In today's digitally connected world, the spread of conspiracy theories on social media poses a significant challenge to societal trust and public discourse. This thesis aims to develop a model for identifying conspiracy theories on Telegram, a platform known for its private nature and the use of channels to disseminate information. The main research question guiding this study is: *How can conspiracy theories be identified on Telegram?* To address this, the research leverages graph theory, machine learning, and topic modeling.

The first step involved modeling the network structure of Telegram channels using graph theory. Channels were represented as nodes, and forwarded messages as directed, weighted edges, allowing for the analysis of network structures and the identification of influential channels. Various centrality measures, including weighted degree centrality, betweenness centrality, and viral message centrality, were computed to assess the influence of channels within the network.

A fine-tuned multilingual BERT (m-BERT) model was used to classify Telegram messages as either conspiracy-related or not. This model was trained on a manually labeled dataset and demonstrated robust performance.

To identify specific conspiracy theories, BERTopic, a topic modeling technique, was applied to the messages classified as conspiracy-related. The resulting topics were then analyzed using the OpenAI API, which linked them to known conspiracy theories. The study found that all identified topics could be connected to existing conspiracy theories, suggesting that the model is effective in detecting these narratives on Telegram.

The research also included the validation of the model using a fictive conspiracy theory, "The Verdant Shadow Conspiracy," created specifically for this purpose. This validation demonstrated the model's ability to detect even simulated conspiracies, though it highlighted the importance of continuous refinement and expansion of the dataset.

The contributions of this thesis are twofold: scientifically, it advances the understanding of how to model and analyze Telegram networks to detect conspiracy theories, and societally, it offers a framework that can be used to monitor and potentially mitigate the spread of harmful misinformation. Future research should focus on expanding the dataset, refining the model, and exploring interdisciplinary approaches to further enhance the detection and understanding of conspiracy theories on Telegram.

The aviation industry is undergoing rapid expansion, with an annual growth rate of 4.5% to 5%, positioning it as the fastest-growing mode of transportation worldwide. This growth, however, comes with significant environmental challenges, primarily due to the industry’s substantial contribution to greenhouse gas emissions. Currently, aviation accounts for 2.4% of global CO₂ emissions and 3% of the European Union’s total greenhouse emissions. If current trends persist, these figures are expected to rise dramatically, intensifying the urgency for the industry to align with international climate agreements, such as the United Nations Framework Convention on Climate Change (UNFCCC), by adopting eco-conscious strategies to reduce both CO₂ and non-CO₂ emissions. One way to achieve this is by reconsidering their airline networks. Airlines have traditionally focused on a cost-driven approach when optimizing their network structures, often sidelining environmental impacts. This approach, primarily motivated by market demand and operating costs, seeks to develop a network that minimizes expenses while maximizing profitability. However, with increasing awareness of the aviation industry’s significant contribution to climate change, it has become essential to consider whether airlines can achieve both cost-efficiency and environmental sustainability through strategic changes to their network design. As the aviation industry evolves, so do the expectations of its customers. In this service-oriented business, delivering high-quality service is a core competitive advantage. This advantage is closely linked to how well airlines listen to and act on customer feedback, particularly by integrating this to early stages of their operational decisions. Understanding passenger needs and expectations is crucial, as decisions made without considering these factors can reduce customer satisfaction. For instance, optimizing a network solely based on cost and climate impact might lead to less convenient flight schedules or routes, which could alienate passengers who prioritize certain departure times or direct routes.
Given these complexities, it is clear that airline network design must go beyond just cost and environ- mental considerations. Integrating the passenger perspective is crucial for developing a network that meets both operational goals and customer satisfaction. This leads to the need for a more holistic modeling approach that combines three key objectives: minimizing costs, reducing environmental impact, and accommodating passenger preferences. The proposed modeling approach in this research aims to address this challenge by developing an optimization model that integrates the economic goal of cost minimization with the environmental objective of reducing CO₂ emissions and the service-oriented goal of satisfying passenger preferences, particularly in terms of departure times. To support the development of this comprehensive optimization model, a thorough literature review was conducted to identify the specific criteria necessary for integrating environmental considerations and passenger preferences into airline network design. This review focused on understanding the environmental impacts of aviation, particularly CO₂ emissions, and the various strategies airlines can adopt to mitigate these effects. Additionally, it explored the importance of passenger preferences, with a particular emphasis on departure times, as a critical factor that can influence network design. Following the literature review, existing hub location models were analyzed to assess their applicability in this context. While traditional models are effective for optimizing cost-driven networks, they often fall short in addressing the additional layers of complexity introduced by environmental and passenger considerations. The research identified specific needs arising from these considerations, such as the need to account for CO₂ emissions linked to flight frequency and the importance of aligning flight schedules with passenger preferences. To address these gaps, the models were adapted and extended, incorporating these new objectives to create a more holistic approach to airline network design. The developed model was then tested using the well-known CAB dataset, a benchmark in hub location research. The testing phase included both single-objective optimization, where each of the three objectives (cost, environment, and passenger preferences) was optimized individually, and multi-objective optimization, where the model sought to balance all three objectives simultaneously. This approach allowed for a comprehensive evaluation of the model’s effectiveness in producing network designs that meet the diverse needs of airlines in today’s complex operational environment. This research provides a significant contribution to the field of airline network design by introducing an optimization model that harmonizes the often conflicting goals of economic efficiency, environmental sustainability, and passenger satisfaction. Through an integrative approach that accounts for the complexities of modern airline operations, this study advances the understanding of how these diverse objectives can be balanced within a single framework. The proposed model, tested using an established dataset, demonstrates its potential to influence both academic discourse and practical applications by offering a more nuanced understanding of the trade-offs inherent in network design. Moreover, this work highlights the broader social and scientific relevance of incorporating environmental and passenger-centric considerations into strategic planning, urging a shift towards more sustainable and responsive practices in the aviation industry.

As the Netherlands aims to phase out gas by 2050, alternative heating solutions are urgently needed, especially in densely populated areas like Amsterdam's city center. Solutions like hydrogen and green gas are infeasible due to limited availability.
A potential solution is a fifth-generation district heating and cooling (5GDHC) network, an energy system that provides both heating and cooling to buildings using external energy. This is possible due to a low temperature heat carrier in combination with bidirectional operation. Heating and cooling can be exchanged between buildings within the network, reducing the need for external energy. A 5GDHC network for a large city requires clustering of the buildings to create smaller networks, which can later be connected. Clustering allows for a bottom-up approach, reducing both the investment risks and the overload risk. A benefit of 5GDHC is the possibility to use aquifer thermal energy storage (ATES), where thermal energy is stored underground. However, research on 5GDHC, particularly on large-scale implementation, is scarce. Dividing buildings into clusters is crucial for large-scale implementation, since it reduces the investment and overload risk, but the best methods are unknown.
Additionally, the possibility of implementing ATES is not considered in research during the clustering process. This research investigates a method to cluster buildings and integrate ATES systems within these clusters. The goal is to create compact clusters to minimize the use of space in the already crowded subsurface. The clusters must also have a high demand fulfillment, meaning the heating and cooling demands must be fulfilled as much as possible within the cluster, either through energy
exchange between buildings or through the use of storage. A higher demand fulfillment reduces the need for external energy. The developed method clusters buildings based on their geographical location using both k-means and equal size k-means to ensure compactness.After this, ATES systems are implemented in the clusters, which can only be placed in available open space. The model results indicate that implementing ATES systems can increase the demand fulfillment from 1.4% to 58.6% for the entire center of Amsterdam, if buildings are insulated to a level fit for low temperature heating. The results show that very compact areas struggle more to meet heating demand. k-means outperforms equal size k-means in both compactness and demand fulfillment, making it the recommended method. The findings suggest that a 5GDHC network with ATES implementation has significant potential, with the most potential for clusters in the east and northwest. The identified method is robust, adaptable to various geographical regions, and particularly effective in densely populated urban areas with limited space and high demand. In conclusion, this research contributes to the fields of aquifer thermal energy storage and fifth generation district heating and cooling. It not only fills critical gaps in existing literature but also provides a practical methodology for optimizing urban energy systems. This approach holds promise for supporting initiatives like the ‘High-hanging fruit’ project by the AMS Institute, assisting sustainable urban heating solutions in Amsterdam and beyond.

The industrial sector is one of the most energy consuming and CO2-emitting end-use sectors. To reach climate goals, decarbonization of these industrial sectors is imminent, however, not so apparent. Especially the hard-to-abate industry sectors, such as the chemical, mineral processing, iron, and steel sectors, are difficult to decarbonize as they require either high temperature heat or use fossil fuels as feedstock.

Hydrogen has the potential to reduce carbon emissions in industries such as chemicals, glass, iron and steel, as well as to serve as a cleaner heat source. To reach net-zero emissions by 2050, sectors that currently use fossil fuels for high-temperature processes and as feedstock will likely need to shift towards blue or green hydrogen. Currently, some industrial hydrogen use relies on gray hydrogen, produced from fossil fuels and contributing to emissions. In contrast, blue hydrogen captures and stores CO2 produced from fossil sources, while green hydrogen is entirely emissions-free, generated from renewable energy. In other words, some processes need to change from gray to green/blue but most of them need to change from other fossil fuel based processes to hydrogen processes.

Hydrogen offers a way to cut carbon emissions in industries like chemicals, glass, iron, and steel, and can also act as a cleaner heat source. Achieving net-zero emissions by 2050 will likely require sectors that currently depend on fossil fuels for high-temperature applications and feedstocks to adopt blue or green hydrogen instead. Today, certain industrial applications still use gray hydrogen, derived from fossil fuels and contributing to carbon emissions. However, blue hydrogen captures and stores the CO2 generated, while green hydrogen is emissions-free, produced using renewable energy. In essence, some processes will need to transition from gray to blue or green hydrogen, while many others will shift from fossil-fuel-based processes to hydrogen-based alternatives.

However, the timing and extent of the hydrogen transition are uncertain as they are heavily influenced by external factors such as hydrogen prices, available subsidies, and alternative decarbonization options. Additionally, industrial plant owners may be reluctant to disclose decarbonization plans due to competitive pressures, adding another layer of demand and participant uncertainty that complicates infrastructure planning.

This thesis addresses the planning of hydrogen infrastructure within an industrial port cluster (IPC). IPCs are defined by their proximity to water and concentration of industrial activities related to a specific sector. In order to effectively address spatial constraints, this thesis will plan the hydrogen networks along the current road network within IPCs. Current infrastructure planning methods have a time horizon of ten years. However, as the expectation is that the hydrogen demand will increase towards 2055, a time horizon of ten years can increase the total costs of the network when the network is implemented over time between 2025-2055. This introduces the following research question:

“How can a cost-efficient, robust pipeline network for an industrial port cluster be developed over time under uncertainty?”

To answer this question, the robust backtracking planning method (RBPM) is developed. This method aims to minimize costs over the 2025-2055 time frame while facilitating the hydrogen to the demanding plants. Because the demand for hydrogen is likely to grow over time, this method finds a robust network that is able to facilitate the demand in many possible future demand scenarios of 2055.

The robust network is then implemented incrementally for 2035, 2045, and 2055 using a backtracking approach. In this context, backtracking means that when an industrial plant transitions to hydrogen in one stage, pipelines are installed with the robust networks’ capacity, rather than just the minimum required to meet that plant’s immediate needs. This extra capacity ensures that if other plants transition in later years, the existing network can accommodate the increased demand without needing costly pipeline extensions. By preemptively building capacity, this approach reduces future installation costs and enhances the network’s ability to adapt to evolving demand patterns.

The RBPM is tested on simulations of multiple simplified IPCs. By testing different IPC simulations, it is studied how the difference in industrial plants determines the development of the network. The RBPM is compared to the results of a traditional planning approach which only plans the networks with a time horizon of ten years.

The results show that the RBPM incurs lower costs over 30 years, but it requires a higher investment in 2035 due to the greater capacity installed at that time. This thesis finds that the total potential hydrogen demand and the physical size of an IPC significantly affect the performance of the RBPM compared to the traditional planning approach. Additionally, the projected installation and operational costs over time also impact the RBPM’s performance relative to the traditional planning approach.

For IPCs with comparatively low hydrogen demand — typically clusters with fewer iron and steel facilities, chemical plants, or refineries — the RBPM emerges as the most economical approach. This method requires only slightly higher investment by 2035 but ultimately generates substantial savings by 2055. By installing sufficient pipeline capacity upfront, the RBPM avoids the need for additional pipelines every ten years, leading to long-term cost efficiency through 2055.

For IPCs with high hydrogen demand—typically found in iron and steel plants, basic chemical plants, or refineries—the initial installation costs and ongoing operational expenses of RBPM make it less advantageous. While RBPM may offer slightly better economic profitability over a 30-year period, the substantial investment required in 2035 compared to traditional planning makes implementation challenging due to budget constraints. In these high-demand clusters, the decision between RBPM and the traditional approach for developing a hydrogen pipeline network depends on the cluster’s budget, anticipated future installation costs, and projected operational expenses over time.

Opportunities for further research include the application of the RBPM to a real case study to validate the result, increasing the amount of possible future scenarios by incorporating uncertainty in installation and operating costs and increasing the demand and participant uncertainty range. Lastly, another research direction to explore is the generation of different robust network methods and their performance.

Freight transport is a crucial component of our society and economy. However, achieving sustainability in this sector is complex, due to a lack of incentives among stakeholders. The heavy-duty road transport sector is struggling to meet European emission standards. To address this issue, a research study examined the impact of combining Battery Electric Trucks (BETs), Hydrogen trucks, and Bio-Liquefied Natural Gas (LNG) trucks on a network level. The study used freight simulation and refuelling location optimization through K-means to determine the cost and emissions of the system under different conditions. The results showed that Bio-LNG trucks and BETs are viable fuel options for sustainable freight transport, with Bio-LNG being immediately usable and BETs showing promise for future technological development. The study further revealed that BETs are a cost-effective and low-emission choice for short- and long-haul freight trips, particularly when using Mega Watt chargers and larger battery capacities. The research contributes to understanding the complexities of transitioning to greener freight transport and provides insights into policy implications. Future research could explore the broader social and environmental impacts of these alternative fuel options.

The pivotal sector of transportation has shown signs of a surge in demand. The European Union projects a 42% increase in passenger transport from 2019 until 2050. Policymakers and stakeholders must collaborate to address the increasing transportation demand while considering environmental, societal, and economic benefits Despite efforts to mitigate emissions, the transportation sector has not achieved substantial reductions. The emergence of Hyperloop technology presents a disruptive solution that could address this transportation challenge in Europe. It has been pursued as a viable alternative to air travel, rail, and traditional forms of transportation due to its affordability, sustainability, and rapid speeds of up to 1200 km/h. Even though Hyperloop is a promising alternative in the transportation sector, the technology is still largely in development. There are multi-dimensional considerations in understanding whether the Hyperloop will become a mainstream transport option for passengers and whether the conflicting objectives will result in an efficient Hyperloop network. A knowledge gap was identified with a lack of studies to explore the relationship between the network design objectives and the network design itself.
In order to identify the impacts of the Hyperloop network design in the global transportation sector, a literature review was conducted on the transformative potential of the Hyperloop. Key strengths were identified as a reduction in travel times and low operational emissions. On the other hand, the high capital resources required and the uncertainty around the safety of technology were the main points of criticism. In order to analyze the potential demand for Hyperloop and model the modal shift, a Multi- Nominal Logit was employed where a utility function was formulated for the total benefit passengers receive upon completing a trip. The key attributes for the utility function were selected as travel time, travel costs, number of transfers, and safety perception, in alignment with previous studies on the subjects. A utility-based probabilistic mode choice was determined for the available demand. A multi-objective optimization problem was formulated for the facility-location network design of Hyperloop.
The decision variables of the model were formulated as the decision to open a Hyperloop hub at a location and the decision to build infrastructure between the selected Hyperloop hubs. The model output is an alternate network optimized for four different objective functions. These objectives are determined to be (1) Utility Maximization, (2) Probability of Purchase Maximization, (3) Emission Minimization, and (4) Revenue Maximization as these factors were determined to be key performance indicators in a prospective Hyperloop network. The model aims to provide the decision-makers with an overview of the trade-offs involved with varying objective criteria considered in the network generation.
A case study was created to test the model within Europe. The main aim of the case study is to assess the economic and environmental impacts of the Hyperloop system and provide recommendations to policymakers regarding the conception of the Hyperloop network within the European Union. The case study employs the NUTS classification and excludes European countries where the demand data is incomplete and focuses on countries within the TEN-T network. Furthermore, three categories of experimental scenarios were set up to assess the sensitivity of the model to parameter values. The categories are (1) pricing strategy scenarios, (2) safety perception scenarios and (3) policy intervention scenarios. The findings reveal significant disparities in network characteristics based on different objective criteria. The Utility Maximization objective focuses on maximizing trip utility, leading to a network design with direct links between hubs, resulting in compact networks and lower infrastructure costs. However, Spain and Italy have lower priority in this design. On the other hand, the other three objectives (probability of purchase maximization, emission minimization, and revenue maximization) yield networks with a minimum-spanning tree pattern. These networks outperform the utility maximization network in terms of attracting passengers, reducing emissions, and economic performance. To maximize societal benefits, it is recommended to prioritize the remaining three objectives. The study finds that Hyperloop becomes more competitive for longer-distance trips. Experimentation with ticket prices, safety perception, and policy interventions demonstrates their influence on modal share, revenue stability, and carbon emissions. Higher ticket prices discourage Hyperloop usage, safety perception plays a crucial role, and policies discouraging short-haul flights result in higher Hyperloop modal share and lower emissions. These findings highlight the importance of considering ticket prices, safety perception, and strategic policies to promote sustainable transportation and reduce carbon emissions through a modal shift to Hyperloop.
Future research opportunities include expanding the utility function to incorporate additional attributes affecting mode choices, exploring modal shifts from other modes to Hyperloop, relaxing assumptions about geographical obstacles and hub locations, integrating strategic and tactical planning, and validating the model with a broader range of origin-destination pairs. Computational performance can be enhanced using meta-heuristics to compare different heuristics for network outputs and efficiency.

Society is facing a huge challenge in switching the energy sectors dependence on fossil fuels into an energy sector using mostly renewable energy sources. The switch towards using more sustainable energy sources is known as the energy transition. The goal of the energy transition is to lower the greenhouse gas (GHG) emissions emitted by the energy sector. Lowering the GHG emissions helps society limit the global warming caused by GHG [3]. 17.5 % of the global energy usage comes from the energy use in buildings [50]. It is thus very important that the energy use in buildings transitions towards using more sustainable energy sources. One of the renewable energy sources that is ought promising in the energy transition for energy use in buildings is geothermal energy [3]. Geothermal energy is energy that is captured in reservoirs of hot water in the earth’s crust. The hot water captured in the hot water pockets is pumped to the surface, to use it in spatial heating. The return pipe returns the cooled water to the geothermal well, where it can heat up again over a certain period of time [63] [23].

In some cases, geothermal energy is applied using a district heating network. A district heating network is an example of a system that provides heating and/or cooling capacities to a group of buildings [65]. A district heating network is a network of pipelines that transport the hot water from the geothermal well to the buildings in the district. A geothermal well in combination with a district heating network is developed in Delft [27]. The district heating network will deliver energy to the TU Delft campus, two neighborhoods in Delft and industry at the Schieweg in Delft [28].

Besides the district heating network in Delft, it is expected that district heating networks will be applied more often to accelerate the energy transition. Yun-Chao and Chen (2012) concluded that most optimization techniques optimize the whole system with its components. Less optimization techniques are applied to the sole components. Besides the fact that most optimization methods optimize the system as a whole, most optimization objectives only include optimizing the cost of the system. Also, effective optimization techniques are required as optimizing large graphs may be computationally time consuming [36]. In literature there are also clear signals that state that the trade-off between thermal comfort, and efficiency with respect to cost has to be tackled [53]. In this research, optimizing district heating networks for cost is compared to optimizing district heating to maximize thermal comfort or efficiency.

In this research two models are developed: a model that calculates the cost of the district heating network, and a model that calculates the thermal losses of the district heating network. Both models are applied to a district heating networks that is developed in a street network. Furthermore, multiple heuristics are applied to come up with better district heating networks. The optimization technique is tested on 100 small, randomly generated district heating networks. After that, the district heating network in Delft is optimized. The differences in cost, efficiency, etc. will be evaluated. Besides, the performances of the district heating networks are evaluated by introducing energy deficits under different conditions.

Optimizing the district heating networks for cost led to a very consistent result: When compared to their individual starting point, the district heating networks became cheaper and more efficient. A moderate-strong correlation is found between the the increase in efficiency and the decrease in cost while optimizing the district heating networks. In contrast to that, the networks that maximize efficiency are much more expensive than their cost optimized alternative, while the increase in efficiency is in most cases moderate. However, there are rare cases where the efficiency is increased much at a moderate increase in cost. This phenomenon is also found in Delft. Given the result that the efficient district heating network also performed much better than the cheapest alternative during energy deficits, in this research it is shown that choosing an objective function has a very large impact on the characteristics of the network. Therefore it is shown that for future district heating network optimization, it is important to trade off cost against efficiency.

The Dutch government has set a goal to phase out the use of natural gas by 2050. As a result, municipalities are focusing on implementing alternative heating strategies in certain neighbourhoods. However, districts with older building stocks are often neglected, raising concerns that city centers with older, poorly insulated buildings and high heat demands may be left until later stages of the transition, despite the need for a clear heating strategy in these areas. Additionally, urban areas face the challenge of the urban heat island effect, which leads to trapped heat and elevated summer temperatures. This effect, coupled with the consequences of climate change, will increase the demand for cooling.
To initiate a new heat transition, it is crucial to adopt new sustainable and locally generated heat systems. A potential solution is the implementation of a 5th District Generation Heating and Cooling (5GDHC) network, which utilizes low-temperature heat and cold, potentially sourced from waste heat. However, the design and implementation of a 5GDHC network presents numerous challenges, including the absence of comprehensive guidelines and limited knowledge regarding the deployment of these systems on a larger scale or in an area with an older building stock. This research focuses on developing a tool that can identify potential clusters for a 5GDHC system in densely populated urban areas.
To achieve the research objective, a methodology is developed to identify clusters for a 5GDHC network. Clusters are essential for the implementation of a 5GDHC system in a larger area, as they mitigate investment risks and facilitate a clearer implementation process. The methodology in this study integrates the Single Linkage clustering algorithm and Geometric Graph Theory, which are extended into a model. This model generates clusters based on building locations and energy profiles, and assesses their performance using metrics such as the aggregated hourly lack of supply throughout the year and the total length of the pipe network.
A case study of the inner city of Amsterdam, part of the 'high hanging fruit' project by the AMS Institute, is utilized to test the model. The model requires data on potential waste heat and retrofitted buildings as input. The developed model effectively identifies clusters within a large urban area based on building locations and energy profiles. Trade-offs between pipe network length and energy efficiency must be considered when evaluating the model's results. It is highly recommended to adopt a bottom-up approach and establish 5GDHC clusters incrementally within the city. The hourly disbalances, calculated by the model, can identify potential clusters ready for connection. Moreover, the performance metrics derived from the model can serve as valuable decision-making guides during the design phase of 5GDHC networks. To enhance the decision-making process further, it is crucial to integrate the model's information with urban planning considerations and engage relevant stakeholders. By combining these factors, a comprehensive and well-informed decision-making process can be facilitated, leading to more effective and efficient 5GDHC network designs and implementations.
The full model created within this research can be retrieved from: \url{https://github.com/svanburk/clustering5GDHC.git}

Freshwater is becoming an increasingly scarce resource globally. The chemical industry is the second largest freshwater consuming industry and the largest wastewater producer. To reduce the freshwater consumption sustainable measures are introduced within the industrial sector, of which Industrial Symbiosis (IS), has gained prominence. Specifically for water, this involves one company utilizing the residual wastewater of another company based on the differences in water quality requirements However, the chemical industry lags behind in embracing IS practices. Two important reasons cause this trend. Firstly, it is technically challenging to recycle water because of all the different water qualities used and produced in chemical parks. And secondly, the industrial symbiosis initiatives have to start with the stakeholders involved in the chemical park themselves in bottom-up regulated countries, while currently, many stakeholders decide not to join an IS water network. This research focuses on the feasibility of implementing IS water networks within chemical parks, considering stakeholder behaviour (wanting to join or not join an IS water network). A model is developed to measure the optimal performance of an IS water network, encompassing technical optimization and stakeholder decisions. The study reveals that the potential for industrial symbiosis in chemical parks relies heavily on existing treatment facilities and stakeholder participation. The type of treatment facility structure, with in-house or separate treatment facilities, impacts costs and water recycling capabilities, with chemical parks with separate treatment facilities exhibiting higher costs and higher recycling potential. Key decision criteria for stakeholders include revenue growth, compliance to regulations, trust among stakeholders, and awareness of water recycling possibilities. Recommendations to enhance industrial symbiosis of water include technical adjustments and construction efforts to the IS water network within the chemical park, and policies addressing stakeholder concerns. The contributions of this research lie in integrating technical and social aspects in the design of optimal IS water networks, providing insights into decision criteria that facilitate or hinder industrial symbiosis in specific chemical parks and how to address them to recycle most water as possible with least amount of costs.

The Dutch government has set the ambitious target to be CO2 neutral by 2050. In order to achieve this goal, large volumes of hydrogen are necessary. And if large volumes of hydrogen are necessary, hydrogen transportation infrastructure is necessary. Based on the expected volumes of hydrogen supply and demand and the distance over which hydrogen needs to be transported, hydrogen pipelines are the most cost-efficient option for the transportation of hydrogen. However, investing in pipelines carries risk the risk of not being fully used, which can lead to high costs for the users. This risk is especially high regarding hydrogen, as there still are many uncertainties surrounding the future supply and demand of hydrogen.

In this research, a model is constructed to determine a cost-efficient realisation of hydrogen pipeline infrastructure in the Netherlands for the years 2030 and 2050. In addition, policy recommendations are made for the Dutch governments on the realisation and regulation of hydrogen pipeline infrastructure.

The decarbonization of economies around the world is crucial for reducing the impact of human-induced climate change. Many proposed means to achieve this decarbonization like the electrification of various sectors or the introduction of ‘new’ forms of energy such as hydrogen and carbon capture and storage require existing energy infrastructures to be expended or entirely new energy infrastructures to be created. Since energy networks are capital intensive, minimizing their construction costs is essential for their realization. Previous works studying the cost minimization of energy networks often neglect that their construction costs can be influenced by geographical areas such as mountain ranges, existing infrastructures or zoning rules. Using methods borrowed from graph theory and geometrical computing, a method has been developed that is able to find costoptimal energy infrastructures in a spatial context of geographic regions with different costs for constructing power cables or pipelines through them. The method models these geographical areas as triangles on which borders a user-defined number of points are uniformly placed representing potential entry and exit points for pipelines or power cables. Experimenting with 144 randomly generated routing problems has shown that, on average, increasing the number of these placed border points results in a reduction of the total network costs. Although, this effect flattens when more than 7 of these points are placed. The method is applied to two offshore electricity networks in the Dutch North sea. These cases demonstrate the method’s strength in identifying trade-offs between energy networks’ investment costs and their (negative) influence on their spatial surroundings. Additionally, the method’s outcomes are well-suited to be used as a springboard for dialogue in energy infrastructure decision-making processes. Further research could focus on the extension of the model, by including existing connections, or the improvement of the model, by implementing a different method for modelling geographical areas that has a higher accuracy and is computationally faster.

To provoke the needed energy transition, European Union (EU) countries initiated significant offshore wind energy investments in the North Sea. However, there also exist adverse aspects to the use of higher renewable electricity levels:

• renewable energy sources, such as offshore wind energy, may threaten the security of our energy system, since they are characterised by a high variability, and limited predictability and controllability;

• the effectiveness with respect to decreasing greenhouse gas emissions is limited, because electrification can be not applied within all sectors, such as heavy industry and heavy duty transport;

• a large increase in offshore wind capacity requires moving further off shore. This results in relatively high costs because of the energy losses within the (longer) electricity cables.


One way of coping with these challenges, is by producing hydrogen offshore, by means of wind energy, and transporting it to shore by for example repurposing the existing offshore natural gas infrastructure. Such an offshore hydrogen system located in the North Sea might sound favorable; however, the feasibility of such a system on this scale is yet to be determined.

In this thesis the possibility is investigated to design a future proof offshore hydrogen system. Such a system would consist of (current as well as new) wind farms, electrolysers to produce hydrogen by using (a part of) the electricity generated by the wind farms, and an infrastructure to bring the hydrogen to shore. Given the EU investment plans in offshore wind energy, a phasing period is used from 2030, 2040, to 2050. This research is done by:

• deriving multiple hydrogen system designs by for example optimising the transmission infrastructure;

• analysing the supply potential of these system designs.


The results show that a cost-competitive hydrogen system in the North Sea can be realised. The proposed system design has a Levelised Cost Of Hydrogen (LCOH) of 2,08 EUR/kg and a positive Net Present Value (NPV) for the most relevant pricing scenarios. This LCOH is relatively low compared to other researches, which are mostly between 2 and 3,5 EUR/kg.

An interesting result concerns including refurbished pipelines of the existing offshore gas infrastructure. When using only new pipelines, the transmission infrastructure costs increase with 36%. Furthermore, the results show that it is more cost-efficient to downscale electrolyser capacities than to use the peak of the available electricity to determine the capacity of the electrolysers. Additionally, the productivity of the wind farms can increase up to even 220% by using the different electricity surplus for hydrogen production.

Based on this research, recommendations can be given:

• National governments should formulate policy on whether or when gas extraction in the North Sea should stop. Thereupon, the (energy) transmission system operators should scope their plans towards transporting offshore hydrogen to onshore, as well as start planning the onshore hydrogen backbone.

• The EU should decide whether to build one interconnected system in the North Sea, or multiple isolated (per country) hydrogen systems. Based on this decision, it is important to start shaping rules and standards for hydrogen trade, as well as determining regulatory regimes to support offshore hydrogen production.

• Further research should be done on the electrolyser costs and efficiencies, as well as the different types of electrolyser locations; on the possibilities of hydrogen storage; and, to include (regional) hydrogen demand values.

This study researched the optimal network of hydrogen refueling stations. The network is designed on a European scale and with a focus on heavy duty trucks. The design is created by means of a two-step optimization. The first step clusters truck representing datapoints into a predetermined number of stations by means of k-means clustering. The second step optimally connects these to a source of supply, which is assumed to be a European hydrogen backbone. Cost values of supply and demand are finally optimized by balancing a cost function.

Research was performed by order of Shell.

Extensive electrification of the inland shipping sector is necessary to achieve the EU goals to be climate neutral and increase inland shipping by 50\% by 2050. This requires a thoughtful and large-scale roll-out of new charging stations layouts, for ships with relatively high and largely varying energy demands. Current approaches for optimal charging station placement, mostly neglect temporal demand fluctuations and cannot cope with varying charging demands. Therefore, we aimed to develop a method that combined a capacitated flow-capturing approach and an agent-based simulation. Moreover, the resulting method was applied to the Dutch inland waterway freight transport sector in a case study. Results indicated that a large-scale transition to battery-electric propulsion is technically possible, but is likely economically unfeasible. The case study can be used to support decision-making towards renewable shipping. In addition, the newly designed may also be used to site energy hubs. Forthcoming, methods to come to efficient charging station layouts will be needed to stimulate the uptake of electrified transportation and avoid lock-ins to inefficient investments.

Industrial clusters are facing major challenges in transitioning away from their carbon-intensive activities in order to accelerate the energy transition away from fossil fuels. Therefore, the industrial clusters; the Port of Rotterdam, Chemelot, North-RhineWestphalia (NRW) and Transition System Operator (TSO) GasUnie are planning to develop a pipeline connection for the transportation of renewable fuels between one another (Port of Rotterdam, 2020b). However, this pipeline connection operates in a very uncertain environment with regard to the technology required to decrease greenhouse gas emissions and the future policies that impact on the investment decisions by the industries that will make use of this connection. Therefore, there is much uncertainty about the requirements of the pipeline, making the way in which this new pipeline connection is to be constructed a complex question. Designing a flexible pipeline with capabilities to pro-actively deal with uncertainty could reduce the financial risk and support investment in the pipeline connection. Even so, there is a lack of knowledge about the specific costs and benefits of flexible pipelines and the question arises of how efficiently these flexible pipelines will perform. Therefore, this research is aimed to analyse the performance of various flexible pipeline designs for the connection of Rotterdam, Chemelot and North-Rhine Westphalia and answers the research question:” How do flexible pipeline designs perform for the transportation of sustainable fuels between the Port of Rotterdam, Chemelot and North-Rhine Westphalia from 2025 until 2050?”.

The Indonesian government has set a target to increase new and renewable energy sources in the energy mix by 23% in 2030 and 31% in 2050, which are to be met by investing in hydropower and geothermal energy capacity mainly. However, Indonesia has abundant other renewable energy potentials, which are largely untapped. The Java-Madura-Bali (Jamali) system is Indonesia's largest electricity system. Integration of variable renewable energy resources requires flexibility options such as grid expansion and short- and long-term electricity storage. This research aims to fill the knowledge gap in the literature on the effect that different carbon emission reduction limits have on the Jamali power system design in 2050. This was done by studying the potential of various promising renewable energy technologies (solar photovoltaics, on- and offshore wind, Ocean Thermal Energy Conversion (OTEC), geothermal and hydropower) in combination with short- and long-term storage (lithium-ion batteries and hydrogen storage) and grid expansions to mitigate renewable variability. For this purpose a techno-economic model was developed that optimizes operation and capacities of generation, storage and network simultaneously. The model simulates the system dynamics in the Jamali system in 2050 and was implemented in Python for Power System Analysis (PyPSA). It was found that there is an exponential relationship between system costs and carbon emission reductions in the Jamali power system. Additionally, only moderate system cost increases were found up to 80% emission reductions compared to the reference scenario with no emission mitigation efforts. At higher carbon reduction scenarios the solar capacities reach their maximum installable capacities. As a result, system cost increase exponentially due to the need for OTEC and offshore wind capacities. The high costs increases by offshore wind can be explained by the uniform costs and coarse resolution of the model. In low carbon scenarios high battery capacities were found and network expansion is limited. It is concluded that the Jamali network cannot smooth the variability of wind throughout the power system, therefore, without storage capacities the system cost almost double. On the other hand, the network remains important to transport electricity from rich renewable regions to large demand centers. Based on the results three recommendations were proposed related to the reconsideration of the energy targets for 2050, strategy to achieve the targets and present policies.

With the penetration of distributed energy resources, peer-to-peer (P2P) energy trading is becoming a promising way to harmonize the decarbonization and decentralization transformations in the energy sector. P2P markets give peers the autonomy to make individual decisions and cooperative behaviors between the peers may emerge. However, existing studies on cooperative behaviors in P2P markets focus mostly on the electricity sector, P2P multi-energy markets are rarely studied. In fact, other energy carriers not only constitute a large part of the total energy demand, but their coupling can potentially benefit the system as well as the end-users. In this paper, we propose a P2P multi-energy market mechanism that allows peers to trade both electricity and heat, where the peers can join two predefined trading coalitions. The proposed system model thus explores the integrated effects of the multi-energy coupling and the cooperative behaviors in the P2P market. In order to maximize the net benefits of the peers, price conditions are derived, based on which the peers will join either of the coalitions and determine their trading volumes. Then, the electricity and the heat markets are cleared separately by a market operator. Lastly, the market mechanism is illustrated by a case study on a neighborhood in the Netherlands using realistic data.

As global climate change concerns intensify, the European Union aims towards the decarbonization of its Member States. Over the last decade, hydrogen has received increasing attention as a vital element to achieve this decarbonization. However, the diffusion of hydrogen in the European Union is critically dependent on the emergence of a pipeline transmission infrastructure. This research aims towards the generation of a robust European hydrogen infrastructure between 2030 and 2050. To do so, a novel methodology combining Exploratory Modelling and Analysis (EMA) and graph theory is proposed. The essence of this methodology is to generate a network that performs well over a wide range of plausible futures. Results of this research highlight that a sizeable European hydrogen network may emerge towards 2050 with Italy and Germany at its core. In 2030, multiple relatively small networks may arise around Italy, Germany and the Benelux. These components can be expected to grow in size by 2040 but nevertheless stay disconnected. As of 2050, a cohesive European network spanning from the Balkan in the East to France in the West may emerge, connecting the previously disconnected networks.

This research evaluates and optimises the competitiveness of a combined wind, solar, and electrolyser plant in the Dutch electricity and hydrogen market. For the electricity market, this study used the EPEX spot market, where most electricity is traded. The hydrogen market is far less transparent than the electricity market; hence, a market had to be simulated for this study. It was assumed that the price of hydrogen is set by the production cost of hydrogen through steam methane reforming, because this is by far the largest producer of hydrogen in the current market. For both markets, it was assumed that an unlimited amount of electricity and/or hydrogen could be sold at the market price at any time. The competitiveness of the hybrid plant was determined by the Net present value of the hybrid plant. This NPV was calculated for nine research scenarios placed on two axes, a climate action axis, and a time axis. The climate axis represents the amount of climate action taken by governments and industries. The time axis represents the commercial operating date, varying from 2021 until 2030; this will illustrate how the plant competitiveness evolves.