Carbon dioxide capture and storage combined with enhanced deep saline water recovery (CCS-EWR) is a potential approach to mitigate climate change. However, its investment has been a dilemma due to high costs and various uncertainties. In this study, a trinomial tree modelling-based real options approach is constructed to assess the investment in CCS-EWR retrofitting for direct coal liquefaction in China from the investor perspective. In this approach, the uncertainties in CO₂ prices, capital subsidies, water resource fees, the residual lifetime of direct coal liquefaction plants, electricity prices, CO₂ and freshwater transport distance, and the amount of certified emission reductions (CERs) are considered. The results show that the critical CER price for CCS-EWR retrofits is 7.15 Chinese yuan per ton (CNY/ton) higher than that (141.95 CNY/ton) for CCS retrofits. However, the exemption from water resource fees for freshwater recovered from saline water and a subsidy of 26% of the capital cost are sufficient to eliminate the negative impact of enhanced deep saline water recovery (EWR) on the investment economy of CCS-EWR. In addition, when the residual lifetime is less than 14 years, CCS-EWR projects are still unable to achieve profitability, even with flexible management and decision making; therefore, investors should abandon CCS-EWR investments. On the whole, the investment feasibility for CCS-EWR technology is not optimistic despite access to preferential policies from the government. It is necessary to establish a carbon market with a high and stable CER price.

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Modeling is a promising approach to understand and predict the safety and efficiency of maritime traffic in ports and waterways. Different types of models have been developed over the years. Nevertheless, several important scientific challenges still remain. For instance, few models consider vessel behavior in ports and waterways under the influence of internal factors including vessel type and size, and external factors, such as wind and visibility. More data and research are needed to understand the influence of internal and external factors on vessel behavior including speed, course and path in ports and waterways; more research is also needed to explore human behavior of the bridge team for vessel maneuvering in ports and waterways. To address the needs listed, this thesis focuses on analyzing the influence of wind, visibility, current and vessel encounters on vessel speed, course and path using Automatic Identification System (AIS) data. Based on this analysis a new maritime traffic model has been developed that considers both internal and external factors, and aims to better predict the individual vessel behavior. The model can be used to provide data for the safety and efficiency assessment of vessel traffic in ports and inland waterways. In the last decades, the AIS system, which is an onboard autonomous and continuous broadcast system that transmits vessel data between nearby vessels and shore stations, has been developed. This is used now by almost all vessels. Therefore, AIS data, including vessel speed, course and path, can serve as a valuable data source to investigate vessel behavior. In this thesis, AIS data from a part of the port of Rotterdam is analyzed to investigate influences of different factors, such as vessel size and type, external conditions and vessel encounters, on vessel behavior. Firstly, vessels are distinguished into influenced and unhindered vessels based on certain thresholds that we obtained from the AIS data. The influenced vessel behavior is compared with the behavior of unhindered vessels, which are not influenced by other vessels or strong external influences of wind, visibility and current. The analysis provides evidence showing that the vessel behavior including vessel speed, course and path is influenced by various factors. Ship speed and path is influenced by internal factors (including vessel type, size, waterway geometry and navigation direction) and external factors (including wind, visibility, current, overtaking and head-on encounters), while ship course is only influenced by overtaking and head-on encounters. It can also be concluded that the AIS data is a useful source to get insights into vessel behavior. @en

The Vessel Maneuvering Prediction (VMP) model, which was developed in a previous work with the aim of predicting the interaction between vessels in ports and waterways, is optimized in this paper by considering the relative position and vessel size (length and beam). The calibration is carried out using AIS data of overtaking vessels in the port of Rotterdam. The sensitivity analysis of the optimal parameters shows the robustness of the calibrated VMP model. For the validation, the optimal parameters are used to simulate the whole path of overtaken vessels and vessels in head-on encounters. Compared to the AIS data, the validation results show that the different deviations in longitudinal direction range from 33 m to 112 m, which is less than 5% of the waterway stretch. Both the calibration and validation show that the VMP model has the potential to simulate vessel traffic in ports and waterways.

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The impact of many external factors, such as wind, visibility and current, on the behavior of vessels in ports and waterways has not been investigated systematically in existing maritime traffic models. In order to fill the current knowledge gap and provide a basis for developing a new model to effectively simulate maritime traffic, the influences of wind, visibility and current as well as vessel encounters on vessel behavior (vessel speed, course and relative distance to starboard bank) have been investigated in this study by analyzing Automatic Identification System data collected from the port of Rotterdam. It is found that wind, visibility, current and encounters have significant impact on the vessel speed and relative distance to starboard bank, while vessel course is mainly affected by current and encounters. The results also showed that the vessels would adapt their speed, course and relative distance to starboard bank during encounters. These findings showed the importance of considering external factors and encounters in simulating vessel behavior in restricted waterways and provide a starting point for building up more comprehensive maritime traffic models.@en

Because of ever-increasing economic globalization, it is necessary to simulate vessel behavior for investigating safety and capacity in ports and inland waterways. A new maritime traffic model was developed; it comprises two parts: the route choice model and the operational model. This paper presents the operational model, which describes vessel sailing behavior by optimal control. In the operational model, the main behavioral assumption is that all actions of the bridge team, such as accelerating and turning, are executed to force the vessel to sail with the desired speed and course. In the proposed theory, deviating from the desired speed and course, accelerating, decelerating, and turning will provide disutility (cost) to the vessel. Through prediction and minimization of this disutility, the longitudinal and angular acceleration can be optimized and predict individual vessel sailing behavior. To verify the route choice model and the operational model, a case study was carried out; it applied the models to predict individual vessel behavior (path, speed, and course) in the entrance channel to Maasvlakte I at the Port of Rotterdam, Netherlands. The simulation results show a good prediction of the vessel path and vessel course. As no other model has been built specifically to predict vessel behavior in the port area, the current methods provide a fundamental basis for investigating vessel behavior in restricted waterways. In addition, this research showed the potential of the model to increase the safety and capacity of ports and inland waterways.@en