This research investigates the optimal integration of power and heat grids to meet energy demands in the Drechtsteden region at minimal costs. Driven by the global push for decarbonization, the study explores the cost-effectiveness of reinforcing the existing power grid versus expanding the heat grid infrastructure, including the potential of waste heat utilization. A Python-based model, leveraging the PyPSA library, was developed to simulate and optimize different scenarios. It was found that reinforcing the power grid is the most cost-effective solution for satisfying the 2030 heat demand, assuming stable geopolitical conditions and material availability. However, under specific conditions, such as decreased heat grid connection costs or abundant waste heat sources, a combined approach might become more viable. The sensitivity analysis highlights the significant impact of technology costs on decision-making. Future research should incorporate resource constraints, long-term projections, and spatial limitations for a more comprehensive assessment of energy system integration.

This research investigates and describes an image search engine for digital history using deep learning technologies. It is part of the Engineering Historical Memory research, contributing to a multilingual and transcultural approach to decode-encode the treasure of human experience and transmit it to the next generation of world citizens. The engine provides a new way to search in online (historical) digital libraries using content-based image retrieval and makes linguistic metadata redundant. State-of-the-art deep learning methodologies in computer vision have been investigated and tested. These methodologies include both template-based matching and feature-based matching. A VGG16 Convolutional Neural Network based approach, called D2-Net, is concluded to provide the best basis. D2-Net is then further analyzed, improved, and optimized to run on a large dataset of more than 12k image combinations related to history, heritage, and art. The final implementation shows promising results with a precision of 0.96 and a recall of 0.44 on a challenging testing dataset. Future improvements include speed improvement and model training.