Retrofit heat exchanger network (HEN) optimization is a fundamentally unique problem which requires the consideration of existing structures, compared to grassroots design problems. The optimization of retrofit HENs is particularly difficult due to the integration of both existing and newly acquired equipment. The re-routing of existing equipment can lead to various network topologies, increasing the complexity of considerations. In this work, we exploit the P-graph framework to solve retrofit HEN problems, guaranteeing to find the topology of optimal solutions within the constrained space of the HEN retrofit problem. The P-graph framework has additional advantages that allows topologically-efficient search space, simplifies additional unit placement, considers unit positional swapping (re-sequencing and re-piping within search constraints), considers stream splitting, and n-best solution visualization. The pinch minimum utility constraint also provides a bound for the maximum number of modifications in the HEN, significantly reducing search space. The proposed P-graph-based approach is demonstrated using a real refinery case study to show its capability in obtaining the topology of the optimal HEN, highlighting the economic and energy benefits. Further extensions to other retrofit process integration problems (e.g. retrofit water network, hydrogen network etc.) will be enabled via the proposed P-graph approach.

P-graph is a popularly used framework for process network synthesis (PNS) and network topological optimization. This short communication introduces a Python interface for P-graph to serve as a linkage to modern programming ecosystems. This allows for a wider application of the fast and efficient P-graph solver, to provide structural and topological enumeration in numerous fields. The proposed framework allows for more integrative usage in Artificial Intelligence (AI), machine learning, process system engineering, chemical engineering and chemometrics. Large and repetitive topologies can also be automated using the new programming interface, saving time and effort in modelling. This short communication serves as a demonstration of the newly developed open-sourced P-graph interface.

With the adoption of the European Green Deal, the target to reduce net greenhouse gas emissions by 55 % by 2030, compared to 1990 levels, requires a higher renewable energy fraction and better energy efficiency. This requires a comprehensive re-evaluation of the power infrastructure within the European Union (EU). To achieve this, a EU-focused digital twin has been constructed, focusing on the European region and neighboring countries. The twin uses annual and 30-min resolution data from 113 main stations representing 40 countries, with a focus on EU member states. Multi-way analysis (PARAFAC2) is used to align interpretation for both data and high-resolution data, prioritizing regional energy infrastructure features. An automated graph-theory (P-graph) approach is used to construct a large-scale multi-time-sliced energy-balanced model as a digital twin model. This novel integration of macro-level trend analysis (via PARAFAC2), time-resolved optimization, and equity-based constraints enables a data-driven exploration of diverse policy scenarios. This study shows that effective EU energy policy should balance renewable diversification, equity in energy access, and regional cooperation, as policy shifts significantly affect energy flows and trade dynamics. While resilient infrastructure may require high investment, trade-off analysis reveals cost-effective, balanced pathways that optimize both sustainability and security objectives. The work demonstrates the potential for data-driven policy making for regional or international infrastructure, focusing on optimization of energy transfer activities, promotion of renewable sources, and systematic planning.