Expected Outcome:
Project results are expected to contribute to all the following outcomes:
- Ship operators will benefit from the use of Artificial Intelligence (AI), using techniques such as Machine Learning (ML) and Machine Reasoning, and improved instrumentation, as well as on monitoring and control systems to optimise ship operations, while integrating new fuel types, power conversion systems, propulsion and heat systems, wind-assisted propulsion, innovative energy storage systems (beyond conventional batteries), and other energy efficiency solutions;
- Improved supervision, forecasting, and real-time control of the full spectrum and complexity of ship energy needs and flows and sea state, according to the variety of operation profiles (i.e., ship energy dynamics, varying -especially low- water levels);
- Development of adaptable real-time optimisation strategies to accommodate expected and unforeseen operational conditions;
- Improved calibration and certification of sensing systems, including low-cost innovative real-time sensors, to support the enforcement of Greenhouse Gas (GHG) emissions reduction, as well as SOx and NOx emissions regulatory framework, both at a European (e.g., Sulphur Directive, NRMM Regulation) and international level (e.g., MARPOL Annex VI), as well as the reduction of emissions of any other relevant harmful emissions, leading towards common operational procedures, methodologies and reporting;
- Improved ship design concepts including lessons learnt from energy efficiency optimisation, considering various parameters that influence ship performance under diverse operational conditions (e.g., wave characteristics, wind strength, hull biofouling growth rate, low water levels/droughts).
Scope:
Energy Management Systems (EMS) have the potential through adaptive control, ML and AI to optimise energy demand and minimise harmful emissions. This paradigm shift will also lead to increase vessel efficiency through pioneering assessments and optimised integration of groundbreaking power conversion and energy storage systems, Waste Heat Recovery (WHR) systems, machinery prognostics and system simulations, variable speed electric motors, and both centralised and modular energy micro-grid architectures. In parallel, such advancements allow for real-time exhaust gas monitoring, including particulates, SOx, NOx and GHG emissions, which could be integrated into the overall monitoring and adaptive control.
Proposals are expected to address all the following aspects:
- Development, adaptation, and integration of real-time monitoring solutions for continuous mapping of ship operating profiles, energy flows, Greenhouse Gas (GHG) emissions, as well as SOx, NOx and other polluting emissions (e.g., emissions from methane slip), including the assessment of potential ammonia environmental footprint;
- Development of advanced monitoring, supervision and forecasting models, by capitalizing on AI and ML capacities for real-time data analysis and feedback, considering both internal and external data sources; comprehensive machine learning and machine reasoning models should enable intelligent vessel performance monitoring and supervision, vessel operation optimisation and planning;
- Design of centralised or modular ship energy architecture, including micro-grid architectures, applicable to a variety of commercial ship types, and adaptive energy management systems to improve the overall vessel energy efficiency, through energy demand reduction and energy supply efficiency;
- Showcase the flexibility of the adaptive energy management system using simulation methods for different vessels having various propulsion system types, operating in a wide range of environmental conditions;
- Assessment of design, including retrofitting, and operational measures and controls by focusing on three (3) complementary case studies, on, Inland Waterways Transportation (IWT), coastal shipping and Deep-Sea Shipping (DSS), aiming at maximising vessel efficiency, in the context of designing for efficiency.
This topic implements the co-programmed European Partnership on ‘Zero Emission Waterborne Transport’ (ZEWT). As such, projects resulting from this topic will be expected to report on results to the European Partnership ‘Zero Emission Waterborne Transport’ (ZEWT) in support of the monitoring of its KPIs.
