“We are at the start of a long voyage to decarbonize shipping…These ships are huge, and they use a vast amount of fuel. This won’t be an easy task, but we don’t have a choice.”– Camille Bourgeon, IMO
On the journey towards a greener maritime industry, SMARTShip is proving a vital digital tool when it comes to assessing the performance and sustainability of future fuels like biodiesel.
The IMO’s strategy to combat climate change revolves around technological innovation in future fuels to achieve greenhouse gas (GHG) reduction. It’s time to have everyone onboard from the technology developers to manufacturers, suppliers, owners, and operators.
Decarbonization has been top of the agenda for quite some time, but now we’re starting to see the collective cooperation and action from all parties involved. Recently, The MOL Group and The Trafigura Group have set up an initiative to adopt clean alternative fuels to achieve net zero greenhouse gas emissions by 2050. Both MOL and Trafigura have committed to a joint study on biodiesel fuel (BDF), with the intention of establishing a global supply of BDF for MOLCT’s fleet.
The consensus in the shipping industry is that the most promising path to reduce emissions is by transitioning from traditional fuel oil towards alternative fuels. However, there is no clear frontrunner on the fuel of the future; the alternative fuels that the industry is considering all have advantages and disadvantages, including sustainability, cost, reliability, safety, and of course environmental impact.
With so many considerations, it is important for the industry to generate, collect, and analyze the real-world data of ships powered by nontraditional energy sources to make informed decisions on fuel alternatives.
As part of the joint study, a sea trial was conducted on a voyage from Rotterdam to the U.S. Gulf Coast with TFG Marine-supplied BDF on the MOLCT-operated chemical tanker Niseko Galaxy.
Biofuels are considered one of the few options for existing deep-sea shipping vessels to achieve IMO targets. Biofuels are attractive because they can achieve substantial (~30%) GHG reduction, can be blended with conventional fuels, and can be used directly in existing installations without major technical modifications. There are some concerns and technical considerations because biofuels can have poor flow properties and fuel quality can be compromised by microbial growth and oxygen degradation. To ensure trouble-free operations, managing these challenges and monitoring the equipment during operation is critical.
Niseko Galaxy has Alpha Ori Technology’s SMARTShip IoT platform onboard which was used to perform:
The SMARTShip IoT platform is a suite of components that enable remote data collection from connected devices and offers applications that provide real-time monitoring, analytics, and insights to allow for faster and more informed decision making. AMS data (as well as navigation and cargo data) is constantly stored on board the vessel and streamed to the shore in real-time.
For the BDF trial, the following was performed/configured within the SMARTShip platform to monitor engine performance and alert on potential issues early in case of observed equipment degradation:
Asset AI, a machine learning predictive maintenance application that learns equipment behavior and alerts on anomalous running conditions, was continuously monitoring engine parameters throughout the trial. Asset AI produced baseline performance models for all engine parameters from 1 year of historical data prior to the BDF trial, which was used to assess the shift in parameters during and following the BDF trial. The below example displays how the average exhaust gas temperature was monitored during the trial. Real-time data is continuously compared against both the historical data produced baseline (represented in green) and shop trial baseline (represented in pink). The alert thresholds (represented in yellow and red) indicate boundaries for anomalous running conditions, and when breached, users are alerted.
The below chart displays the data in a scatter chart better representing the machine learned and shop trial models with data overlaid.
During the BDF trial, Asset AI monitored 57 parameters and no significant deviations were observed from the historically established baseline.
Configured user-specified dashboards within the remote monitoring and diagnostics application to allow for viewing/trending all the pertinent data related to the trial.
Configured user-specified notifications including multiple parameters and logical conditions to allow users to get notified right away on any potential issues.
There was no fuel mass flow meter sensor available, so user-specified derived tags were created to approximate fuel consumption using fuel tank sounding tables, the sounding level sensor data, and vessel trim sensor data available.
Data prior to, during, and following the BDF trial was used for analysis to assess whether any changes in engine parameters were observable, monitor for potential degradation, and any potential undesired effects following the trial.
The following data was used for analysis:
|Dates||Total Steady* Time / Point Count|
|Prior to BDF Trial||Oct 1, 2021 00:00 – Apr 3, 2022 13:00||50.9 days / 146,463 points|
|BDF Trial||Apr 3, 2022 13:00 – Apr 15, 2022 07:40||10.5 days / 30,236 points|
|Post BDF Trial||Apr 15, 2022 07:40 – Apr 23, 2022 00:00||4.7 days / 13,408 points|
Fuel Oil Pressure: There was no significant shift in FO inlet pressure. Pressure was maintained at 0.75 MPa (+/-0.05 MPa) during the BDF trial.
Fuel oil temperature: FO inlet temperature was maintained at 86°C (+/-3°C) during the BDF trial.
Fuel index: The fuel index did not have a major shift, however, between 50-65% load, the fuel index was approximately 5% higher than the pre-BDF and post BDF data. This was expected due to the lower energy (LCV) content of the BDF.
Cylinder exhaust gas temperature: There was no significant shift in exhaust gas temperatures. Exhaust gas temperatures prior to, during, and following the BDF trial were approximately the same considering the engine load. Individual unit exhaust gas temperature deviations from average were also analyzed and found to be normal.
Cylinder max pressure: Real-time data was not available for cylinder pressure data. Data was recorded at 4-hour intervals by the crew during the BDF trial. There was an approximate Pmax increase of 5 bar while running on BDF; this increase is likely due to longer injection timing of the fuel. There were no changes made in the MOP for the trial
Cylinder compression pressure: There was no significant shift in compression pressure during the BDF trial.
Cylinder scavenge air box temperature: There was no significant shift in scavenge air box temperatures. Temperature prior to, during, and following the BDF trial were approximately the same considering the scavenge air cooler air outlet temperature. Individual unit scavenge air box temperature deviations from average were also analyzed and found to be normal.
Cylinder PCO outlet temperature: There was no significant shift in PCO outlet temperatures. Temperature prior to, during, and following the BDF trial were approximately the same considering the LO inlet temperature. During the BDF trial, LO inlet temperature was maintained at a constant 46°C. Individual unit PCO temperature deviations from average were also analyzed and found to be normal.
Cylinder JCFW outlet temperature: There was no significant shift in JCFW outlet temperatures. Temperature prior to, during, and following the BDF trial were approximately the same considering the JCFW inlet temperature. Individual unit JCFW temperature deviations from average were also analyzed and found to be normal.
Turbocharger parameters were also analyzed and found to be normal.
The BDF trial data analysis conclusion is that there was no significant deviation in engine operation on the 30% biofuel blend. From a technical perspective this is a viable fuel alternative. However, this is only a small sample size of one vessel on one voyage. It is important to continue to generate, collect, analyze, and share data, so the industry can make confident data-driven decisions and collectively achieve the aggressive decarbonization goals.
Tom Callahan is a Product Manager at Alpha Ori Technologies. He has a decade of experience in the marine industry and predictive maintenance applications. Contact: firstname.lastname@example.org