Repurposing Natural Gas Infrastructure for Hydrogen Transmission: Development of a network optimisation model for finding minimum cost networks that utilise existing infrastructure

Societal concern is growing regarding the effect fossil energy use has on our planet and societies. Many promising sustainable energy solutions, like hydrogen, carbon capture and storage, and district heating, require long-distance distribution and transmission through networked infrastructure. Often, existing infrastructure can be repurposed to reduce overall network costs. Examples are the repurposing of natural gas infrastructure for hydrogen transport or the repurposing of oil pipelines for transport of CO2. This repurposing of existing infrastructure is expected to have a major influence on future network layouts, which uncovers an interesting research question. What is a suitable network optimisation approach that aids in the design of costeffective future network layouts, taking into account repurposing of existing infrastructure?. Reviewing the available literature on network optimisation modelling in relation to repurposing of existing infrastructure, we find that Geometric Graph Theoretical methods are the most promising approach to the problem at hand. Mixed Integer (Non-)linear programming approaches are computationally heavy, and do not provide the flexibility necessary to gain quick insights into design implications. Agent Based models provide more than enough flexibility, but no suitable methodology for integrating repurposing was found. A network optimisation model by Heijnen et al. (2020) is selected for adaption because of its already integrated existing pipeline functionality. Limitations of the existing model are that it does not take into account costs related to the repurposing of existing infrastructure, and that it can not work with cycles in the network. These cycles, defined by a multitude of paths between two points, are necessary because existing infrastructure often contains cycles, and the optimal addition of new infrastructure can include cycles that contain existing infrastructure. To address these limitations, first, repurposing costs were integrated into the objective function of the model. A novel variable called the repurposing cost coefficient is used to allow model users to input the cost of repurposing relative to the building of new pipelines. Three novel heuristics are also introduced. The network simplex repurposing heuristic creates optimal network layouts for a single moment in time and allows for cycles in the network to be created if this is the most optimal layout. When investigating supply and demand patterns that differentiate over time, the heuristic is supplemented by the timesteps merged heuristic that combines the networks created for each separate moment in time into one network. Finally, the joined edges heuristic removes redundancies from the network to create a cost optimised network layout. The model was unit tested by applying it to small experiments, and the basic functionalities of the model were verified...Complex Systems Engineering and Management (CoSEM

Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

Background: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. Methods: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. Results: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. Conclusion: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes