Segregation, or de-mixing, is a significant challenge in the handling and processing of granular materials. It can lead to several problems, including inconsistent product quality, poor flowability, and process inefficiencies. In blast furnace steelmaking, segregation of the ferrous mixture (consisting of pellets, sinter, lump ore, and nut coke) causes uneven distribution of mixture components at the furnace throat. This reduces permeability and disrupts the gasburden interaction, which is critical for stable and efficient operation. Therefore, understanding segregation is essential. The Discrete Element Method (DEM) offers a powerful way to investigate segregation, particularly in the blast furnace, where direct measurements and experiments are difficult due to the harsh operating environment.

This thesis developed a systematic, accurate, and computationally efficient approach based on DEM to model the flow behaviour of multi-component mixtures, and to apply the developed model to the blast furnace charging system, from the weighing bunkers to the top hopper.

Autonomous surface vessels are increasingly expected to operate cooperatively in future waterborne transport systems, where multiple vessels can sail in coordinated formations to improve safety, efficiency, and operational capability. However, when vessels operate in close proximity, ship-to-ship hydrodynamic interactions become non-negligible. These interactions influence maneuvering dynamics, tracking performance, formation stability, and propulsion energy demand. Conventional formation control methods often treat such interactions as external disturbances or ignore them entirely, which limits their applicability to energy-efficient and interaction-sensitive multi-vessel operations. This thesis addresses this gap by developing hydrodynamics-aware model predictive formation control methods for energy-efficient multi-vessel systems.

The thesis first reviews cooperative formation control strategies, communication structures, hydrodynamic interaction mechanisms, and formation-resistance characteristics for autonomous surface vessels. Based on this synthesis, a conceptual framework is proposed in which ship-to-ship interactions are not only treated as disturbances to be compensated, but also as predictable physical couplings that can be exploited for energy-aware formation design. An interaction-aware model predictive control framework is then developed for multi-vessel formation tracking. A three-degree-of-freedom vessel model is combined with a data-informed ship-to-ship interaction model, allowing surge, sway, and yaw interaction effects to be incorporated into the prediction and control process. Simulation studies demonstrate that explicitly considering interaction forces improves tracking robustness and provides a more realistic basis for formation control in close-spacing regimes.

Building on this interaction-aware control foundation, the thesis further investigates hydrodynamics-aware formation optimization for reducing fleet-level energy consumption. A hierarchical control architecture is designed, where an upper-level decision layer optimizes formation configuration and reference speed based on interaction-aware energy indicators, while a lower-level model predictive controller tracks the resulting references under vessel dynamics and actuator constraints. Different formation layouts, including tandem, triangular, echelon, and adaptive configurations, are examined to reveal the trade-offs between energy saving, formation tracking accuracy, and stability. The results show that energy-efficient formations are strongly speed- and geometry-dependent, and that favorable hydrodynamic interaction regions can be used to reduce resistance and propulsion demand.

Finally, the thesis extends the framework to route-following operations under environmental disturbances. A leader–follower MPC structure with disturbance estimation is proposed to improve post-turn spacing recovery and maintain interaction-favorable geometries under wind, current, and sea-state-related energy effects. Compared with centralized MPC, the leader–follower formulation keeps the fleet more persistently in energy-saving regimes. In the studied route scenario, the LF-MPC architecture achieves a mission-average energy-consumption index of −4.17%, whereas the centralized MPC case results in a positive average index of +1.28%. These findings indicate that hydrodynamics-aware formation control can provide additional energy-saving potential beyond conventional single-vessel optimization, while preserving formation tracking performance and operational feasibility.

Overall, this thesis contributes a systematic framework for integrating ship-to-ship hydrodynamic interactions into model predictive formation control. It demonstrates that interaction-aware prediction, configuration optimization, and hierarchical control can jointly support energy-efficient, robust, and adaptable multi-vessel operations. The results provide a foundation for future research on scalable distributed control, propulsion-inclusive energy optimization, and real-world deployment of cooperative autonomous vessel formations.

Industry 4.0 technologies have the potential to transform logistics by enabling smarter, more autonomous warehouse systems. However, Shuttle-Based Storage and Retrieval Systems (SBSRS) still rely largely on centralized or hierarchical control architectures, limiting their adaptability, scalability and responsiveness. While existing research has focused on optimizing control policies within these conventional frameworks, the architectural layer itself remains underexplored. This study investigates the impact of control architectures on SBSRS performance by comparing a conventional hierarchical model with a hybrid control architecture that introduces local autonomy and inter-shuttle communication. A hybrid Discrete-Event and Agent-Based Simulation model was developed to evaluate both architectures, using a tier-captive SBSRS inspired by Vanderlande’s ADAPTO system. System Performance was assessed under varying system sizes and shuttle densities using throughput, normalized efficiency, scalability and robustness as key performance indicators. The results show that the hybrid architecture consistently outperforms the centralized approach in throughput, especially in large scale system, with up to 36\% throughput gain, up to 68\% efficiency gain (normalized throughput), improved scalability and reduced variability. These findings suggest that hybrid control offers a promising path for designing adaptive, Industry 4.0-ready SBSRS. The study contributes a conceptual design and simulation-based comparison to guide future control architecture development.

This research addresses the need for advanced perception systems in offshore crane operations, driven by the global shift towards sustainable energy and the increasing complexity of offshore wind farms. Offshore crane operations are essential for the installation and maintenance of these structures, and currently rely heavily on the subjective judgement of operators. This dependence contributes significantly to human error, with approximately 60-80% of accidents linked to poor situational awareness and misperception. Huisman Equipment identified the potential for enhancing safety and operational efficiency by employing advanced sensing technologies, such as 3D-Light Detection and Ranging (LiDAR) systems.

A comprehensive review of existing technologies and methodologies for load detection and tracking highlighted the advantages of LiDAR sensors over cameras, radar, and radio-based sensors. Particularly in offshore environments, where visibility is often compromised, LiDAR’s ability to produce high-resolution, three-dimensional point cloud data, coupled with the stability of a fixed-mounted setup, ensures reliable and consistent monitoring of loads during crane operations.

To achieve accurate load detection using a single LiDAR sensor, the study incorporated techniques such as Statistical Outlier Removal (SOR) and voxel grid downsampling for effective data preprocessing. RANSAC and HDBSCAN were employed for robust background removal and clustering, respectively. These methods were seamlessly integrated into the detection architecture, demonstrating reliable performance against key performance indicators (KPIs) and achieving high accuracy under varying conditions.

The research identified suitable motion models for reliably tracking the movement of detected loads, including a constant turn model for horizontal motion and a constant velocity model for vertical motion. These models were integrated with the Unscented Kalman Filter (UKF) for tracking and the Joint Probabilistic Data Association Filter (JPDAF) for data association. Experimental results confirmed the system’s ability to accurately track load positions, maintain precision, and distinguish loads from clutter in dynamic offshore scenarios.

An experimental setup was designed to replicate real-world conditions and collect data to develop and test the proposed LiDAR-based methods. The setup, simulating the operational environment of a vessel-mounted LiDAR system, provided diverse datasets essential for validating the detection and tracking methods. Testing confirmed that the setup effectively replicated offshore conditions, supporting the refinement and evaluation of the system. The developed methods were rigorously evaluated for their accuracy, reliability, and performance in simulated offshore crane operations. The detection system consistently achieved accuracy exceeding 70% in high-performance scenarios, with effective clustering indices surpassing minimum thresholds. Tracking results demonstrated reliable load identification and positional precision, despite minor systematic errors. The methods proved robust and reliable, addressing key challenges in offshore environments.

In conclusion, this study successfully developed and validated a fixed-mounted LiDAR system for load detection and tracking in offshore crane operations. The research provides a practical, technology driven solution to improve safety and efficiency, while future work should explore enhancing the system’s capabilities under adverse weather conditions and integrating additional sensor technologies to further advance situational awareness.

Our study focuses on employee assignment for component inspections at a majorDutch airline, where externally repaired components and consumables require inspection before release into stock. The problem features limited capacity, heterogeneous skills, component-workstation compatibility, and strict priority ordering. We formulate a binary integer program as a variant of Multiple Subset Sum Problem with Assignment Restrictions (MSSP-AR) extending the general formulation with with a strict priority constraint. Because strict priorities can block capacity, we introduce a phased procedure that iteratively re-optimizes after removing subsequent components in blocked categories, aligning with the logic of the IT system and improving utilization of employees. To keepmulti-run evaluation feasible under stochastic inspection times, we truncate the tail of priority level 3 components, which preserves optimal employee assignment solutions while reducing runtime substantially. Validation against operational data shows the average gap to the 90% target reduced from 65 percentage points to 15.3 percentage points under the model. On weekdays the effect is the highest as the average gap reduces from 57.9 percentage points to 2.4 percentage points. The approach delivers implementable employee assignment solutions under constraints applicable to the operation and provides a basis for future work on worker productivity heterogeneity, adaptive truncation, multi-day planning, tactical and strategic models, and broader applicability inMRO/logistics settings.

Steel production begins by charging a mixture of different iron-ore particle types into a blast furnace, a tall shaft reactor designed to extract iron through melting and chemical reduction. The main materials are pellets, fired near-spherical agglomerates of iron-ore concentrates, and sinter, fused agglomerates of fine ore and fluxes. The mixture is charged in layers, and these layers collectively form the packed bed in the furnace. During operation, hot reducing gas must flow through the void space of this packed bed to drive the reduction reactions and provide heat for melting, which makes bed permeability critical. Observing how these materials distribute during charging is difficult because of harsh operating conditions and scale, yet this distribution governs bed permeability and thereby the gas flow that affects efficiency, energy use, and emissions. As a result, it remains unclear whether the deposited mixture is uniform and, if not, how to improve it. Despite decades of industrial use, the blast furnace therefore remains, to some extent, a “black box”.

The Discrete Element Method (DEM) is well suited to this problem because it resolves the motion of individual particles and their interactions, which enables a detailed assessment of mixture distribution within and between layers. This thesis develops a calibrated DEM framework to predict mixed-layer formation and to assess its implications for blast furnace permeability. The focus is on component distribution (pellet versus sinter) and packing distribution as key descriptors of the burden structure in the charged layers. The model adopts the Hertz-Mindlin contact law with rolling model C for coarse, free-flowing materials and treats a 15-parameter interaction set for the pellet–sinter mixture.

A pre-calibration step evaluates three inter-component parameters: restitution, sliding friction, and rolling friction, using porosity as a proxy for permeability. Sensitivity analyses show that all three parameters strongly affect porosity and component distribution, and they are therefore retained in the full calibration. Robust calibration requires conditions representative of charging and multiple constraints. To that end, a high-velocity laboratory setup with a 4.7 m drop height enables systematic piling tests for pellets, sinter, and their 50/50 mixture. Five KPIs (hopper discharge time, heap mass, heap contour, heap center height, and heap porosity) are used in a stepwise calibration, first for the single components and then for the inter-component interactions. The calibrated model reproduces experiments across discharge heights with maximum deviations of 5.5% at the highest drop, which indicates reliable predictive performance over a range of velocities.

Industrial-scale charging is addressed via particle up-scaling. While a factor of 2 is insufficient for practical runtimes, factors up to 5 preserve layering, segregation, and porosity patterns when mass flow rate is adjusted, without compromising the final packed-layer structure. Using this up-scaled model, case studies quantify how operating conditions shape component and packing distributions in successive layers, and they offer practical guidance for charging strategies that improve mixture distribution and, consequently, overall furnace performance.

The relationship between energy consumption and baggage handling systems (BHSs) has not been widely studied. In this study, this relationship is explored by considering BHS configurations - the designed arrangement of devices. The BHS consists of seven main processes: drop-off, transportation from drop-off to screening, hold baggage screening (HBS), transportation from screening to sortation, sortation, early baggage storage (EBS), and make-up. Transportation appears to cause a high share of the system's energy consumption, as it is a part of and connects several processes. An equation-based model is developed to estimate the energy consumption of BHSs with varying configurations. The model only contains parameters available at the BHS's conceptual design phase and is based on the BHS of a midsize airport in Scandinavia. It includes a series of formulas for each process, which depend on the structure of the process elements. The study proved a twofold effect of an airport’s BHS configuration on the overall energy consumption. Firstly, more process elements structured in series result in bags travelling a longer distance on the conveyor within the BHS, thereby increasing energy usage. Secondly, the parameter values for transportation impact energy consumption notably. The research suggested that more process elements structured in series decrease the overlap of device usage, which in turn reduces energy consumption.

The drilling industry faces the challenge of moving away from conventional maintenance strategies to maximise operational efficiency. This thesis introduces a holistic approach to land drilling rig Predictive Maintenance (PdM), integrating component maintenance into system-level decision-making. By taking the operations of the whole system into account, PdM can be applied more effectively, and the uptime of a drilling rig can be improved. A hierarchical Multi-Agent System (MAS) framework is proposed, employing two layers of agents for the core equipment of a drilling rig and one centralised system agent. Middle-level agents perform component diagnostics, prognostics, and operational state classification. The system agent uses expert reasoning for integrated maintenance scheduling. As a proof of concept, the proposed model is partially developed and validated, using readily available data and additional condition monitoring.
In the field research phase of this thesis, valve vibration signals were gathered from a mud pump during a geothermal drilling project in Delft. From these signals, dimensionless features were extracted and used in a Fuzzy Logic (FL) classifier combined with Weibull Accelerated Failure Time (WAFT) modelling for dynamic Remaining Useful Life (RUL) prediction. In the case study, where synthetic valve failure is induced in historical operational data, the model demonstrated its ability to predict these failures on time. Maintenance actions were suggested by the model when components reached 93% of their theoretical lifetime, preventing excessive maintenance and minimising disruption to drilling operations through integrated scheduling.

This thesis presents a data-driven design approach for emission reduction using bunker delivery notes (BDNs) to help support the revised IMO strategy to achieve net-zero greenhouse gas emissions by international shipping close to 2050. This research supports the Horizon Europe’s Digital Twin for Green Shipping (DT4GS) project which focuses on the development of digital twins (DTs). Part of this project involves the development of a DT-supported method for the design and retrofit of ships. DTs are a promising approach for supporting maritime decarbonization efforts due to their simulation and big data handling capability. Despite the abundance of shipping data and growing digitalization, the potential of using ship operational data for decarbonization efforts remains not fully exploited. A data-driven method such as a DT could fill this gap. However, as DTs, by definition, require real-time connection between a physical entity and the digital representation, developing a true DT for new-build alternatively fueled ship designs remains a challenge. This research thus starts by looking into retrofitting using data from existing ships.

A design framework is proposed to construct digital models to support a DT for retrofitting purpose. The proposed framework is tested on a case-study using a 300-meter bulk carrier. Since January 2019, operational ship data is collected through BDNs, a mandatory data collection method for ships of 5000 GT and above, adopted by the IMO. Constructing a DT based on BDNs is considered to be convenient as it provides a solid source of operational data in the future.

First, the available data from the BDNs is preprocessed using an adopted framework based on data science literature. The resulting 5,678 data points are used for the construction of a model representing the bulk carrier and a model representing the green ship technologies part. A fuel consumption model is constructed to represent the bulk carrier. It utilizes a gray-box modeling approach, consisting of a white-box resistance model and a black-box artificial neural network. Both models incorporate environmental-dependent inputs. The investigated green ship technologies for the potential retrofit are represented by various wind-assisted ship propulsion (WASP) systems, namely a towing kite, a DynaRig sail, and a Flettner rotor. These systems are modeled using a white-box modeling approach, together with available wind data. Using an adopted integration framework, based on the propeller-engine matching procedure, both representations are combined into one green ship digital model.

An environmental assessment is performed using the IMO's EEXI and CII assessment tools, respectively evaluating the design and operational aspects of the potential retrofit. Additionally, a financial assessment is conducted using the payback period. Results showed the design implications and emissions reduction potential of implementing such systems which will guide the retrofit decision by the ship's owner.

This research focuses on the complexities of integrating routing decisions, inventory management and pressure control in the context of the cryogenic logistics of Liquefied Natural Gas (LNG). This research aims to address these complexities by developing a planning method that considers the transportation and nitrogen cooling costs. The study evaluates the developed planning method through a case study at Rolande, which is a company that operates LNG and Bio-LNG refuelling stations in the Netherlands, Belgium and Germany.

This study considers three refrigeration methods for controlling the pressure level at refuelling stations. These are nitrogen cooling, offload cooling and logistic trailer cooling. The identified key performance indicators that are used to evaluate the develop planning method are: Total Costs, Total Transportation Costs, Total Nitrogen Cooling Costs and Cost per Kilogram. Furthermore, the key constraints and variables for the integrated control of the LNG logistics are identified.

The planning methodology was developed through three steps. First a Full Mixed-Integer Linear Programming (MILP) model was developed. Second, the Full-MILP model was refined to a Simplified MILP model. This Simplified MILP model was improved with the rolling horizon approach and a pre-solve process. The rolling horizon approach divided the planning horizon into smaller manageable time blocks. The pre-solve process identified the critical stations for each day to reduce the number of nodes in the network.

The proposed planning methodology was experimented in the case study that involved a network of 19 refuelling stations in the Netherlands and Belgium. The sensitivity analysis indicated that the model was sensitive to the vehicle capacity. Therefore, the pre-solve process was extended with determining the supply of LNG. The case study results revealed that the model is able to solve a seven-day planning horizon, while maintain the inventory and pressure levels within the specified bounds. However, the model can become computationally complex for days with high number of critical stations and cool vehicles.

This research contributes to inland LNG logistics by addressing the integration of routing, inventory and pressure management. Future studies should focus on a comparative analysis with heuristics, experimenting the feasibility and computational time with soft inventory and pressure bounds, and model a non-linear offload cooling effect to improve the realism of the model.

With Schiphol Airport's flight numbers growing, working assets are essential to ensure on-time processes. The Passenger Boarding Bridge (PBB) is a critical asset in the airport's turnaround process. By ensuring that the asset is working properly, the operational processes can run efficiently. Currently, improving the reliability of the PBB when in use happens after the fault has occurred. With this maintenance strategy, the PBB data is not used to predict the future health state of the PBB. Literature shows that the PBB can be classified as a multi-component system. Research in the predictive maintenance strategy of multi-component systems is still in an early phase. Research until now is more theoretical than practical, and an investigation into applying theoretical knowledge in practice is needed. With the upcoming developments of Industry 4.0, a Cyber-Physical System (CPS) architecture is proposed for a multi-component system. This architecture has been applied to the PBB to develop and use a predictive maintenance strategy for this system. Based on the implementation of a simulation model, the output showed that the proposed CPS architecture enabled the development of a predictive maintenance strategy for the PBB. With this strategy, proactive maintenance is planned while the system's reliability is held on a preset level to ensure a working asset during in-time use.

Efficient maintenance scheduling is critical to ensure the reliability and cost-effectiveness of production lines. Traditional approaches often rely on corrective maintenance and limited preventive maintenance, which can lead to unplanned downtime and higher operational costs. This research investigates the impact of predictive maintenance, supported by a condition-based model, on the reliability and cost performance of a production line. Due to insufficient real-world condition monitoring data, a synthetic dataset was generated based on historical process data and literature assumptions. This dataset simulates the running time, probability of cavitation, and operational load conditions for valves and pumps across the production line, allowing the model to provide failure probability estimates and recommend maintenance interventions before production begins.

The study compares two scenarios: the first follows conventional maintenance practices with primarily corrective actions and minimal preventive maintenance during scheduled production-free weeks; the second scenario integrates the predictive maintenance model to guide both preventive and predictive interventions. Key performance indicators (KPIs) are defined to evaluate the effect of the model on line reliability and maintenance costs. The primary KPI, the Maintenance Downtime Index (MDI), measures the ratio of planned maintenance hours to total downtime hours, reflecting the proportion of downtime that is scheduled versus unplanned. Additional KPIs analyze the distribution of maintenance costs among corrective, preventive, and predictive actions, with higher proportions of predictive maintenance indicating improved reliability.

Results demonstrate significant benefits of using the predictive maintenance model. The MDI shows a reduction of 5% in total downtime hours due to fewer unplanned interruptions and a greater allocation of downtime to planned maintenance activities. Maintenance cost analysis reveals a 53% reduction in total costs when predictive maintenance is applied. Furthermore, the proportion of corrective maintenance costs decreases substantially, confirming that the model effectively shifts maintenance efforts from reactive to proactive interventions. These findings indicate that predictive maintenance enhances both operational reliability and cost efficiency, supporting more informed decision-making by operators and maintenance planners.

The study highlights the importance of integrating condition-based predictive models into production scheduling, even when limited real-time data is available. Synthetic datasets, grounded in historical data and validated assumptions, provide a viable approach to evaluating predictive maintenance strategies and their impact on key operational metrics. By prioritizing predictive interventions over corrective actions, production lines can achieve lower downtime, improved reliability, and reduced maintenance expenditure. The findings offer practical guidance for manufacturing operators seeking to optimize maintenance strategies and support the broader adoption of predictive maintenance in industrial settings.

The parcel transport industry utilizes automated parcel sorting systems to effectively manage incoming parcels. These systems typically comprise multiple infeed conveyors that converge onto a single merge conveyor for parcel merging. However, the conventional First-Come-First-Serve merging method results in reduced utilization of the merge conveyor and throughput. To address this, a novel approach is proposed in this study, combining a dynamic programming-based control algorithm with a maximum heap algorithm. This approach aims to enhance the utilization of the merge conveyor and achieve a balanced load among the infeeds. To optimize utilization, an additional imaginary segment is introduced ahead of the merge conveyor, synchronously moving with the merge conveyor's speed. This segment is intelligently populated with parcels using dynamic programming, eliminating unnecessary empty spaces. Results demonstrate that the dynamic programming approach outperforms other techniques in terms of utilization. This research presents an initial investigation into implementing dynamic programming as a control algorithm to improve merge conveyor utilization in the parcel transport industry.

The demand for accurate and effective cool chains has been expected to increase, especially for pharmaceuticals in the air freight industry. However, several problems and challenges remain such as cool chain breaks and cool storage capacity constraints while there are rarely routine systems in place for consistent insight into the operational quality of such systems. Besides, the concept of a DT has received increasing attention in the literature, while a multitude of applications have been found in fresh cool chains. Therefore, the DT concept has been applied to a pharmaceutical cool chain for operational quality improvement. Firstly, a novel operational quality metric has been proposed: the OCCE. Consequently, a cool chain at KLM Cargo has been studied and modelled by means of the DES technique in order to derive a virtual representation. Consequently, the DT concept has been applied through the implementation of a decision support module for cool storage decision-making. The model implementation has shown that the cool chain operational quality has been improved while the average exposure of freight has decreased.

Ship-to-Ship (STS) cargo transfers can significantly improve the efficiency of offshore installations while reducing their associated costs. However, the added complexity of cargo transfers involving ship-mounted cranes at sea poses significant challenges. To address this, crane manufacturers have introduced Relative Heave Compensation (RHC) systems to assist crane operators. Nevertheless, concerns have emerged among installation vessel companies regarding the relative heave measurement systems, which rely on Motion Reference Unit (MRU) sensors placed on both ships. Given that many supplier vessels lack the required sensors and the communication protocols pose bureaucratic hurdles, this research focuses on proposing novel, feeder-independent relative heave measuring solutions. This work introduces two unexplored sensor units, namely the LiDAR-MRU and RADAR-MRU. In the absence of actual sensor data, a simulation workflow using Simulink and Unreal Engine (UE) is presented to generate synthetic data. After, four distinct measurement solutions are developed, implemented, and evaluated using the simulated data in MATLAB. The implemented solutions estimate relative heave distance and speed with a Mean Absolute Error (MAE) ranging from 0.3-5.2% and 0.8-4.3% of the reference's maximum amplitude, and processing times from 1.8-91.4 [ms]. The results underscore their potential for field implementation. However, future validation is needed with actual sensor data.

Automation in tomato greenhouses is lacking compared with some other crops. The efficiency and consistency of greenhouse operations can be boosted by implementing automation. Therefore, a new concept that facilitates the robotisation of crop-handling tasks is investigated. This concept combines the existing technology of a movable-gutter system with the cultivation concept of a limited-truss tomato. The combination of these two techniques has not been previously explored. For this paper, a generic conceptual design framework was developed which contains a simulation model and an evaluation protocol. In the evaluation protocol a sensitivity analysis and an optimal dimension analysis is performed. Case studies are conducted to evaluate the simulated performance, to examine the capacity of the transport system and crop-handling tasks and to assess the robustness. These case studies prove that the developed framework is generic and all-encompassing. The framework has proven its worth by eliminating erroneous design choices before significant resources were spent. The new concept is promising, as it facilitates automation and has a competitive simulated production. Therefore, it is advised to move on to the next design phase, which should mainly focus on validating the crop-growth model and the preliminary design of subsystems as the transport system and components as the movable-gutter itself. Furthermore, a cost analysis and economic assessment should be conducted.

Bulk handling grabs are often used for unloading bulk materials such as iron ore and coal from bulk carrying ships. To improve and compare virtual simulations of new grabs, accurate real life kinematic data of grabs is required. Moreover kinematic data can potentially provide insight in current use of grabs, which can be interesting information for both manufacturer and customer of the grab. The possibilities towards implementation of a sensor system on bulk handling grabs are investigated by selecting a suitable sensor and measuring kinematics and operations of imitated grab movements. This research focuses on investigating kinematics of multi rope grabs with dual scoops. A sensor selection procedure is followed resulting in the selection of an Inertial Measurement Unit (IMU) sensor with Global Navigation Satellite System (GNSS) receiver. In several experiments the sensor data is compared with reference data of imitated grab movements assumed as ground truth. When using multiple IMU’s (one on each grab scoop) providing orientation data, the opening angle can be determined. The results of the experiments indicate that the position data provided by the sensor can be used for classification of activities, but less for high precision movement trajectory reconstruction. Position and orientation data of imitated grab movements can be interpreted to generate performance indicators that describe grab operations. Experiments showed that performance indicators of imitated grab movements can be identified accurate up to an error of two seconds when comparing known performance indicators and sensor data analysis. When a high level of accuracy of the kinematic data is reached, performance indicators describing grab operations can be determined more accurately.

Container traffic worldwide has shown a growing trend during the last years. Also, the number of containers handled per call has increased for many terminals. This poses challenges to existing terminals for more efficient operations and higher productivity. Automation is a way some terminals have tried to achieve this. Most existing automated container terminals were greenfield projects, but brownfield automation is gaining momentum.
Literature on brownfield automation of container terminals is limited. The PIANC MarCom WG Report n° 208 – 2021, Planning for Automation of Container Terminals is the most recent and complete guideline for automation projects, but it is mainly focused on greenfield projects. Challenges for automating brownfield terminals are different than for greenfield ones. All of this translates into little guidance being available for future brownfield automation projects. Therefore, the experiences and lessons learnt from historical brownfield automation of container terminals are a valuable source of information to guide future projects. The aim of this thesis is then to present a characterisation of historic container terminals converted, or in the process of being converted, to some degree of automated operations.
An empirical research methodology was defined to gather data on drivers for automation, challenges, observed benefits, drawbacks, and solutions adopted by brownfield automated terminals. A questionnaire was used for this purpose. Also, satellite images of the conversion process were analysed to observe implementation strategies. Additionally, terminal throughput, equipment, yard size and quay length were determined through a desk study. Data was analysed through categorization. Trends regarding the type of automation, i.e., semi-automation (automated yard equipment) or full automation (automated yard + automated transport between the stacks and the quay), and regarding the type of yard equipment adopted were determined.
For the historic conversions analysed, it was concluded that the main drivers for automation are operational cost reductions, labour shortage, productivity increase, reliability/safety, and capacity. But these drivers are case specific. No benefits can be assumed a priori from automation. Objectives for automating should be defined by each terminal and solutions to achieve them proposed. The main challenges were continuity of operations during the conversion, adaptation to new operations, labour relations, and communication systems. Most terminals adopted semi-automated solutions, due to fewer labour issues expected and improved vessel productivity compared to full automation, among other reasons. It was observed that terminals with yards smaller than 10 [ha] chose aRTGs as their stacking equipment, while terminals with larger yards chose either an aRMG or the automated version of their yard equipment before automating.
Three implementation strategies were observed: greenfield-like, phased, and big bang approaches. The first one refers to developing the automated yard in a new location, with little disruption of the operations of the old yard. A phased approach means that the implementation is divided in phases, and operations of the old yard must be disrupted from phase 1. The big bang approach was observed only for conversions from straddle carriers (SC) to auto SC. In these cases, a small site is used to test the technology. When the testing is finished, the terminal is closed for 2-3 days, and the technology is rolled out onto the entire yard or a large portion of it.

In this thesis, a simulation cost model is presented that can be used to assess the cost-effectiveness of a selection of PdM programs. The proposed model combines a discrete event simulation (DES) model with a cost model. It is specifically designed to analyse the PdM programs for the vacuum compressor pumps at Duyvis. All the PdM programs proposed will work with vibration analysis to detect bearing failure. The simulation model will focus on the sample frequency and detect-ability of a PdM program. The sample frequency is related to maturity levels, and the detect-ability is related to detecting the different stages of bearing damage. The goal is to use the proposed simulation cost model to assess a selection of PdM programs on their overall cost and reliability.