There is a real-time flow of information from each vessel to the database regarding meter data, consisting but not limited to propulsion/hotelling procedures, fuelling data, particulate matter emissions, as well as emissions from other pollutants. Accumulation of this kind of data allows Canopus to provide on-line insights, by employing task-specific AI algorithms that are continuously trained and re-deployed. Complementary to these real-time suggestions, the extracted data are utilized from SeaQuest’s Data Scientists in order to provide periodical consultation services, as well as long term solutions. This combination of on-line and off-line services is the derivative to ensuring the successful optimization of the vessels’ operations related to any relevant parameters.
Last but not least, Canopus is built on the fundamental idea that each and every subsystem on the vessel is a link of a functional chain. Any deviation from the optimal state for a given link reduces the functionality of the whole chain, increasing the costs either directly (via performance loss) or indirectly (via increasing maintenance costs). This being the case, Canopus allows for swift assessments of the link responsible for the performance loss, while also ensuring that maintenance costs are kept to a minimum by employing a RCM procedure.
A particular case of asset maintenance concerns the scrubber towers recently installed aboard vessels in order to abide by the aforementioned regulations regarding emissions. Among the most commonly encountered complications in scrubber towers are thermal shocks, which lead to corrosion that gradually propagates across the system and eventually reaches the exhaust gas system. By continuously monitoring the scrubber towers’ steel condition, we are able to identify abrasions or other types of damage, thereby ensuring minimal repair costs and preventing their complete breakdown.