The pressure to reduce emissions in the sector of inland shipping is increasing. Especially considering emissions of environmental pollutants, the increasing pressure gives rise to the question how to get insight into emissions distributions along an inland waterway network. In this research, a bottom-up method is developed that is able to map the potential CO2, PM10 and NOx emissions levels of a single inland vessel on a certain waterway network, as a function of time and space. This method uses the dimensions of the ship, its speed and the waterway characteristics to estimate the energy consumption and corresponding emissions of a vessel. This method can be used in the support of development and evaluation of emission reduction policies. To illustrate the potential of this method, it is applied to a case study: the inland fleet on the Rotterdam-Antwerp corridor, one of the main transport axes in the Netherlands. The developed method is applied to observed AIS data, to map the current potential emission patterns (‘t0 emission scenario’). In addition, a model has been developed that simulates the ‘t0’ case. This model serves as a tool to assess alternative measures to reduce emissions.

Inland shipping is widely acknowledged as a sustainable mode of transportation due to its low energy consumption and emissions in comparison to road and rail transport. However, with growing concerns around reducing emissions in the transportation sector, there is pressure to address environmental issues associated with inland shipping. In the Netherlands, a Green Deal has been formulated to outline the goals for reducing CO2 emissions by 2030 and other environmental pollutants by 2035 in inland navigation, to enable us to take the next step towards a climate-neutral society by 2050. This increasing pressure raises the question of how to get insight of the energy consumption and the associated emissions from inland shipping. To date, an accurate method is lacking that is able to estimate the total resistance, the propulsive power and in turn the energy consumption in shallow water and thus to quantify the CO2 emissions. Over the years, several power estimation methods have been developed for inland vessels, with the Rijkswaterstaat power estimation method being one of the most widely recognized. Recently, Backer van Ommeren (2019) investigated the Rijkswaterstaat power estimation method and found that certain assumptions and parameters used in the method were not well-founded, and that some approximations were unnecessary. The main objective of this study to conduct a comprehensive literature analysis on Backer van Ommeren (2019) comments and recommendations regarding the Rijkswaterstaat (RWS) or Bolt (2003) method in order to clarify to what extent these recommendations will indeed improve the Bolt (2003) method or if an alternative power method should be proposed instead. This will be accomplished through a comparison process of the power results as a function of sailing speed, water depth, and channel dimensions for various types of inland vessels, utilizing the selected methods that will be derived from the literature study along with Backer van Ommeren (2019) recommendations applied to the original method. After coding these methods in Python and analyzing their results, the best practice(s) that will be derived from the test cases, will be implemented on two classes of motor vessels an M6 and an M8 to estimate the resistance and the power and then they will suggest to Rijkswaterstaat for potential future use. To achieve the main research objectives, the following research was conducted. Initially, a literature analysis on the available resistance methods, how they consider and divide the several resistance components, and which are the shallow water effects that affect them, was done in order to evaluate their performance in terms of power estimations. Secondly, the comments made by Backer van Ommeren were presented and analyzed. Specifically, he investigated various formulations for calculating the return flow, water level depression, and characterizing the waterway as normal, narrow, wide, or very wide. This study was accomplished through the use of specific power efficiency and resistance coefficients. Based on his study, he derived a method, the Backer method (Backer van Ommeren, 2019) and suggested a number of formulas to be further tested. After completing the literature review, the findings lead to the selection of the power methods that will be treated in this thesis and the kind of improvements that will be applied to the original Bolt (2003) method. Subsequently, from the literature study and Backer van Ommeren (2019) review, four methods were derived to be simulated and tested in this thesis. These methods include the TU Delft method, Bolt method with speed correction, Bolt method modified by Backer, and Backer method. The simulation was achieved with two rounds of tests that are conducted, firstly the “Academic test case” and secondly the “Real-world test case”. In the “Academic test case” five methods were simulated and the most promising that met specific criteria are selected. Then, in the “Real-world test case” the selected methods as they were derived from the “Academic test case”, were further evaluated for the selection of the best practice(s). The first round of tests is applied to two classes of motor vessels in narrow and wide waterways with shallow, intermediate and deep water depth conditions and the results include the total resistance and the brake power while the second round simulates only one motor vessel of class six in wide waterways for the same depth conditions as previously and the outcome includes the delivered power. The “Real-world test case” is divided in two parts. The first part includes the comparison between the estimated and the measured delivered power in order to assess the performance of the methods with the real data. The second part evaluates the performance of the methods in the presence of a current flow, by comparing the fuel consumption in upstream, downstream and round trips. The evaluation of the methods in a real-world test case led to a number of conclusions, and the best practices were recommended accordingly. It should be noted that the comparison process was based solely on a single real-world case, utilizing a singular set of real data. It is important to be conducted additional comparisons across multiple real cases in order to increase the understanding of the accuracy of the various methods being compared. In the context of power estimation in shallow water, both the Bolt (2003) method and TU Delft method(Jiang, Baart & van Koningsveld, 2022) have demonstrated remarkable accuracy in their predictions while Backer method and Bolt method modified by Backer are not recommended for power predictions. Notably, Bolt (2003) method has proven to be effective in estimating power within a speed range of 2.5m/s to 3.5m/s while TU Delft method (Jiang, Baart & van Koningsveld, 2022) showed accurate predictions within a speed range of 2.5m/s-4m/s (accurate as defined within 20% of the observed value). Regarding the intermediate and deep water conditions, only TU Delft method (Jiang, Baart & van Koningsveld, 2022) showed acceptable performance in power estimation again for sailing speeds varying 2.5 m/s – 5 m/s. The power demand at very low speeds for all the three methods display a considerable deviation between the estimated power output and the actual values, surpassing the acceptable rate of 20%. This can be attributed to two reasons. At low speeds, the interaction between a sailing vessel and the boundary layer becomes more pronounced, causing the ship to experience turbulent effects that dominate the boundary layer more intensively. As a result, the vessel experiences increased resistance, requiring more power. Secondly, in actual operating conditions, a ship has a minimum power engine setting that is dependent on the engine characteristics. So, when the ship is moored and the "hotel mode" is on, as the ship not having a separate auxiliary power unit, a propeller brake is used to allow the turbine to continue running and generate power without the propeller spinning. This effect does not consider by the power estimation methods that rely on parameters such as sailing speed and water depth. The Backer (2019) method demonstrated satisfactory performance in predicting resistance and power for both types of motor cargo vessels within narrow waterways. This method effectively accounted for the variations in depth by accurately estimating lower resistance and power demand as the depth increased. However, Its accuracy in wide waterways diminished due to the equations' unsuitability for such conditions, by generating nearly identical resistance and power estimations for the three different water depths. Based on the aforementioned restriction, it is not recommended to employ this particular approach for subsequent power estimations. As regards the Bolt method modified by Backer performs poorly in estimating resistance and power across narrow and wide waterways with varying depths. It consistently yields similar results for shallow, intermediate, and deep depths at a specific sailing speed. Therefore, it is not recommended as an improvement to the Bolt method. In the presence of current flow, three methods have shown promising results. Specifically, the TU Delft method (Jiang, Baart & van Koningsveld, 2022) is recommended for motor vessel, as it produces deviations from real measurements of 0.93% for upstream, 1.36% for downstream, and 0.45% for round trips. Also, TU Delft method (Jiang, Baart & van Koningsveld, 2022) is recommended in case of pushed and coupled convoys as it has been found to produce the smallest deviations in upstream sailing, with a maximum of 3.9% while the deviations observed for downstream sailing and round trips are around 1.9%. Bolt (2003 )method and Bolt method with speed correction, were found to produce acceptable deviation rates of around 7% for upstream trips, with the benefit that these methods require less detailed input data. Nevertheless, for downstream and round trips, the deviations were much higher, reaching up to 80% and 30%, respectively and event that requires additional investigation and validation.

This research focuses on a new system for nourishing the Dutch coast with sand, which utilizes semi-autonomous sailing barges and an electrical crawler to continuously replenish the beach. Yearly, the Dutch government grants contracts to nourish the Dutch coast with volumes around 10.000.000 m$^3$. The Dutch dune coast protects the low part of the Netherlands against inundation from the North sea. This sand is dredged by Trailer Suction Hopper Dredgers (TSHD) on a depth of 20 m and transported to the coast. This transport causes a CO2 emission of about 190 kilo tonne a year. As emissions are expected to grow with increasing sea levels the Dutch government looks for ways to improve the high-energy demanding strategy that is currently used. Sweco introduced the Slow Sailing, and believes in the energy reducing concept and therefore initiates this further research. The general idea of this concept is to reduce the energy demand and emissions associated with traditional sand nourishment practices by using multiple barges that sail slowly and continuously nourish the Dutch coast. The aim of this research is to concretize this concept, in which feasibility of the system compared with energy, emission and cost calculations are central. To achieve this goal, the Slow Sailing system is compared to the traditional trailer suction hopper dredger, currently used for sand nourishment in the Netherlands. The comparison is based on a scenario in the Northsea. This leads to the main research question: What is the gain in energy consumption, emissions and cost for the Slow Sailing concept compared to classical nourishment strategies? In order to achieve this goal a review of current sand nourishment strategies was conducted. Next, the OpenTNSim model was adapted to perform energy calculations and enable a comparison of energy demands and emissions between the new system and the traditional trailer suction hopper dredger. Finally, a cost evaluation was conducted to compare the cost per cubic meter of sand for a 1.000.000 cubic meter nourishment project at the Egmond aan Zee nearshore in the Netherlands. Compared to the traditional trailer suction hopper dredger, the Slow Sailing system showed a significant factor 15 decrease in energy demand in the sailing component of the dredging cycle. The Slow Sailing system with barges of 1500 m$^3$ was found to be the most energy-efficient and cost-effective option, with an estimated project cost of 3.00 euro/m$^3$, while the TSHD with 4280 m$^3$ was found to be the cheapest option, with an estimated project cost of 2.55 euro/m$^3$. In terms of CO2 emissions, the Slow Sailing system emitted significantly less CO2 compared to the TSHD, with a difference of 1000 tonnes CO2+ emission in a one million cubic meter dredging project. Overall, the results suggest that the Slow Sailing system is a more energy-efficient and environmentally-friendly option for sand nourishment of the Dutch coast.

The dredging industry is a contributor to the greenhouse gas (GHG) emissions due to the energy-intensive process of dredging and transporting sediment. This industry has to reduce the GHG emissions due to future possible restricting regulations on GHG emissions. The dredging industry is shifting towards more environmentally friendly fuels, but designing new vessels which can run on these fuels will be a time consuming transformation. Though, the demand of dredging activities is growing, think of construction of offshore wind farms and creating flood defences against sea level rise. To minimize the costs due to the high GHG footprint in the short term, Van Oord should be able to estimate the energy consumption (GHG emissions) based on different dredging strategies. Van Oord uses a model to estimate energy consumption that accurately describes the majority of dredging projects, based on the power installed, dredging time, and a power coefficient term. This power coefficient term is based on empirical data and may result in a less accurate estimation of more complex (new) projects such as offshore wind farms and cable dredging, which are the new opportunities arising for dredging companies. To better estimate energy consumption during these complex dredging operations, a physics-based and semi-empirical method is developed that can more accurately estimate energy consumption based on certain dredging strategies. Previous research already looked at the sailing phases of the dredging cycle, therefore the main objective of this thesis is: To quantify the energy consumption of different TSHD dredging strategies based on physics for the loading phase. To achieve this research objective, first a literature study is conducted to list the different physics-based methods available to estimate the energy consumption. The energy consumption of a TSHD is divided into five main energy consumers (propulsion system, dredge pumps, jet pumps, bow thrusters and board net) in which the energy of the individual components is described by a power and a time term. The time component is represented by the duration of the loading cycle, which in this study is limited to the filling process of the hopper until overflow. The power term of the jet- and dredge pumps have been estimated by the (adjusted) in-house physics-based methods of Van Oord. The board net and the bow thrusters have been estimated by data analysis of a case study. The goal of this research is to develop a method to estimate the required propulsion power and a tool to bring all methods together to simulate the total energy consumption during the loading phase based on different dredging strategies. A model is developed to estimate the required propulsion power during loading. The model development is divided into three phases; The first phase describes the development of a semi-empirical physics based model. In this, the various resistance forces acting on the vessel, suction pipe, and draghead are calculated based on stationary parameters such as vessel dimensions, and project parameters. The project parameters, including water depth, trailing speed, and visor angle, are extracted by filtering the actual data from the case study. In this way the estimation model is set equal to a reference case. Finally, the output of the estimation model is calibrated by comparison with the actual data of the case study. This calibration phase is an iterative process in which the accuracy of the model is described and increased. The model shows that it is possible to calculate the required propulsion power based on operational parameters, such as trailing speed. Furthermore, the model provides insight into the amount of resistance on the three components (draghead, suction pipe and vessel). The power curve follows a quadratic pattern for increasing trailing speed, which appears to be a reasonable estimate. When comparing the model to the actual data it can be seen that the model underestimates at lower trailing speed and over estimates at higher trailing speed. By finding correlations between visor angle and trailing speed the model is calibrated and seems to better fit the dataset, however the slope of the curve still has a large deviation compared to the regression line based on the actual dataset. Therefore, the model needs to be further developed before it can be included as an addition to the existing models within Van Oord. The static friction term is not included, which could compensate the underestimation of the model and the high cutting forces are the potential reason for the overestimation at high Van Oord Marine Ingenuity iii Delft University of Technology trailing speed. For now, the current developed model falls within the reference dataset for the trailing speed between 1.6 [kn] and 1.9 [kn], thus making it a reasonable first estimate of the required propulsion power. The OpenCLSim python package, which can be used to simulate discrete events, is used to simulate the dredging processes of the loading phase. A plugin for this python package is developed to calculate the total energy consumption during these processes. The propulsion model is integrated (with the set limitations) in this plugin together with the four other power estimation methods (dredge pumps, jet pumps, bow thrusters and board net). The simulation tool runs based on four input characteristics: vessel parameters (TSHD), site characteristics, dredging strategy, and data describing the bow thrusters and board net. The output of the simulation includes required power, duration, and consumed energy. Additionally, the simulation estimates the fuel usage, emissions, and project costs associated with energy consumption. The main ability of the simulation tool is that it can visualize the energy consumption (and emissions) based on dredging strategies and location. This enables the prediction and potential reduction of emissions in sensitive areas, such as fine dust emissions near cities. By using this simulation tool, a dredging plan can be created based on dredging strategies (trailing speed) to reduce emissions in these sensitive areas. To better demonstrate the other capabilities of the developed plugin within the OpenCLSim Python package, the simulation tool is applied to an imaginary project called Barachi. This project has strict regulations that prohibit overflow and apply emission taxes. Two dredging strategies are compared based on the limitations of the developed propulsion model: trailing speeds of 1.6 [kn] and 1.9 [kn]. The results show that, first of all, the project duration will decrease with an increasing trail speed. Secondly, the increase in power has a greater effect on energy consumption than the decrease in project duration, meaning that energy consumption will increase. Since fuel use and corresponding emissions are linked to energy consumption, they will also increase. However, the cost analysis provides a different view. In this case, because the ship’s operating costs are governing, faster trailing is the most cost-efficient, even if an emission tax of 200 euros/ton CO2e is applied. To conclude, by creating a model to estimate the propulsion power and by integrating this model in a simulation tool, it is possible to quantify the energy consumption of different TSHD dredging strategies based on physics for the loading phase. Subsequently, the simulation also provides insight into the fuel usage, carbon emissions, and costs of the project. Further development and validation of the propulsion model is needed to give a more accurate estimation of the required power. Firstly, the static friction component should be included in the estimation model which could potentially solve the underestimation at lower trailing speed. Secondly, a maximum trail speed limit should be set based on the available propulsion power with respect to the total power distribution of the vessel. This limit should be included in the simulation tool, since it is linked to the other power consumers and the operational parameters. Thirdly, the propulsion model can only be used in saturated sand projects. Other cutting models based on cutting of silt, clay and rock should be added in the propulsion model to be able to apply the model in these soil conditions. Finally, the use of the tool is limited to dredging projects where overflow is not allowed. The suction production and the settling process within the hopper should be added to expand the capabilities of the tool to projects where overflow is possible.