Dubai, 27 February 2020
Class societies provide independent assurance of safety and environmental compliance, establishing the bedrock of successful ship operations. To enhance operational capabilities and reduce opex, Genova headquartered RINA, adopts a holistic approach by leveraging on new data analysis technologies to optimize ship performance and strengthens the basis for decision-making. The IMarEST UAE branch hosted an interactive symposium detailing how RINA advances efficiency in shipping through data analysis considering industry ambitions and business realities.
At the helm of the stimulating event was Nikeel Idnani, Honorary Secretary of the IMarEST UAE branch, who in the opening introduction said, “You can only manage what you measure. Affordable & accurate awareness is key to informed decision making and business success”. He shared his practical views on energy efficiency both as a mariner and Chartered Engineer who has developed solutions to engineering problems using innovative technologies, creativity and change with accountability for complex systems involving significant levels of risk.
In the keynote presentation ‘Boosting Efficiency in Shipping through data analysis’, Dimitris Alexandros Zisimopoulos, Business Development Manager, Marine Southern Europe & Africa Area at RINA, unearthed the mystery shrouding ship operating efficiency. He brought to light the fact that a ship might sail for months with a rope entangled around the propeller, increasing the fuel consumption, without the crew being aware. Similarly, hull and propeller fouling, or the main engine needing maintenance, can heavily compromise the ships’ energy performance. Interventions to fix these and other comparable issues can be costly and need to be accurately planned considering ships schedules and cost-benefits of any initiative. At the onset, Dimitris clarified, it is essential to monitor a vessel performance before any efforts are expended towards enhancement. The most important parameter that is computed onboard is the propulsive power required to move the ship. This can be constantly monitored and benchmarked against a target in order to understand if the ship is performing efficiently. The challenge, Dimitris admitted, is getting good targets. If the average predictive error of a model is high, we cannot rely on the target and real time monitoring becomes useless. Targets computations are usually based on collected data. This may come from Noon reports (collected manually on board and sent ashore once a day) of from a data collector installed on board. In a structured system, a data collector receives signals from the ship’s navigation and automation systems, from sensors e.g. high precision inclinometers, torque meters, flow meters as well as manual input data. The actual values of propulsive power, fuel consumption and specific fuel oil consumption of the main engine are calculated and benchmarked against target values considering ship variables (like loading conditions, speed through water & Trim) and environmental variables (like sea & wind state with their relative directions). The Analytics module provides invaluable accurate decision support, reducing the administrative burden onboard.
Machine Learning (based on in service recorded data of minimum 3 months in 5 minute intervals) is the best method to compute targets since the average error on predicted power is 1.5% while in case of traditional physical method (based on ship characteristics, tank tests, Open Water Propeller Diagram) equals to 13%. The only way to reach such accurate results is to have the best possible data set quality. So, the biggest part of RINA’s work commensurate with their values of “Excellence Behind Excellence”,
Dimitris revealed, is focused on filtering and selecting the points that will build a predictive model. He emphasized the important point of not only collecting data, but ‘good’ data is needed to effectively improve business e.g. a critical part that can be analyzed is hull fouling causing efficiency degradation. If we have set good targets on the propulsive power, we can evaluate over time how the performance changes and its trend enabling an otherwise tricky decision on hull cleaning.
Dimitris explained that interventions made on board are usually analyzed based on data manually collected by the crew with the daily noon reports. This data is of course prone to human error and to the sensitivity of each person filling the data. Having one report per day, the obtained dataset is also limited. On the other hand, an automatic data collector on board results in a higher frequency and accurate data with limited human intervention. To exemplify this, Dimitris demonstrated a case of propeller reblading where RINA was involved as a third party to certify the gain. They received two data set - one from noon reports and the other one coming from a data collector installed on board. The contractual conditions were to analyze three months before and after the intervention, considering limited wind force and a stipulated range of displacements. It was only possible to evaluate the final 17% of power saving from the Automatic Data Acquisition curves while it was impossible to get a result from the manually collected data. The onboard data collector must not provide mere telemetry but be capable of elaborating and guaranteeing the reliability of collected data. Towards this end, Registro Italiano Navale has developed RINACube – OPTIMUM, a performance management solution available on their digital platform that can be used to set up a Fleet Operation Center.
In CLASSIFICATION 2.0, Dimitris explained that digitalisation is best when it enables simple, practical solutions that improve results and save time and money. In that regard, ground-breaking Remote Surveys formed an important pillar for inspections of a Fleet having received authorizations from major flag administrations.
RINA is a partner in the ROBINS (ROBotics technology for INspection of Ships) collaborative project funded by the EU Commission aimed at filling the technology and regulatory gaps that today still represent a barrier to the adoption of Robotics and Autonomous Systems in activities related to inspection of ships. Under this project, RINA will deploy drones to safely inspect hazardous spaces with harsh or dirty conditions and carry out close-up visual inspection and thickness measurements with dependable data capture, obviating the cumbersome need for a surveyor with breathing apparatus to access these confined spaces with on-hand tank rescue and expensive time-consuming access equipment e.g. scaffolding.
Andrea Di Bella, RINA’s Middle East & Turkey Area Marine Director candidly remarked, “Classification societies have been established to ensure the highest standard of safety for people and ships and provide valuable advice on the latest trends of the shipping industry. Artificial intelligence, blockchain, Internet of things are very fashionable words. Each day in the newspaper you can find many articles in this regard. As engineers, we don’t like fashionable words. We like black figures which generate margins at the end of the month. There is not yet artificial intelligence, but we have developed an incredible capability of analyzing huge amount of data in a very short time and it is possible to get reliable forecasts and therefore take informed decision. RINA It is at the forefront of these developments and this evening we invite you to be part of our experience”.
The 108 participants present at the forward-looking IMarEST technical meeting were treated to an unrivalled opportunity involving cross-industry engagement, bringing together Middle East ship managers, maritime organizations & commercial enterprises to discuss the future of shipping through Planning, Monitoring and Analyzing. Deliberations continued over ‘Calamari and Campari’, hosted by RINA, on the rooftop of the upscale Ramada Jumeirah hotel in Dubai.