This thesis investigates the yaw stability of wind-driven vessels using wingsails, addressing the urgent need for decarbonising global shipping. An analytical method is developed to assess directional stability based on aerodynamic and hydrodynamic characteristics, yielding a universal stability criterion validated with SEAMAN data. The study explores practical stability assessment approaches and evaluates the impact of varying hydrodynamic parameters, aerodynamic models, and surge coupling effects.

Results show that while stability diagnostics are invariant under different hydrodynamic models, significant variations occur in destabilised hulls. Including a boundary layer in the wind model has negligible effects on stability but increases computational effort. The closed-loop analysis assesses proportional and derivative feedback control strategies, demonstrating satisfactory stability for both human and autopilot control across all points of sail in the original hull configuration.

Time-domain responses to step rudder inputs indicate small steady errors and oscillatory components under human control, with autopilot control yielding even more robust outcomes. The study highlights the importance of surge coupling in stability analysis, often overlooked in previous models. The thesis concludes that wind-driven vessels can achieve directional stability under active control schemes, providing a foundation for future research on sustainable maritime transportation.

This thesis explores the potential for enhancing the operational efficiency of deep-sea mining through the semi-real-time modelling of the Vertical Transport System (VTS) and umbilical positioning. As the demand for rare earth minerals rises with the global shift toward renewable energy, deep-sea mining has emerged as a crucial method for resource extraction. However, the complex and dynamic nature of deep-sea environments presents significant challenges, particularly in ensuring the safety and efficiency of the mining equipment.

The thesis develops a computational model that simulates the position and dynamics of the VTS and umbilical in semi-real-time, providing predictive insights for operational decision-making. The model incorporates a shape calculator for the umbilical, jumper, and riser, a current finder to estimate ocean currents, collision detection, and a P-turn optimisation algorithm to improve manoeuvring efficiency during mining operations. By integrating sensor data and environmental conditions, the model offers real-time feedback and predictive analysis, aiding operators in minimising risks such as ground collisions and equipment entanglement.

The model’s accuracy was verified against OrcaFlex, a widely used marine simulation software, and data collected during a pilot mining test (PMT). The verification process demonstrated that the model’s predictions are within an acceptable error margin of less than 1% of the total length of the umbilical, confirming its reliability for practical application. Additionally, the model's P-turn optimisation showed the potential to reduce the distance travelled by the vessel during turns by up to 35%, translating into significant time and fuel savings for mining operations.

While the model offers valuable insights and improvements in operational efficiency, the research also acknowledges its limitations, including the simplification of certain environmental factors and the need for more detailed modelling of the riser. Future work is recommended to expand the model's capabilities, particularly in incorporating three-dimensional movement, enhancing fuel efficiency considerations, and further refining the optimisation process for P-turns.

Overall, this thesis demonstrates that semi-real-time modelling of the VTS and umbilical is a viable method for improving the efficiency and safety of deep-sea mining operations, contributing to the sustainable extraction of critical resources.

The demand for offshore marine contractor services is expected to increase in the coming years, whilst contractors, such as Boskalis, receive growing pressure to reduce greenhouse gas emissions from their offshore operations. Dynamic positioning (DP) is a crucial aspect of most offshore operations, but the high redundancy levels and frequent load variations present several problems and negatively impact fuel efficiency.

This MSc Thesis proposes improved diesel generator (DG) load allocation strategies as a method for reducing fuel consumption from DP operations of offshore vessels, considering existing ship designs and industry practices whilst maintaining high safety standards. The main principle of this solution is to improve the overall fuel consumption by allowing prime movers to operate at different loads. The main research objective of this report is to determine the fuel savings achieved through improved DG load allocation strategies for a selected case study, considering realistic operational constraints, standard industry practices, and a dynamic load profile which approximates real conditions.

A diving support vessel is selected as case study vessel based on its power system characteristics, operational profile, and expected load profile during DP operations. Realistic vessel limits and practices are gathered from operational vessel documents, and constraints are established for different load allocation strategies. Instead of using evolutionary algorithms to create DG sequences that are optimised for fuel efficiency, DG sequences are manually created and correspond to ten different load allocation strategies in order to maintain a clear structure which operators are able to utilise in real life.
The best DG sequences of each load allocation strategy are selected and further analysed in a preliminary analysis, providing the hourly fuel consumption of each load allocation strategy, between 0% and 110% MCR w.r.t. the benchmark strategy.
In addition, a dynamic model is developed to simulate a 24-hour DP operation of the selected case study vessel, providing information on the resulting fuel consumption and potential savings of different improved DG loading strategies for a dynamic load profile. This load profile changes over time to approximate real-life conditions, including most frequent power levels and occasional load transients.

Results indicated that all improved DG load allocation strategies provide fuel savings for both load cases w.r.t. the benchmark strategy. Savings are maximally 5.5% for open bus-tie configurations and 11.3% for closed bus-tie configurations.
Additionally, the outcomes of the dynamic model are compared to those provided by the preliminary analysis, which represents current state-of-the-art methods. The frequently low power levels and occasional load transients are not well represented by an operational average load, whilst these significantly influence the prospected fuel savings of the proposed solutions. Therefore, the results emphasise the importance of considering realistic industry practices and determining fuel consumption for a dynamic load profile which approximates real DP operations.

Although similar fuel savings are expected for other conventional diesel-electric DP vessels, the extent greatly depends on aspects such as the operational profile in DP mode or the fuel consumption characteristics of the power system. Actual fuel savings are likely lower as the effects of load transients are not widely considered within this research. Nevertheless, significant fuel savings are expected, as methods could be devised to limit this.
Ultimately, fuel savings from improved diesel generator load allocation strategies have been provided for realistic operational constraints, presenting an alternative out-of-the-box solution for reducing fuel consumption of Dynamic Positioning operations from Offshore Vessels.

The shift to sustainable energy sources has increased demand for Energy Transition Metals such as nickel, copper, cobalt, and manganese. To satisfy this need while reducing the negative social and environmental effects of conventional mining, Deep-sea Nodule Collection (DSNC) appears to be a feasible option. Despite its potential, DSNC encounters difficulties with path planning since it depends on imperfect global paths, which result in inefficiencies and safety issues. This thesis looks into the possibility of using a local path planning algorithm, specifically an Interpolating Curve Planner (ICP).

To assess the ICP's performance under various model hyperparameters and environmental scenarios, a simulation tool is created. The study examines how well the ICP performs in terms of area coverage, collection rate, and reliability along different hyperparameters and presurvey measurement resolutions. It integrates novel production rate objectives, previous track following, and real-time sensor measurements within an environment generated from bathymetric data.

The examined local path planner shows a step towards optimised DSNC. Further research is needed to refine objectives and the structure of the local path planning algorithm to maximize its potential in addressing DSNC challenges effectively.

The Global Shipping industry is responsible for transporting 90% of global commerce and is responsible for 3% of global greenhouse gas (GHG) emissions. Addressing this, the International Maritime Organization (IMO) aims to reduce GHG emissions from international shipping by 40% by 2030 and achieve net zero by 2050. This study explores Low Temperature-Proton Exchange Membrane Fuel Cell (LT-PEMFC) hybrid energy systems as a potential solution to reduce shipping emissions. Emphasizing the operational zero-emission capability of PEMFC fueled by hydrogen, the research scrutinizes the emission intensity from hydrogen production and the impact of component degradation on hybrid system efficiency and hydrogen consumption.

The research pivots around optimizing the design and operation of ship hybrid energy systems to minimize costs while considering well-to-wake (WTW) emissions and component lifetime. It investigates two hybrid configurations: PEMFC/Li-ion battery (LIB) and Diesel Generator (DG)/PEMFC/LIB. Employing a Mixed Integer Linear Programming approach for component modeling, the study conducts a two-stage analysis: design optimization considering various hydrogen sources and plant lifetime estimation focusing on PEMFC and battery degradation.

Initial findings reveal that system design costs do not significantly differ across hydrogen grades. The DG/PEMFC/LIB configuration emerges as cost-effective, reducing CAPEX by 62.8% compared to the PEMFC/LIB setup. Carbon Capture and Storage (CCS) hydrogen grades strike a balance between cost and emission reduction, notably cutting emissions by up to 85% in the PEMFC/LIB configuration at a 27% OPEX increase.

Lifetime estimation highlights the effectiveness of a hierarchical optimization method in mitigating PEMFC voltage loss and extending component lifespan, albeit with increased battery cycling aging. The study underscores the importance of selecting the appropriate hydrogen grade and operational strategies to enhance the sustainability and economic viability of maritime hybrid energy systems, aligning with IMO’s emission reduction goals.

Reduction of carbon emissions and greenhouse gases within the transportation sector is crucial due to its significant contribution to environmental degradation. To reduce this impact, it is important to develop and implement energy efficient systems. In addition to exploring renewable fuel alternatives, effective energy management systems are vital. This paper presents an optimization-based control referred to as Hierarchical Model Predictive Control along with a comparative framework, to evaluate its effectiveness as a real-time control strategy. Three energy management strategies are designed for this purpose, with the objective of minimizing the fuel consumption of a hybrid marine powertrain, while optimizing the power distribution among its components to achieve high operational efficiency.

The urgent need for marine transportation to reduce CO2 emissions has led to considering alternative energy and storage sources. The resulting increase in the complexity of powerplant architectures led to the development of advanced control strategies to achieve energy savings. At the same time, the health status of components has been often overlooked, leading to shortened powerplant lifecycles.

To address this issue, this paper proposes a novel health-aware control strategy for hybrid and full electric powerplants featuring fuel cells and batteries that simultaneously extend service life and reduce energy consumption. Specifically, the thesis identifies the most detrimental behaviour for powerplant components and propose a bespoke cost function to mitigate those.

To assess the effectiveness of the proposed control strategy, a comparative analysis will be conducted against a non-health-aware control strategie and a health aware control strategy from literature, to demonstrate the advantages of the proposed approach in terms of energy performance and service life extension, contributing to the development of sustainable marine transportation.

Chemicals within a chemical tanker need heating to keep the cargo above a desired temperature. This research focuses on the prediction of the fuel consumption used to heat the cargo. There are three methods discussed in this research: a physics-based model (PBM), a data-driven model (DDM) and a hybrid model (HM). The PBM is a model based on the physical relations defined in the theory of heat transfer. The DDM is a model based on data gathered by a chemical shipping company (Stolt Tankers). Here an algorithm is trained and tested to acquire the model. The last method is a hybrid model. This HM is the combination of both the PBM and the DDM to use the best of both worlds.
The PBM uses ordinary differential equations to model the fuel consumption and uses a numerical approximation. This method is first compared with a simulation in COMSOL Multiphysics and then scaled to an existing vessel. The PBM is able to predict the fuel consumption over a trip where the deviation in fuel consumption is 0.043 tons of fuel per day (5.6% deviation). The temperature deviation averaged over all tanks is 0.68 ∘𝐶. The DDM compares two algorithms: the linear algorithm based on the least squares method and an algorithm based on support vector regression (SVR). The model using SVR acquires the most accurate result, with a mean absolute error (MAE) of 0.0014 ± 1.7880 ∗ 10−4 and a mean absolute percentage error (MAPE) of 5.18616 ± 1.05567. When the PBM and the DDM are combined into one model the HM is formed. The HM is able to predict the fuel consumption of the vessel with a MAE of 0.0001441 ± 0.00010 and a MAPE of 0.8929 ± 1.0955.
The PBM could also be used to calculate the reduction in fuel consumption when insulation is applied. Different types of insulation are simulated, with different thicknesses and thermal conductivity properties. Insulation of the cargo tanks could potentially reduce the cargo heating consumption by 64.8%. This reduction makes it worth looking into the insulation of vessels to reduce the fuel consumption and emissions. The DDM and HM are also tested to see if these models are able to predict values outside the range for the trained data. The DDM is not able to do so, whilst the HM is able to calculate scenarios where the average cargo tank temperature is outside the range for trained data.

Hybrid technology can significantly reduce fuel consumption and emission for vessels that have high power demand peaks followed by long periods of low loading. Hybrid technology refers to powertrain layouts that consist of hybrid propulsion and/or hybrid power supply. Advanced energy management strategies (EMS) are required to make optimal use of these available power resources. In this paper it is investigated how much fuel consumption reduction can be achieved by applying a causal, real-time equivalent consumption minimization strategy (ECMS) to a hybrid propulsion and hybrid power supply plant with a power load forecasting scheme, for a case study vessel: The Holland-class offshore patrol vessel. The forecasting tools evaluated are Linear regression, moving average, ARIMA and recurrent neural networks (RNN). The RNN outperformed the other methods and is able to predict the power demand for up to 48 seconds, while maintaining a mean absolute percentage error of under 5%. An optimization-based controller is combined with an ECMS approach which assigns an equivalent consumption cost to the battery. The controller is able to identify the power split for the hybrid propulsive system, and the power split for the hybrid power supply. A simulation proved that 0.77% fuel savings are achieved with a 400 kW battery, compared to a no-battery scenario. Fuel savings could not be proven for the EMS with a control horizon of 48 seconds, leveraging power predictions supplied by the RNN, due to limiting factors. The limiting factors are the combination of the small control horizon, the limited battery capacity compared to the overall power demand and the limited tuning of the ESFC curve.

This research analyses the potential of Electrical Signature Analysis (ESA) as a means for condition monitoring of mechanical defects of induction motor driven thruster assemblies in the maritime industry. This is done by measuring the three phase current and voltage of three thrusters, with different health conditions, of a marine vessel sailing at different speeds. In this data, the mechanical behaviour of the machines is detected by plotting the frequency spectra of various electrical parameters, such as complex current and torque. After successfully identifying mechanical behaviour and defects, a suggested approach for the implementation of ESA as a means for condition monitoring is presented.

Transshipment is a key component of modern-day shipping logistics. Container supply chains rely on transhipment hubs to access remote locations. With globalisation driving growth in container trade, maritime congestion is rising at container terminals in ports worldwide. This is expected to worsen as demand continues to grow. The ill effects of this congestion are particularly being felt along transportation networks between deep-sea container terminals and Hinterland terminals. Therefore, this research aims to suggest solutions that address current problems and meet current targets, while also delivering future-proof and scalable transshipment options across a range of ports and hinterlands worldwide. This work in particular will evaluate the impact of novel maritime concepts such as amphibious AGVs, floating terminals, and super barges and how they can contribute to developing efficient container transportation networks that could address the congestion problem of incoming container barges at deep-sea container terminals. The key performance indicators (KPIs) emphasize reduced maritime congestion, while also aiming to achieve similar daily throughput, transport time, and reduced transporter fleet sizes. To implement this, an agent-based simulation methodology quantifies the existing problems along the deep-sea-hinterland network and validates the feasibility of the proposed transportation networks. The proposed innovative concepts and the current container transshipment scenario are implemented in the simulation environment of the Port of Rotterdam. The global applicability and scalability potential of the proposed transshipment solutions is further demonstrated on a much larger scale by testing them in the Hong Kong-Pearl River Delta, which covers an area similar to the size of the Netherlands. From an innovation point of view, concepts like Amphibious AGVs and floating terminals depict versatility in mobility and flexibility in execution respectively. This work, therefore, opens up an intriguing future scope for maritime transshipment that is both sustainable and adaptable, while also discussing limitations and concerns that need to be carefully considered.

With the rising demand for renewable energy, floating offshore wind turbines have gained importance, particularly in regions where deep waters prevent the use of traditional monopile structures. These floating turbines rely on mooring lines for stability against environmental conditions, particularly when facing strong winds and high waves. Ensuring a satisfactory lifetime and health of mooring lines is critical. Moreover, degradation can compromise the turbine’s functionality or even lead to catastrophic failures. While direct monitoring is ideal, it is often hampered by high costs and extensive maintenance. This Master’s thesis introduces a novel method to assess mooring line degradation. The proposed approach simulates the impact of environmental conditions on the mooring lines, considering various forces and weather scenarios. The research presents modeling of fatigue and corrosion effects along the mooring line. A unique corrosion model calculates variations based on seawater’s oxygen and temperature profiles. Concurrently, mooring line stresses are deduced from real-world environmental conditions. Integrating these, a finite element model is constructed to analyze different load scenarios and the onset of corrosion on line degradation. The model considers the joint impact of corrosion and fatigue on mooring lines, including the influence of hydro static pressure and out-of-plane bending. Validation of this methodology draws upon existing research and experimental results on mooring lines.

It is known that the Dutch North Sea coast consists mostly of sand, which creates the need for continuous maintenance.
For the time being, these maintenance activities are carried out with the aid of trailing suction hopper dredgers (TSHDs), which emit greenhouse gases during dredging operations.
As the world continues to prioritize sustainability, the field of marine engineering is no exception.
The Dutch Rijkswaterstaat goal is to reduce these emissions to zero by 2030 at the latest.
For this reason, C-Job Naval Architects has designed the Autonomous Low Energy Replenishment Dredger (ALERD) to meet the anticipated increase in Dutch coastal maintenance while simultaneously providing sustainable and cost-effective solutions for such maintenance work.
The development of autonomous replenishment dredgers has to be further investigated in order to determine the energy requirements and if it is a worthy future investment.

Regarding the research’s approach, first, the environmental conditions need to be determined in order to identify the environment in which these submarines will operate and the relevant destabilizing forces that they create. Ocean currents, ocean waves, as well as seabed interaction with submarines and generally underwater vehicles will be reviewed with respect to modelling, as they can have a significant impact on stability and manoeuvrability.
In order to counteract the developed forces, forward thrusters, vertical tunnel thrusters, and the trim tanks will be modelled.
Investigating the aforementioned topics, the simultaneous research about the modelling of the environmental forces and the seabed interaction on underwater vehicles and the existence of a model that can estimate the energy consumption is recognized as a “gap” in the literature.
As a result, a simulation model for an underwater vehicle, especially a submarine that operates in shallow water, that takes into account all the previously mentioned destabilizing factors and counteracting forces, will contribute to the advancement of a model that effectively estimates the required energy, using Matlab, Simulink.

In the developed model, three PID controllers in the three different degrees of freedom, surge, heave and pitch, are applied.
They control the rpm for the forward thrusters and the vertical tunnel thrusters in surge and heave respectively.
The PID controller in pitch direction adjusts the water mass that needs to be transferred from one trim tank to another in order to handle the pitch motion.
The ALERD is used as a case study for the generated model.

At the present phase of the ALERD concept, detailed hydrodynamic characteristics remain unavailable. The undertaking of CFD simulations or scale-model tests is deemed undesirable due to their perceived expense and time-consuming nature, particularly given the incomplete status of the ALERD's key parameters.
The mathematical equations that describe the former are modelled in Matlab, Simulink to perform time-domain simulations.
The ALERD has an unique operational profile in which it operates underwater in all three of its operational modes: transit, dredging, and discharge. This operational profile for the ALERD is based on conventional dredgers working in coastal replenishment along the Dutch coastline. Each operating mode has its own set of criteria, such as the goal depth, desired speed, and operational time.

It is worth noting that given the operational profiles for the velocity in surge, the depth and the pitch, the ALERD follows them successfully.
Nevertheless, the presence of the waves cause an oscillating behavior of the ALERD in heave and pitch directions which cannot be eliminated if only PID controllers are utilised.
The PID controllers fail to cancel out the oscillations by themselves.
Since the implementation of a different control strategy is not in the scope of this project, a different solution needs to be explored to estimate the required energy.
For this reason, a low-pass filter is applied at the resultant rpm of the PID controller in heave direction in order to make the oscillating behaviour of the rpm converge to a value, and calculate the required thrust.
As a result, the consumed energy for a full dredging cycle is determined per actuator.

It needs to be mentioned that when factoring in all environmental conditions and seabed interactions, the ALERD exhibits a maximum overshoot of 6.25\% when descending to a depth of 8 meters. This deviation is not considered critical, as it poses no risk of grounding.
In the case of operation at a depth of 15 meters during dredging conditions, the combined maximum overshoot, which accounts for both the depth controller and pitch controller, is estimated at 7.3\% (0.58m). This level of overshoot is considered safe, given that the ALERD has a clearance of 2 meters.
Regarding the energy results, it is concluded that the forward thrusters consume 4192kWh, the vertical tunnel thrusters 340kWh and the pump for the trim tank system 0.2kWh.
These results cannot be verified since the ALERD is a unique concept design and there are no available data of similar submarine dredgers.
However the developed model can be used for the estimation of the required energy of similar designs that are in a preliminary design phase.
Furthermore, the maximum demanded power for the thrusters and the pump can be determined using the model, in order to define the size of the necessary machinery.

The concluding recommendations mostly revolve around the functionality improvement of the tool, in order to increase its quality and generate models of higher accuracy.
An adaptive PID controller is recommended to be implemented, since the gains for the PIDs can change dynamically. By using adaptive coefficients, a PID controller can be made more robust and versatile, able to handle nonlinearities, and changes in system dynamics.
Furthermore, a state-observed is suggested to be applied in combination with the already existed PID controllers in order to estimate and compensate for unknown environmental forces in real-time and eliminate the oscillating behaviour.
Last but not least, the hydrodynamic coefficients should be improved to the actual hull shape of the submarine in further investigation.

A significant decrease in greenhouse gas emissions can be achieved by including a prediction of future power consumption in the control of ships’ power plants during transits. Moreover, including a prediction of power consumption in the Energy Management System (EMS) during Dynamic Positioning (DP) operations can also contribute to a reduction in greenhouse gas emissions. However, predictions of future power consumption for DP operations, as well as its implementation in an EMS, have not been investigated widely, since this is a complex task for a relatively specific application. This research aims to investigate how power consumption during DP operations can most accurately be predicted for near-future and far-future, within reduced computational requirements for real-time applications.

In this research, six different approaches for power consumption prediction are investigated. These approaches can be categorized into projection models, which determine the power consumption independent of time based on the environmental conditions, and forecast models, which rely on the recent past behaviour of the system.
The four projection models consist of two Physical Models (PMs), a Data-Driven Model (DDM), and a hybrid model. The first PM is the static model, in which the environmental loads are directly compensated by the thrusters, hence the ship is assumed not to move. The second PM is the dynamic model, in which the motions of the ship are also modelled. Then, the DDM projection is based on Kernel Regularized Least Squares (KRLS) to capture the nonlinear relation between environmental conditions and power consumption based on historical data. Lastly, the hybrid projection model is an integration of the dynamic model in the DDM.
Afterwards, two forecast models are developed, being one DDM and one hybrid model. The DDM forecast is based on a combination between KRLS and Time Series (TS) forecasting. The hybrid forecast is an integration of the dynamic model in the DDM forecast.

Simulations are performed for each model using a data set provided by RH Marine. The DDMs show the most accurate results, taking into account the required computational effort. The results show that the forecast model, using the combination between KRLS and TS, predicts the near-future at 1 s in the future with 2.8 % error, increasing to 6.9 % at 120 s in the future. Afterwards, the projection model, using merely KRLS, predicts the far-future up to the length of the operation horizon with an accuracy of 11.2 %, based on the weather forecast. This multi-horizon prediction of power consumption can enable the EMS to make accurate short-term decisions in the control of the power plant and to define optimal scheduling of the power plant components over the
complete horizon of the DP operation.

Due to the expected increase in coastal replenishment along the Dutch coastline, and the need to decrease pollutant emissions in the Netherlands and worldwide, the Autonomous Low Energy Replenishment Dredger (ALERD) is developed. The ALERD is a sustainable and cost-effective solution to perform coastal replenishment work along the Dutch coastline and will be used for submerged dredging operating fully electric. The ALERD is modelled as an underwater vehicle in six degrees of freedom, from which the forward speed, the depth, and the pitch angle are controlled using motion controllers. The ALERD is the successor of the Autonomous Underwater Maintenance Dredger (AUMD), for which it is shown that the power for propulsion can be reduced with 55%, and the power required for the dredging pumps can be reduced with 80%, due to the submerged operations. A simulation model is used to determine the required power for stability and buoyancy control of the ALERD, since this was up to this point still unknown. To be a cost-effective solution, the energy consumption for motion control should be minimized. Traditional motion control methods such as proportional-integral-derivative (PID) control are widely used for controlling underwater vehicles. The more advanced motion control method Model Predictive Control (MPC) is seen as a more promising motion control method, in terms of both trajectory-tracking and minimizing the control effort. A Model Predictive Control strategy is developed and used to minimize the energy consumption of the ALERD for stability and buoyancy control. It is shown that MPC indeed outperforms PID control on tracking the desired reference signal, while minimizing the control effort and thus the corresponding required energy. A sensitivity analysis is done using Monte Carlo Simulations, to show the effect of modelling uncertainties on the outcomes of the simulation model. There is a minimum variation in the outcomes, considering the modelling uncertainties. MPC is better capable of handling with modelling uncertainties compared to PID control, in terms of trajectory-tracking and minimizing the corresponding energy requirements. The MPC controlled ALERD is compared with the PID controlled ALERD, and a conventional dredger, and it is shown that for a similar operational profile, the MPC controlled ALERD is the most energy efficient solution. The results from the simulation model still show significant advantages of submerged dredging compared to conventional dredging, even when considering the energy demand for stability and buoyancy control, based on the used operational profile.