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D.F.G. Tijdeman
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As the frequency and intensity of natural disasters increase, there is growing recognition of the need to address climate change and limit the increase in global average temperature. The shipping industry, which contributes 2.9% to global anthropogenic greenhouse gas emissions, plays a significant role by releasing substantial amounts of CO2, other harmful gases, and fine particles, negatively impacting climate change and the health of those near ports or waterways. As part of a wider initiative to reduce those effects, the International Maritime Organization has set the goal for the shipping industry to achieve net zero emissions by 2050. In order to accomplish this, accurate and comprehensive information on emissions is crucial.
Various methods have been developed to estimate emissions in the shipping industry. Top-down methods are applied using large-scale data to estimate emissions over a wide area. This approach provides a comprehensive overview but lacks the specific details required for local interventions. In contrast, bottom-up methods are applied and start with detailed data at the source and aggregate information to estimate the total emissions. Bottom-up methods offer more precise insights, however, more extensive data and complex modeling is required. To obtain more precise understandings, the modern bottom-up models use Automatic Identification System as input, a globally used system for tracking vessels. AIS data consists, among others, of time-dependent variables such as speed, location, and ship identification number. The data is used to assign an operational mode to the ship (sailing, maneuvering, anchoring, berthing). Based on this mode, it is determined if the main engines of the ship are on. If they are, the engine power is calculated from the resistance force acting on the moving ship. It is possible to convert engine power to fuel consumption and later to emissions with Specific Fuel Oil Consumption and emission factors.
Currently used emission models calculate engine power with the use of empirical formulas. So far, these models have mostly been validated by comparing them either to a noon report, or to another model. As a result, it gives no insight into the capability of a model to predict with a high spatial resolution, which is important in port areas and inland waterways.
There is little room for improvement in current semi-empirical bottom-up methods. However, in recent years, machine learning has shown the capability of replacing and often outperforming empirical models in other fields of research. This is due to the ability to model nonlinear behavior, find relations that humans can not, and work in higher dimensions. As a ship's engine power is reliant on a multitude of factors, machine learning might be the solution towards more accurate predictions.
This research aims to assess the capability of machine learning models to predict a ship's engine power with a high spatial resolution, with a focus on LSTM models. To do so, onboard sensor data from two ships was used; one sea-going container vessel and one inland tanker. The measured data was used to validate the semi-empirical method. Afterwards, the machine learning model was trained against the same sensor data. The predictions of the semi-empirical and machine learning models were then compared to one another.
Comparisons showed that the error for total energy used for the container vessel went from +62.87% to -0.82% when using a Bi-LSTM model with speed, acceleration, draught and depth as input. For the assessment of the spatiotemporal predictions, the MAE and RMSE were used. Based on these performance indicators, a normal LSTM network with speed, acceleration, draught and depth as input performed best. The MAE compared to the reference went down from 9542 to 2863, or about 7% of the maximum engine power.
The second case study highlighted the challenges that come with engine power prediction in inland waterways. Firstly, the fact that the speed over ground is used has a bigger influence in this case, as the ship will always sail with or against the current. Secondly, the influence of the blockage factor can
not be ignored without having higher losses. In addition to these two shortcomings, the model was trained on a diesel-electric ship. This ship has a much more constant power profile than ships with a more classical propulsion run on fossil fuels. This likely made the model more robust to changes. The combination of these three aspects caused less accurate predictions. Although there was more data available, errors were higher than for the case study. The best performing models showed a +3.41% error on total energy use and an MAE of 205.65, or about 12.8% of the maximum engine power.
This research has contributed new insights to the field of maritime emission modeling. The potential of machine learning models has been shown in comparison to an existing semi-empirical model. In addition, this model has now been validated using measurement data for a seagoing container vessel. Finally, shortcomings of the method using machine learning were exposed and a solution is proposed for a more complete, generalizable model. ...
Various methods have been developed to estimate emissions in the shipping industry. Top-down methods are applied using large-scale data to estimate emissions over a wide area. This approach provides a comprehensive overview but lacks the specific details required for local interventions. In contrast, bottom-up methods are applied and start with detailed data at the source and aggregate information to estimate the total emissions. Bottom-up methods offer more precise insights, however, more extensive data and complex modeling is required. To obtain more precise understandings, the modern bottom-up models use Automatic Identification System as input, a globally used system for tracking vessels. AIS data consists, among others, of time-dependent variables such as speed, location, and ship identification number. The data is used to assign an operational mode to the ship (sailing, maneuvering, anchoring, berthing). Based on this mode, it is determined if the main engines of the ship are on. If they are, the engine power is calculated from the resistance force acting on the moving ship. It is possible to convert engine power to fuel consumption and later to emissions with Specific Fuel Oil Consumption and emission factors.
Currently used emission models calculate engine power with the use of empirical formulas. So far, these models have mostly been validated by comparing them either to a noon report, or to another model. As a result, it gives no insight into the capability of a model to predict with a high spatial resolution, which is important in port areas and inland waterways.
There is little room for improvement in current semi-empirical bottom-up methods. However, in recent years, machine learning has shown the capability of replacing and often outperforming empirical models in other fields of research. This is due to the ability to model nonlinear behavior, find relations that humans can not, and work in higher dimensions. As a ship's engine power is reliant on a multitude of factors, machine learning might be the solution towards more accurate predictions.
This research aims to assess the capability of machine learning models to predict a ship's engine power with a high spatial resolution, with a focus on LSTM models. To do so, onboard sensor data from two ships was used; one sea-going container vessel and one inland tanker. The measured data was used to validate the semi-empirical method. Afterwards, the machine learning model was trained against the same sensor data. The predictions of the semi-empirical and machine learning models were then compared to one another.
Comparisons showed that the error for total energy used for the container vessel went from +62.87% to -0.82% when using a Bi-LSTM model with speed, acceleration, draught and depth as input. For the assessment of the spatiotemporal predictions, the MAE and RMSE were used. Based on these performance indicators, a normal LSTM network with speed, acceleration, draught and depth as input performed best. The MAE compared to the reference went down from 9542 to 2863, or about 7% of the maximum engine power.
The second case study highlighted the challenges that come with engine power prediction in inland waterways. Firstly, the fact that the speed over ground is used has a bigger influence in this case, as the ship will always sail with or against the current. Secondly, the influence of the blockage factor can
not be ignored without having higher losses. In addition to these two shortcomings, the model was trained on a diesel-electric ship. This ship has a much more constant power profile than ships with a more classical propulsion run on fossil fuels. This likely made the model more robust to changes. The combination of these three aspects caused less accurate predictions. Although there was more data available, errors were higher than for the case study. The best performing models showed a +3.41% error on total energy use and an MAE of 205.65, or about 12.8% of the maximum engine power.
This research has contributed new insights to the field of maritime emission modeling. The potential of machine learning models has been shown in comparison to an existing semi-empirical model. In addition, this model has now been validated using measurement data for a seagoing container vessel. Finally, shortcomings of the method using machine learning were exposed and a solution is proposed for a more complete, generalizable model. ...
As the frequency and intensity of natural disasters increase, there is growing recognition of the need to address climate change and limit the increase in global average temperature. The shipping industry, which contributes 2.9% to global anthropogenic greenhouse gas emissions, plays a significant role by releasing substantial amounts of CO2, other harmful gases, and fine particles, negatively impacting climate change and the health of those near ports or waterways. As part of a wider initiative to reduce those effects, the International Maritime Organization has set the goal for the shipping industry to achieve net zero emissions by 2050. In order to accomplish this, accurate and comprehensive information on emissions is crucial.
Various methods have been developed to estimate emissions in the shipping industry. Top-down methods are applied using large-scale data to estimate emissions over a wide area. This approach provides a comprehensive overview but lacks the specific details required for local interventions. In contrast, bottom-up methods are applied and start with detailed data at the source and aggregate information to estimate the total emissions. Bottom-up methods offer more precise insights, however, more extensive data and complex modeling is required. To obtain more precise understandings, the modern bottom-up models use Automatic Identification System as input, a globally used system for tracking vessels. AIS data consists, among others, of time-dependent variables such as speed, location, and ship identification number. The data is used to assign an operational mode to the ship (sailing, maneuvering, anchoring, berthing). Based on this mode, it is determined if the main engines of the ship are on. If they are, the engine power is calculated from the resistance force acting on the moving ship. It is possible to convert engine power to fuel consumption and later to emissions with Specific Fuel Oil Consumption and emission factors.
Currently used emission models calculate engine power with the use of empirical formulas. So far, these models have mostly been validated by comparing them either to a noon report, or to another model. As a result, it gives no insight into the capability of a model to predict with a high spatial resolution, which is important in port areas and inland waterways.
There is little room for improvement in current semi-empirical bottom-up methods. However, in recent years, machine learning has shown the capability of replacing and often outperforming empirical models in other fields of research. This is due to the ability to model nonlinear behavior, find relations that humans can not, and work in higher dimensions. As a ship's engine power is reliant on a multitude of factors, machine learning might be the solution towards more accurate predictions.
This research aims to assess the capability of machine learning models to predict a ship's engine power with a high spatial resolution, with a focus on LSTM models. To do so, onboard sensor data from two ships was used; one sea-going container vessel and one inland tanker. The measured data was used to validate the semi-empirical method. Afterwards, the machine learning model was trained against the same sensor data. The predictions of the semi-empirical and machine learning models were then compared to one another.
Comparisons showed that the error for total energy used for the container vessel went from +62.87% to -0.82% when using a Bi-LSTM model with speed, acceleration, draught and depth as input. For the assessment of the spatiotemporal predictions, the MAE and RMSE were used. Based on these performance indicators, a normal LSTM network with speed, acceleration, draught and depth as input performed best. The MAE compared to the reference went down from 9542 to 2863, or about 7% of the maximum engine power.
The second case study highlighted the challenges that come with engine power prediction in inland waterways. Firstly, the fact that the speed over ground is used has a bigger influence in this case, as the ship will always sail with or against the current. Secondly, the influence of the blockage factor can
not be ignored without having higher losses. In addition to these two shortcomings, the model was trained on a diesel-electric ship. This ship has a much more constant power profile than ships with a more classical propulsion run on fossil fuels. This likely made the model more robust to changes. The combination of these three aspects caused less accurate predictions. Although there was more data available, errors were higher than for the case study. The best performing models showed a +3.41% error on total energy use and an MAE of 205.65, or about 12.8% of the maximum engine power.
This research has contributed new insights to the field of maritime emission modeling. The potential of machine learning models has been shown in comparison to an existing semi-empirical model. In addition, this model has now been validated using measurement data for a seagoing container vessel. Finally, shortcomings of the method using machine learning were exposed and a solution is proposed for a more complete, generalizable model.
Various methods have been developed to estimate emissions in the shipping industry. Top-down methods are applied using large-scale data to estimate emissions over a wide area. This approach provides a comprehensive overview but lacks the specific details required for local interventions. In contrast, bottom-up methods are applied and start with detailed data at the source and aggregate information to estimate the total emissions. Bottom-up methods offer more precise insights, however, more extensive data and complex modeling is required. To obtain more precise understandings, the modern bottom-up models use Automatic Identification System as input, a globally used system for tracking vessels. AIS data consists, among others, of time-dependent variables such as speed, location, and ship identification number. The data is used to assign an operational mode to the ship (sailing, maneuvering, anchoring, berthing). Based on this mode, it is determined if the main engines of the ship are on. If they are, the engine power is calculated from the resistance force acting on the moving ship. It is possible to convert engine power to fuel consumption and later to emissions with Specific Fuel Oil Consumption and emission factors.
Currently used emission models calculate engine power with the use of empirical formulas. So far, these models have mostly been validated by comparing them either to a noon report, or to another model. As a result, it gives no insight into the capability of a model to predict with a high spatial resolution, which is important in port areas and inland waterways.
There is little room for improvement in current semi-empirical bottom-up methods. However, in recent years, machine learning has shown the capability of replacing and often outperforming empirical models in other fields of research. This is due to the ability to model nonlinear behavior, find relations that humans can not, and work in higher dimensions. As a ship's engine power is reliant on a multitude of factors, machine learning might be the solution towards more accurate predictions.
This research aims to assess the capability of machine learning models to predict a ship's engine power with a high spatial resolution, with a focus on LSTM models. To do so, onboard sensor data from two ships was used; one sea-going container vessel and one inland tanker. The measured data was used to validate the semi-empirical method. Afterwards, the machine learning model was trained against the same sensor data. The predictions of the semi-empirical and machine learning models were then compared to one another.
Comparisons showed that the error for total energy used for the container vessel went from +62.87% to -0.82% when using a Bi-LSTM model with speed, acceleration, draught and depth as input. For the assessment of the spatiotemporal predictions, the MAE and RMSE were used. Based on these performance indicators, a normal LSTM network with speed, acceleration, draught and depth as input performed best. The MAE compared to the reference went down from 9542 to 2863, or about 7% of the maximum engine power.
The second case study highlighted the challenges that come with engine power prediction in inland waterways. Firstly, the fact that the speed over ground is used has a bigger influence in this case, as the ship will always sail with or against the current. Secondly, the influence of the blockage factor can
not be ignored without having higher losses. In addition to these two shortcomings, the model was trained on a diesel-electric ship. This ship has a much more constant power profile than ships with a more classical propulsion run on fossil fuels. This likely made the model more robust to changes. The combination of these three aspects caused less accurate predictions. Although there was more data available, errors were higher than for the case study. The best performing models showed a +3.41% error on total energy use and an MAE of 205.65, or about 12.8% of the maximum engine power.
This research has contributed new insights to the field of maritime emission modeling. The potential of machine learning models has been shown in comparison to an existing semi-empirical model. In addition, this model has now been validated using measurement data for a seagoing container vessel. Finally, shortcomings of the method using machine learning were exposed and a solution is proposed for a more complete, generalizable model.
Student report
(2023)
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D.F.G. Tijdeman, J.H.M. Stevens, L.G. Teuber, M.X.K. Lonissen, N.H.M. Roeders, R.J. Lip, F.C. Lange, J.S. Hoving, O.J. Kirichek
This paper investigates the technical, life cycle, and economic feasibility of a 30 MW upscaled downwind turbine, comparing it to a 15 MW X1 Wind PivotBuoy downwind turbine and a benchmark 15 MW IEA Umaine VolturnUS-S upwind turbine in the 450 MW Sud de la Bretagne I wind farm site. The study is significant due to the rising energy demand, the potential for decreasing the levelized cost of energy with increased turbine size, and the optimized use of space. The size limit of current upwind turbine designs could be addressed using a downwind turbine solution.
The research is conducted by modelling the global dynamic response of the structure using OpenFAST and computing the natural frequencies and stresses using a finite element model. A lifecycle analysis is performed to identify potential pitfalls and bottlenecks by analysing the individual lifecycle phases. The economic feasibility is assessed by simulating the annual energy production using TOPFARM and utilizing structural analysis and lifecycle assessment to quantify capital, operational, and abandonment expenditures. Based on the annual energy production and the performance indicators the levelized cost of energy is calculated.
The findings indicate that while the global stability is within boundaries, the stress in members is too high with a simple scale-up of the proposed design. Bottlenecks are found in lifting operations and supply chain readiness. The levelized cost of energy and capital expenditure increased due to substructure self-weight, rendering the proposed 30 MW scale-up currently unfeasible when compared to the other two wind farms.
These findings are important as they demonstrate that the 15 MW X1 Wind PivotBuoy is not scalable without design changes. The levelized cost of energy does not decrease with an increased floater solution. The 15 MW X1 Wind PivotBuoy downwind turbine seems more economically viable, making it a more interesting option for future development. ...
The research is conducted by modelling the global dynamic response of the structure using OpenFAST and computing the natural frequencies and stresses using a finite element model. A lifecycle analysis is performed to identify potential pitfalls and bottlenecks by analysing the individual lifecycle phases. The economic feasibility is assessed by simulating the annual energy production using TOPFARM and utilizing structural analysis and lifecycle assessment to quantify capital, operational, and abandonment expenditures. Based on the annual energy production and the performance indicators the levelized cost of energy is calculated.
The findings indicate that while the global stability is within boundaries, the stress in members is too high with a simple scale-up of the proposed design. Bottlenecks are found in lifting operations and supply chain readiness. The levelized cost of energy and capital expenditure increased due to substructure self-weight, rendering the proposed 30 MW scale-up currently unfeasible when compared to the other two wind farms.
These findings are important as they demonstrate that the 15 MW X1 Wind PivotBuoy is not scalable without design changes. The levelized cost of energy does not decrease with an increased floater solution. The 15 MW X1 Wind PivotBuoy downwind turbine seems more economically viable, making it a more interesting option for future development. ...
This paper investigates the technical, life cycle, and economic feasibility of a 30 MW upscaled downwind turbine, comparing it to a 15 MW X1 Wind PivotBuoy downwind turbine and a benchmark 15 MW IEA Umaine VolturnUS-S upwind turbine in the 450 MW Sud de la Bretagne I wind farm site. The study is significant due to the rising energy demand, the potential for decreasing the levelized cost of energy with increased turbine size, and the optimized use of space. The size limit of current upwind turbine designs could be addressed using a downwind turbine solution.
The research is conducted by modelling the global dynamic response of the structure using OpenFAST and computing the natural frequencies and stresses using a finite element model. A lifecycle analysis is performed to identify potential pitfalls and bottlenecks by analysing the individual lifecycle phases. The economic feasibility is assessed by simulating the annual energy production using TOPFARM and utilizing structural analysis and lifecycle assessment to quantify capital, operational, and abandonment expenditures. Based on the annual energy production and the performance indicators the levelized cost of energy is calculated.
The findings indicate that while the global stability is within boundaries, the stress in members is too high with a simple scale-up of the proposed design. Bottlenecks are found in lifting operations and supply chain readiness. The levelized cost of energy and capital expenditure increased due to substructure self-weight, rendering the proposed 30 MW scale-up currently unfeasible when compared to the other two wind farms.
These findings are important as they demonstrate that the 15 MW X1 Wind PivotBuoy is not scalable without design changes. The levelized cost of energy does not decrease with an increased floater solution. The 15 MW X1 Wind PivotBuoy downwind turbine seems more economically viable, making it a more interesting option for future development.
The research is conducted by modelling the global dynamic response of the structure using OpenFAST and computing the natural frequencies and stresses using a finite element model. A lifecycle analysis is performed to identify potential pitfalls and bottlenecks by analysing the individual lifecycle phases. The economic feasibility is assessed by simulating the annual energy production using TOPFARM and utilizing structural analysis and lifecycle assessment to quantify capital, operational, and abandonment expenditures. Based on the annual energy production and the performance indicators the levelized cost of energy is calculated.
The findings indicate that while the global stability is within boundaries, the stress in members is too high with a simple scale-up of the proposed design. Bottlenecks are found in lifting operations and supply chain readiness. The levelized cost of energy and capital expenditure increased due to substructure self-weight, rendering the proposed 30 MW scale-up currently unfeasible when compared to the other two wind farms.
These findings are important as they demonstrate that the 15 MW X1 Wind PivotBuoy is not scalable without design changes. The levelized cost of energy does not decrease with an increased floater solution. The 15 MW X1 Wind PivotBuoy downwind turbine seems more economically viable, making it a more interesting option for future development.