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The present work introduces an innovative yet feasible heating system consisting of a ground source heat pump, borehole thermal energy storage, an auxiliary heater, radiators, and ventilation coils. The concept is developed by designing a new piping configuration monitored by a smart control system to reduce the return flow temperature and increase the temperature differential between the supply and return flows. The radiators and ventilation heating circuits are connected in series to provide the heat loads with the same demand. The investigation of the proposed model is performed through developed Python code considering a case study hospital located in Norway. The article presents, after validation of the primary heating system installed in the hospital, a parametric investigation to evaluate the effect of main operational parameters on the performance metrics of both the heat pump and the total system. According to the results, the evaporator temperature is a significant parameter that considerably impacts the system performance. The parametric study findings show that the heat pumps with a thermal capacity of 400 kW and 600 kW lead to the highest heat pump and total seasonal performance factors, respectively. It is also observed that increasing the heat pump capacity does not affect the performance indicators when the condensation temperature is 40 °C and the heat recovery is 50%. Moreover, choosing a heat pump with a smaller capacity at the heat recovery of 75% (or higher) would be an appropriate option because the seasonal performance values are not varied by changing the heat pump capacity. The results reveal that reducing return temperature under a proper parameters selection results in substantially higher seasonal performance factors of the heat pump and total system. These outcomes are in-line with the United Nations sustainable development goals including Sustainable Cities and Communities.

This work proposes a novel yet practical dragonfly optimization algorithm that addresses four competing objectives simultaneously. The proposed algorithm is applied to a hybrid system driven by the solid oxide fuel cell (SOFC) integrated with waste heat recovery units. A function-fitting neural network is developed to combine the thermodynamic model of the system with the dragonfly algorithm to mitigate the calculation time. According to the optimization outcomes, the optimum parameters create significantly more power and have a greater exergy efficiency and reduced product costs and CO2 emissions compared to the design condition. The sensitivity analysis reveals that while the turbine inlet temperatures of power cycles are ineffective, the fuel utilization factor and the current density significantly impact performance indicators. The scatter distribution indicates that the fuel cell temperature and steam-to-carbon ratio should be kept at their lowest bound. The Sankey graph shows that the fuel cell and afterburner are the main sources of irreversibility. According to the chord diagram, the SOFC unit with a cost rate of 13.2 $/h accounts for more than 29% of the overall cost. Finally, under ideal conditions, the flue gas condensation process produces an additional 94.22 kW of power and 760,056 L/day of drinkable water.