Project references
October 2023
EU EMERGE Project contribution Authored by Danaos Research Centre
EU EMERGE project referencing papers, authored by Danaos Research Centre, Index
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T. J. Varelas, D. Kaklis, I. Varlamis and A. Flori, "Improving Voyage Efficiency in the Shipping 4.0 De- carbonization Era," 2023 IEEE International Conference on Service-Oriented System Engineering (SOSE), Athens, Greece, 2023, pp. 191-198, https://doi.org/10.1109/SOSE58276.2023.00030 D. Kaklis, I. Varlamis, G. Giannakopoulos, C. Spyropoulos and T. Varelas, Online Training for Fuel Oil Consumption Estimation: A Data Driven Approach, 23rd IEEE International conference on Mobile Data Management Cyprus, 22 pp. 394-400 doi.ieeecomputersociety.org/10.1109/MDM55031.2022.00088 D. Kaklis, P. Eirinakis, I. Varlamis, G. Giannakopoulos, C. Spyropoulos and T. Varelas, A big data approach for Fuel Oil Consumption estimation in the maritime industry, 8TH IEEE International Conference on Big Data Computing Service, Newark, USA, pp. 39-47 https://doi.ieeecomputersociety.org/10.1109/BigDataService55688.2022.00014 Takis Varelas, Dimitris Kaklis, Iraklis Varlamis and Artemis Flori, Turn Benefit of Doubt to trustworthiness of MCDA, 9 th international conference on Operations research (HELORS) on June 29-30 and July 1 Athens Greece, follow-up of 94th EWG (European working group 2022) http://eeee2023.uniwa.gr/Book%20of%20Abstracts_HELORS%202023.pdf full paper accepted to be published on book of proceedings D. Kaklis, T. Varelas, I. Varlamis, P. Eirinakis, G. Giannakopoulos, C. Spyropoulos, From STEAM to Machine: Emissions control in the shipping 4.0 era, 8TH SNAME International Symposium on Ship Operations, Management and Economics, Athens, Gr., March 23 doi.org/10.5957/SOME-2023-020 D. Kakis, I. Varlamis, G. Giannakopoulos, T. Varelas, C. Spyropoulos, Enabling digital twins in the maritime sector through the lens of AI and industry 4.0, International Journal of Information Management Data Insights, Volume 3, Issue 2, 2023, 100178, ISSN 2667doi.org/10.1016/j.jjimei.2023.100178 https://www.sciencedirect.com/science/article/pii/S2667096823000253
Danaos participation in EMERGE project References
EMERGE Evaluation, control and Mitigation of the EnviRronmental impacts of shippinG Emissions
Approaching the end of being implemented with success EU Research funded EMERGE project, we hereby report our contribution references to papers, presentations and video authored by Danaos Research Centre. Our involvement concerns the on board LL1 and IoT network implementation using the Danaos ArteMIS system. Appied research techniques of e-Monitoring- Identifying – Suggesting: ArteMIS©drc) real-time monitors vessel operational state by employing a digital replica2 and optimizes a WTW3 multivariable objective function with decarbonization, emissions fuel consumed reduction, operational efficiency (TCE) and rules compliance (CII) as arguments without retrofits. The core functionality entails a multidisciplinary DSS enhanced by simulations conducted on HQs real-time control actuation on the edge. System specifications, conclusions and suggestions are published in a white paper which available on request
Scrubber Monitoring is incorporated in ArtEMIS data analytics platform where row operational and processed measurements4 captured from the designed and implemented wireless gateway are retrieved continuously from EMS We can now provide users with good insights on scrubber operational data almost in real time and can easily confirm compliance with the regulatory requirements for all needed parameters. Moreover, the Scrubber Reference log is a feature that demonstrates compliance in case any monitoring sensor fails, indicating compliant operation under similar conditions meeting the regulations the malfunction is rectified.
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EMERGE is an innovative 4-year project funded by the European Commission under the Horizon 2020 program. The project aims at quantifying and evaluating the effects of potential emission reduction solutions for shipping in Europe and developing effective strategies and measures to reduce the environmental impacts of shipping. EMERGE will systematically analyze the complex interactions between technological options, pollutant emissions and dispersion, and the environment. Danaos Research Centre DrC participates in EMERGE project managing the on-board living lab scrubber emissions monitoring campaign. Main activities are: IoT infrastructure setup of CEMS, Flowmeter, torque-meter in a wireless OT network deployment: using LORA, MODBUS, RS422, NMEA 2.0 protocols and standards. Familiarization, Introductory lessons and training of the onboard guests on voyage operation, risk management, ISM procedures as well as scrubber principles, operation, functionality, architecture and CEMS (continuous emission monitoring system). Data capture, cleaning, correlation, analysis and suggestions for efficiency optimization.
LL William J. Mitchell at MIT is credited with first exploring the concept of a Living lab as a user-centered, open-innovation ecosystem often operating in a territorial context (e.g. city, region, vessel, campus), integrating concurrent research and innovation processes within a public-private-people partnership 2 Digital twin A virtual representation of a real-world physical system or process (a physical twin) that serves as the indistinguishable digital counterpart of it for practical purposes, such as system simulation, integration, testing, monitoring, and maintenance. A digital twin may (but not must) be used in real time and regularly synchronized with the corresponding physical system 3 WTW All greenhouse gas emissions from the production, transportation, transformation and distribution of the fuel combusted by the vessel. Well-to-wheel emissions can be divided into well-to-tank (WTT) emissions and tank-to-wheel (TTW) emissions. 4 Measurements principles Niels Bohr: If we cannot measured it, it does not exist. Lord Kelvin: if exists measure as accurate as possible and consequently we can manage IoT Dr. E Deming: if are proccesible, improve DT, AI
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From STEAM to Machine: Emissions control in the shipping 4.0 era
The maritime sector is required to adhere to the IMO 2020 - mandated reduction of emissions. This reduction can be conducted by either using a compliant fuel with lower sulfur content, an alternative fuel (e.g. LNG, methanol), or clean its exhaust gasses with a "scrubber" technology to reduce the output of CO 2 , NOx and SOx emissions. The objective of this paper is to present a holistic approach to continuously monitor and estimate the emissions of a vessel as well as to assess and improve the efficiency of scrubbers. Furthermore the deployment of a cutting-edge, integrated framework, incorporating the latest technological advances, that can ofFer the ability to capture, process and analyze vessels’ operational data in order to improve efficiency, sustainability, and rule compliance is presented. Particularly the conceptualization and materialization of a big data application suite that exploits the IoT (Internet of Things) and AI (Artificial Intelligence) advancements and technologies, to employ a “digital replica” of the en-route vessel is demonstrated. By collecting a multitude of features from on-board sensor installments, we present how we can effectively utilize these features, harvested in real time, in order to accurately assess and estimate the environmental footprint of the vessel by employing robust Fuel Oil Consumption (FOC) predictors. Then we describe in detail the streamlined procedure from data acquisition to model deployment, utilizing the proposed big data framework, in order to assess and estimate the emissions during the operational state of the vessel. Finally, we demonstrate experimental results by deploying comparative analysis utilizing operational data from one containership-centric Living Lab (LL) in order to validate and confirm our approaches in terms of accuracy and performance in a real world setting. Acknowledgements: This Publication was supported partially by the program of Industrial Scholarships of Stavros Niarchos Foundation, within the content of the EMERGE project, an European Union’s Horizon 2020 research and Innovation program under the Grant Agreement No 874990 and partially funded by Danaos Shipping has provided access to industrial data and related models Keywords: sustainability, air emission, neural network, AI, DL, ML
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foc measured vs calculated
D. Kaklis, T. Varelas, I. Varlamis, P. Eirinakis, G. Giannakopoulos, C. Spyropoulos From STEAM to Machine: Emissions control in the shipping 4.0 era 8TH SNAME International Symposium on Ship Operations, Management and Economics, Athens, Gr., March 23 doi.org/10.5957/SOME-2023-020
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Improving voyage efficiency in the shipping 4.0 decarbonization era
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Invited paper T. J. Varelas, D. Kaklis, I. Varlamis and A. Flori, "Improving Voyage Efficiency in the Shipping 4.0 Decarbonization Era," 2023 IEEE International Conference on ServiceOriented System Engineering (SOSE), Athens, Greece, 2023, pp. 191-198, doi.org: 10.1109/SOSE58276.2023.00030
The objective of this work is to pave the way toward a carbon-neutral and efficient operational blueprint for the waterborne sector, through the lens of the Industry 4.0 era. In this direction, we demonstrate a cutting-edge integrated ecosystem (ARTeMIS) for operational efficiency and environmental compliance and focus on the respective building block comprising the envisaged platform. ARTeMIS incorporates an IoT suite responsible for data acquisition, as well as a multi-purpose processing pipeline for CI/CD (continuous integration deployment) of simulation models concerning operational optimization. In the context of this work, the proposed framework was adapted accordingly to capture, analyze and continuously predict the Fuel Oil Consumption (FOC), Effective Horsepower (EHP), speed, and pollutant concentrations, of the vessel. Utilizing this streamlined procedure, we are able to assess the scrubbing efficiency, as well as the environmental footprint of the vessel (CII - Carbon Intensity Indicator) in order to further optimize the vessel operation. Furthermore, the paper argues on how generic models like STEAM (Ship Traffic Emissions Assessment Method) should be transformed radically with the utilization of shipping 4.0 driven frameworks like the one proposed in the context of this work. Finally, the unleashed highly accurate prediction potential of the proposed system concerning operational efficiency, anomaly detection, and environmental compliance is demonstrated. The onboard proof of concept and the assimilated results so far, are also depicted to illustrate the feasibility and potential of the proposed approach. Acknowledgement This work was supported partially by the program of Industrial Scholarships of Stavros Niarchos Foundation, and partially by the SmartSea project, an Erasmus+ European knowledge alliance Innovation program under the Marie Skłodowska-Curie Grant Agreement No 612198. Danaos provided industrial data as well as fully funded and managed the on-board LL (living lab), training session and campaign of EMERGE an EU Horizon research program under grand agreement No 874990. Keywords: IoT, regression. ANN, MADM, digitalization, decarbonization
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Turn Benefit of Doubt to trustworthiness of MCDA
In this work we focused not only to suggest optimal among alternate linearly formulated decisions not even to unify the better but at the same time to gain the minimum benefit of doubt, in other words the maximum trust or the confidence of decision maker(s). This is achieved with a non-linear programmed and integrated interactive e-model to resolve the ranking problem with aggregation of known ordering techniques weighted or non-weighted sum, multi-criteria analysis, topsis index and dea superior index or matrix of indexes. Therefore, the novelty is that the solution satisfies different ranking methods. Furthermore, it can also adjust further the suggested weights based on relative supreme relationships according to decision maker’s preferences. The power of interactivity enable the running of what-if scenarios, to simulate strategies or to proof related theorems. It utilizes several models linear and non-linear and was used in several operational cases. Variables values may be expressed as crisp or fuzzy numbers and initial weights from one or a group of experts may be assigned. Different data normalization (min-max, linear, vector, z-score) may be are utilized. This work resolves the mentioned contradictions by extracting the endogenous composite indicators that furthermore may be adjusted and personalized according to experts’ supreme preferences. Initially normalization, ranking methods are presented and a new variation of dea composite indicator is defined. Subsequently, the non-linear method to unify topsis and dea ranking is described and some theorems are proved. Finally, the developed “BoD e-model” is presented deploying the decision making for vessel’s emission monitoring as it was implemented within the content of EMERGE EU project.
Takis Varelas, Dimitris Kaklis, Iraklis Varlamis and Artemis Flori Turn Benefit of Doubt to trustworthiness of MCDA 9th international conference on Operations research (HELORS) on June 29-30 and July 1 Athens Greece 94th EWG (European working group 2022) http://eeee2023.uniwa.gr/Book%20of %20Abstracts_HELORS%202023.pdf
Acknowledgement
This work initially was discussed in 94th EWG (European working group 2022) and was partially funded from vesselife.com a European Maritime and Fisheries program EMFF under GA No 863565/2019, smart-sea.eu: (SMARTSEA) funded by EU Erasmus+ . The model is evaluated within the content of emerge-h2020.eu: Evaluation, control and Mitigation of the Environmental impacts of shipping Emissions, EU HORIZON 2020 program under GA No. 874990.
https://94ewgmcda.hmu.gr/94%20EWG%20on%20MCDA%20%20Programme%20and%20Book%20of%20Abstrac ts-f.pdf
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Online Training for Fuel Oil Consumption Estimation: A Data Driven Approach
D. Kaklis, I. Varlamis, G. Giannakopoulos, C. Spyropoulos and T. Varelas Online Training for Fuel Oil Consumption Estimation: A Data Driven Approach 23rd IEEE International Conference on Mobile Data Management Cyprus, 22 pp. 394400 doi.ieeecomputersociety.org/10. 1109/MDM55031.2022.00088
Estimating the Fuel Oil Consumption (FOC) of a vessel is a critical task for the maritime industry, affecting route planning and the overall management of the vessel's operation and maintenance. Consumption is strongly coupled with the operation of the Main Engine (ME), but also with the environmental conditions (i.e., weather, ocean-energy spectrum) and the hydrodynamic features (i.e., resistance, propulsion) of the vessel. Current research shows that either a multitude of features collected from the AIS (Automatic Identification System) or on-board sensors can assist to the continuous prediction of FOC. Even when a FOC estimation model is perfectly trained on a specific vessel, its performance may degrade over time, when new weather conditions apply or when the hydrodynamics of the vessel change over time, due to fouling, aging and negligent maintenance. This work presents an online learning framework that employs a custom encoding-decoding Neural Network scheme and real-time data from various on-board sensors, to appropriately update FOC estimation models. The model is able to adapt to newly acquired data using a temporally aware batch scheme that samples from the initial training set using a custom auto-encoder. Keywords: training, adaptation models; estimation, maintenance, engineering, hydrodynamics, data models
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A big data approach for Fuel Oil Consumption estimation in maritime Route optimization has been a research topic for many years in the maritime industry and it constitutes one of the key components to improving energy efficiency and sustainability in ship operations. This paper deals with the challenge of estimating Fuel Oil Consumption (FOC) in the context of Weather Routing (WR). Given a plethora of features collected from the vessel’s Automatic Identification System (AIS) or on-board sensor installations, we examine how a predictive FOC scheme can be coupled with WR optimization algorithms in order to reduce the vessel’s FOC, emissions, and the overall cost of a voyage. In order to handle the amount of data required for FOC prediction, we employ a streaming pipeline that harvests data in real-time from different sources and processes them appropriately for visualization, causal analysis, and forecasting purposes. In this direction, we first conduct an exploratory analysis to examine and unveil the importance and inter-association between the various variables related to sea-keeping and weather features, in order to utilize them effectively in the context of a FOC predictive scheme. Furthermore, we introduce a novel recurrent neural network architecture that approximates ideally the underlying function describing the features and the vessel’s FOC by taking into account historical data, and we showcase the results. Finally, we demonstrate how the FOC prediction model can be coupled with a WR algorithm to propose the optimal route for a vessel in terms of FOC efficiency. Keywords: industries, recurrent neural networks, big data, routing, prediction algorithms, fuels
D. Kaklis, P. Eirinakis, I. Varlamis, G. Giannakopoulos, C. Spyropoulos and T. Varelas A big data approach for Fuel Oil Consumption estimation in the maritime industry 8TH IEEE International Conference on Big Data Computing Service, Newark, USA, pp. 39-47. https://doi.ieeecomputersocie ty.org/10.1109/BigDataService 55688.2022.00014
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Enabling digital twins in the maritime sector through the len of AI and industry 4.0
D. Kakis, I. Varlamis, G. Giannakopoulos, T. Varelas, C. Spyropoulos Enabling digital twins in the maritime sector through the lens of AI and industry 4.0 International Journal of Information Management Data Insights, Volume 3, Issue 2, 2023, 100178, ISSN 2667-0968, https://doi.org/10.1016/j.jjimei .2023.10017 (https://www.sciencedirect.co m/science/article/pii/S2667096 823000253)
Sustainability and environmental compliance in ship operations is a prominent research topic as the waterborne sector is obliged to adopt ”green” mitigation strategies towards a low emissions operational blueprint. Fuel-Oil-Consumption (FOC) estimation constitutes one of the key components in maritime transport information systems for efficiency and environmental compliance. This paper deals with FOC estimation in a more novel way than methods proposed in literature, by utilizing a reduced-sized feature set, which allows predicting vessel’s Main-Engine rotational speed (RPM). Furthermore, this work aims to place the deployment of such models in the broader context of a cutting-edge information system, to improve efficiency and regulatory adherence. Specifically, we integrate B-Splines in the context of two Deep Learning architectures and compare their performance against state-of-the-art regression techniques. Finally, we estimate FOC by combining velocity measurements and the predicted RPM with vessel-specific characteristics and illustrate the performance of our estimators against actual FOC data.
Presentations
Several other presentations were given with references to project conclusions and experiences focused on IoT, digitilazation, digital twins concept, big data analytics and AI which will revolutionary improve veracity and radically upgrade even replace conventional emission monitoring, and reduction methods. In addition, WTW instead of TTW is strongly recommended for emission footprint measurement. Related topics like “how can remove aromatic polycyclic hydrocarbons”, “how VLSFO is produced”, “has Desulfurization: environmental negative impact”, “how refineries reduce the negative impact of Desulfurization”: “what are electrostatic filters to capture PMs praticles”, “Is time to install APM5 ?” were placed on discussion.
Digitalizing the decarbonization routeing | Feb 23 |T. Varelas
Emission control and environmental compliance in the shipping 4.0 era| July 23 | D. Kaklis
Sustainability in ship operations in the era of Industry 4.0 |May 23| D. Kaklis
https://issuu.com/takisvarelas/docs/emergeanaosreferences1810?fr=xKAE9_zU1NQ Presentations may be downloaded from our site: www.danaos.com as well as the awarded ESG report
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AMP
Alternate Maritime Power or AMP is an anti-pollution measure that helps reduce air pollution generated from diesel generators by using shore electric power as a substitute. AMP is used when the ship is halting at a port so that the engines of the ship (working on diesel) do not need to be used unnecessarily.