize a risky situation in this context. At the ULCCs, the crew has, especially at night, poor visibility of what is happening on deck. In addition, the situation can deteriorate very quickly, for example in the case of parametric roll.
By combining the knowledge gained in the field of excessive ship movements with recent developments in the field of data science and machine learning, it is expected that the chance of excessive ship movements occurring, or the chance of exceeding movement limits , can be estimated. This would help the crew in recognizing a situation in time.
The aim of this project is to make a “proof of concept” for an onboard system that predicts the probability of a risky situation (excessive motions, seabed impact, greenwater).