Autonomous Cargo Handling on Oil and Chemical Tankers

Autonomous Cargo Handling on Oil and Chemical Tankers: A concept study

Cargo handling operations on oil and chemical tanker vessels, vessel-to-vessel and vessel-to-shore loading and discharging procedures, are complex, demanding and are associated with high risks.This project investigates the conditions (gaps, opportunities and operator needs, as well as the technological possibilities) for an optimized cargo handling management system with inter-system communication and increased automatization to predict and control cargo handling on tankers using AI and ML. A design concept and guidelines for the updated cargo handling management system can be outlined as a result.

On a modern tanker, the ship's officers plan and direct the cargo operations. Cargo handling operations on oil and chemical tanker vessels, vessel-to-vessel and vessel-to-shore loading and discharging procedures, are complex, demanding and are associated with high risks. They involve handling and coordination of dangerous cargo (sometimes of multiple types/grades), ballast and inert gas systems while also simultaneously considering the vessel’s mooring, draft, trim, list, stability, shear forces and bending moments. Tank and pipe system conditions such as level pressure, oxygen content, sequences, temperature, atmosphere, flow rate and line clearance allowances are also being monitored.

Therefore, loading and discharging operations are planned carefully in advance by the vessel’s chief officer, agreed and approved with the vessel’s master and, before commenced, agreed upon together with the shore terminal and/or the representatives of the other vessels. Cargo plans are posteriorly followed by the vessel’s officers of the watch. They control the loading and discharging operations by regulating fluid flows/pump speeds, and by throttling valves and pumps, etc. To their aid, they have a number of technical systems onboard to monitor and control the cargo handling systems.

Today’s cargo planning systems can reactively alert at predefined levels if deviations to the plan occur during operation (e.g., alerts on stability issues, limits of the hull). Yet, it is the vessel’s officers who operatively predict, decide, and perform the manoeuvres to control the flow of cargo, ballast, and inert gas systems. Today’s cargo handling systems are not integrated (i.e., they do not collaborate) to provide predictive decision support.

Today's technological availability should allow for cargo handling systems to become more integrated and facilitate the digitalization of rules and regulations into the system. It should also be able to analyse historical operational data with the use of Artificial Intelligence (AI) and Machine Learning (ML) principles, and therefore provide a) predictions on the time required for cargo handling operations and the next availability of the terminals, etc., and b) decision support to the operator for planning and executing a cargo loading/discharging process. The ability to predict cargo handling operations with higher precision fits well with other development projects with the aim of optimizing shipping operations and timely activities, such as the Sea Traffic Management (STM) project.

This project consists of a concept study and its objective is to map current cargo handling operations on oil and chemical tankers (including communication with port) and investigate the conditions (gaps, opportunities and operator needs, as well as the technological possibilities) for an optimized cargo handling management system with inter-system communication and increased automatization to predict and control cargo handling on tankers using AI and ML. A design concept and guidelines for the updated cargo handling management system can be outlined as a result.

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