Introduction: Negative pressure wound drainage (NPWD) systems are widely used in postoperative care to promote wound healing and reduce complications. However, current commercial solutions exhibit several key limitations, including pressure loss during transport, a limited shelf life due to factory pre-evacuation, and poor regulation of suction pressure, posing risks to both device performance and patient safety. Methods: This thesis aims to redesign Van Straten Medical’s NPWD system to overcome these challenges. Multiple concepts were developed by exploring various mechanisms for vacuum generation, container types, materials, and activation methods. These concepts were evaluated using a weighted criteria matrix. The winning concept, a bottle-based system activated by a constant-force spring pulling a plunger, was prototyped using 3D printing. Simulations and mechanical calculations were conducted to validate the design’s structural integrity. Performance testing compared the prototype with three existing commercial systems. Results: The prototype achieved a stable negative pressure within the target range (100–150 mmHg) and demonstrated superior pressure consistency as the container filled. Although 3D printing enabled rapid prototyping, surface imperfections led to minor air leakage and seal instability. Despite this, the prototype outperformed commercial systems in maintaining therapeutic pressure throughout use. Conclusion: This thesis presents a promising mechanical NPWD solution that enhances pressure stability, removes the need for pre-evacuation, and supports the use of recycled materials, addressing clinical, logistical, and sustainability challenges. Future refinement through injection moulding and clinical evaluation is recommended to improve sealing and manufacturability. The design holds potential to enhance patient safety while aligning with industry trends towards circular, low-waste medical products.

Increasing distributed generation of and demand for electrical energy results ever more in problems like congestion. Forecasting the demand, photovoltaic power generation and the number of electric vehicles connected to charging station for a residential neighbourhood can be an important part of a smarter distribution network for such a neighbourhood and can thereby increase the usage of renewable energy. In this thesis an overview of the design steps for making such a forecasting system is given and the steps are applied to a fictional dutch residential neighbourhood of the future. Some key findings are that for accurately forecasting the load, the temperature and time are the most important features. For accurately forecasting the PV power generation, especially the irradiation is most important, but time, horizontal view and humidity are also important features. Furthermore, it is shown that random forest regression models can accurately forecast both the demand and PV power generation with an accuracy above 90%. Artificial neural networks are also adequate models for the forecasting problems, but because they are harder to understand and not necessarily better, it is recommended to start with random forest regressors before making neural networks. Support vector machines seem less suitable for these particular forecasting problems.