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The present article introduces and investigates a new approach for shaving the peak electricity demand and mitigating energy instability. At the heart of this concept is a smart integration for efficient hydrogen production/storage/usage to minimize energy costs and maximize the renewable penetration in the local electricity grid. The system is driven by a wind farm integrated with proton exchange membrane (PEM) electrolyzers and reverse osmosis desalination units for efficient electricity, hydrogen, and freshwater production. It also combines with PEM fuel cells equipped with a hydrogen tank to meet the demand constantly when renewable electricity is unavailable or unstable. The system's practicality is assessed and compared for various Swedish cities with high wind potential from thermodynamic, economic, and environmental aspects to see where it works effectively. The comparative results of various scenarios show that integrating 32 wind turbines, 2 electrolyzers, and 2 reverse osmosis units, with 25% of electricity going to electrolyzers, 20% to reverse osmosis, and 55% to the grid, is the most optimal configuration/allocation. Optimal locations for the power plant are identified in Visby, Halmstad, and Lund due to favorable wind conditions. Setting up the system in Visby could prevent 1878.2 tonnes of CO2 emissions, generate 93,910 MWh of electricity annually, and create 213 ha of green space. The proposed system in Visby could boast the biggest electricity generation capacity, reaching 11,263 MWh, sufficient to power 938 households. Scaling this model to 12 cities in Sweden could provide the electricity needs of 4500 households, demonstrating the potential for widespread impact.

The impact of drug delivery and particulate matter exposure on the human respiratory tract is influenced by various anatomical and physiological factors, particularly the structure of the respiratory tract and its fluid dynamics. This study employs computational fluid dynamics (CFD) to investigate airflow in two 3D models of the human air conducting zone. The first model uses a combination of CT-scan images and geometrical data from human cadaver to extract the upper and central airways down to the ninth generation, while the second model develops the lung airways from the first Carina to the end of the ninth generation using Kitaoka’s deterministic algorithm. The study examines the differences in geometrical characteristics, airflow rates, velocity, Reynolds number, and pressure drops of both models in the inhalation and exhalation phases for different lobes and generations of the airways. From trachea to the ninth generation, the average air flowrates and Reynolds numbers exponentially decay in both models during inhalation and exhalation. The steady drop is the case for the average air velocity in Kitaoka’s model, while that experiences a maximum in the 3rd or 4th generation in the quasi-realistic model. Besides, it is shown that the flow field remains laminar in the upper and central airways up to the total flow rate of 15 l/min. The results of this work can contribute to the understanding of flow behavior in upper respiratory tract. Graphical Abstract: (Figure presented.)

Despite various preventive interventions, nosocomial cross-infection remains a significant challenge in healthcare facilities worldwide. Consequently, prolonged hospitalization, elevated healthcare costs, and mortality rates are major concerns. Proper ventilation has been identified as one of the possible interventions for reducing the risk of cross-infection between patients and healthcare workers in hospital wards by diluting infectious agents and their carrying particles. The use of air cleaners in conjunction with the ventilation system further reduces the concentration of indoor pathogens. This article presents a systematic review of the ventilation solutions employed in hospital wards where pathogen removal performance can be enhanced using air-cleaning techniques while maintaining the thermal comfort of patients and healthcare staff. We provide a comparative analysis of the performance of different ventilation strategies adopted in one-, two-, or multi-bed hospital wards. Additionally, we discuss the parameters that influence the aerosol removal efficiency of ventilation systems and review various air-cleaning technologies that can further complement the ventilation system to reduce contaminant concentrations. Finally, we review and discuss the impact of different ventilation strategies on the perceived thermal comfort of patients and healthcare workers. This study provides insights into the cross-contamination risks associated with various hospital ward setups and the vital role of the ventilation system in reducing the adverse effects of infection risk. The findings of this review will contribute to the development of effective ventilation solutions that ensure improved patient outcomes and the well-being of healthcare workers.

This article presents and thoroughly examines an innovative, practical, cost-effective, and energy-efficient smart heating, ventilation, and air conditioning (HVAC) system. The fundamental component of this concept is a state-of-the-art method called Deep Green Cooling technology, which uses deep drilling to utilize the ground's heating and cooling potential directly without the need for machinery or heat pumps. This method satisfies demands with the least energy use, environmental impact, and operational costs. In order to effectively oversee and regulate energy production, storage, and utilization, the system consists of an intelligent control unit with many smart controllers and valves. Renewable energy deployment is made easier, and the intelligent automation unit is more compatible with the help of a high-temperature cooling resource with a high supply temperature of 16 °C. The technical, environmental, and financial aspects of the suggested smart office building system in the southern region of Uppsala, Sweden, are evaluated using TRNSYS software. According to the results, boreholes provide more than 28.5 % of the building's energy requirements by utilizing the ground's ability to generate affordable, dependable seasonal thermal energy. The district heating network satisfies the remaining demand, amounting to 787.2 MWh, highlighting the benefits of combining conventional and renewable energy sources for increased supply security and dependability. The borehole thermal energy storage system meets the building's entire cooling need, underscoring the importance of high-temperature cooling systems. The most expensive part of the system is the borehole thermal energy storage, which accounts for over half of the total investment. The system has an appropriate payback period of ten years, proving its long-term profitability and cost-effectiveness, thanks to removing the machinery and heat pump. With 3138 MWh of ground-source heating and cooling, the system saves 17,962 USD by reducing CO2 emissions by about 143.7 t, sufficient to grow 16.3 ha of trees throughout the payback period.

The present article proposes a novel smart building energy system utilizing deep geothermal resources through naturally-driven borehole thermal energy storage interacting with the district heating network. It includes an intelligent control strategy for lowering operational costs, making better use of renewables, and avoiding CO2 emissions by eliminating heat pumps and cooling machines to address the heating and cooling demands of a commercial building in Uppsala, a city near Stockholm, Sweden. After comprehensively conducting techno-environmental and economic assessments, the system is fine-tuned using artificial neural networks (ANN) for optimization. The study aims to determine which ANN design and training procedure is the most efficient in terms of accuracy and computing speed. It also assesses well-known optimization algorithms using the TOPSIS decision-making technique to find the best trade-off among various indicators. According to the parametric results, deeper boreholes can collect more geothermal energy and reduce CO2 emissions. However, deep drilling becomes more expensive overall, suggesting the need for multi-objective optimization to balance costs and techno-environmental benefits. The results indicate that Levenberg-Marquardt algorithms offer the optimum trade-off between computation time and error minimization. From a TOPSIS perspective, while the dragonfly algorithm is not ideal for optimizing the suggested system, the non-dominated sorting genetic algorithm is the most efficient since it yields more ideal points rated below 100. The optimization yields a higher energy production of 120 kWh/m2, as well as a decreased levelized cost of energy of 57 $/MWh, a shorter payback period of two years, and a reduced CO2 index of 1.90 kg/MWh. The analysis reveals that despite the high investment costs of 382.50 USD/m2, the system is financially beneficial in the long run due to a short payback period of around eight years, which aligns with the goals of future smart energy systems: reduce pollution and increase cost-effectiveness.

This work presents novel energy production/storage/usage systems to reduce energy use and environmental effects, in order to address concerns about excessive heating demand/emissions in buildings. This focus is the design, control, and comparison of a biomass-fired model with a novel heater type and a solar-driven system integrated with photovoltaic thermal (PVT) panels and a heat pump. The heater has an external boiler and shell and tube heat exchanger, providing enhanced control over the combustion process and increased efficiency. Another feature of the present work is establishing a rule-based automation framework to manage the energy storage/flow among the components/grid/building. This smart integration reduces the size of the components, eliminates the need for a battery, and allows the system to interact in both directions with the electricity grid. The practicality of both systems is assessed and compared via a code developed in TRNSYS-MATLAB, considering the specific conditions of Toronto, Canada, characterized by high heat demand in winter. According to the results, the proposed solar-based system has an acceptable energy cost (78.9 USD per MWh of heating and electricity) attributable to the developed controllers applied to thermal energy storage. The results show that the PVT-based system integrated with a heat pump is environmentally superior, with a reduction in CO2 emission of 7.2 tonnes over a year. However, the biomass-fired system is an excellent option from the aspect of efficiency, with a relatively high energy efficiency of 69 %. Also, it is observed that the night set-back of the supply temperature can reduce the annual primary energy use and emission up to 60.3 MWh and 21.1 t, respectively. While the system relies more on the heat pump in cold months, the solar energy system supplies the entire demand in summer, demonstrating the significance of PVT and heat pump integration to increase energy reliability throughout the year.

In recent years, pandemic outbreaks have raised concerns about the spread of respiratory infections and their impact on public health. Since the pathogen emission during human respiration is recognized as the primary source, characterizing the physical properties of exhaled particles and airflow has become a crucial focus of attention. This article critically reviews experimental studies in exhaled particles and airflow, examines the uncertainty introduced by different measurement methods, analyzes how it is reflected in measurement outcomes, and provides an in-depth understanding of particle size distribution and airflow behaviors of human respiration. The measurement techniques assessment highlights the variability among particle sizing techniques in detection size range, collection efficiency, hydration status of captured particles, and experimental protocols. A combination of sampling-based instruments and laser imaging systems is recommended for particle sizing to cover a wider detection range, with refined setups in thermal conditions, sampling distance, volume, and duration. Meanwhile, it identifies the complementary nature of qualitative and quantitative measurements of airflow characterization techniques. Image recording systems plus data reconstruction programs are suggested to capture dynamic airflow features while accuracy validation by other techniques is required at the same time. Subsequent analysis of the measurement data showed that the various experimental measurements provided substantial information, but they also revealed disagreements and challenges in quantification. The dominance of submicron aerosols in exhaled particles and jet-like transport in exhaled airflow is obvious. More efforts should be made to measure particles larger than 20 μm, capture airflow dynamics in a high temporal and spatial resolution, and quantify the impact of face coverings to improve the understanding of human respiratory emissions.

This article investigates the efficacy of temperature-controlled airflow systems in modern operating rooms for contaminant control, a critical factor in preventing surgical site infections. We have conducted experimental measurements in an operating room equipped with temperature-controlled ventilation to map the airflow field and contaminant dispersion (airborne particles with diameters ranging from 0.5 to 1 μm). The results were used to validate the computational fluid dynamics code, which was then employed to simulate and examine different conditions, including contaminant release locations and air supply rates. Realizable k-epsilon and passive scalar models were utilized to simulate airflow and airborne particle phases. We assessed the airflow distribution and contaminant dispersion, utilizing indices such as ventilation and air change efficiency scales. The analysis provided quantitative insights into the distribution and removal of contaminants, as well as the speed at which the room air was replaced. Contamination was found to be effectively reduced when contaminants were released near exhaust outlets or under central unidirectional inlets. The presence of the operating table caused a big distortion of the central downward airflow, forming a horizontal air barrier at the periphery. Under this unique interior configuration, an appropriate air supply ratio between central and periphery zones was required to achieve optimal overall ventilation performance.

The present paper introduces a new smart building system driven by photovoltaic thermal panels. The concept is to improve the contribution of renewable energy in the local matrix for peak load shaving by having a two-way connection with the local electricity network via a rule-based energy monitoring control design. Besides, the feasibility of removing the electrical storage unit with high investment cost is studied by establishing a dynamic interaction between the energy production and usage components to reduce the energy costs over the year. The system has intelligent thermal energy storage integrated with an electrically-driven coil, heat exchanger, pumps, and several smart valves and control units. The transient system simulation (TRNSYS) package is implemented to assess the practicality of the suggested intelligent model for a building complex in Malmo, Sweden. According to the parametric outcomes, by raising the panel area, while the generated electricity increases, the solar utilization factor falls, indicating conflictive changes among performance metrics. The results also show that the renewable resource covers the building's heating and electricity demands for the majority of the year and that a significant amount of energy is sold to the neighbourhood electricity grid, demonstrating the viability of the introduced intelligent model.

This article aims to support the targeted worldwide green transition process by introducing and thoroughly analyzing a low-temperature heating and high-temperature cooling, smart building system. This concept allows for greater use of renewable energy while utilizing less input energy than conventional heating and cooling techniques. The proposed system consists of a reversible water-to-water heat pump driven by low-temperature geothermal energy. A rule-based control strategy is developed to establish an intelligent connection with the regional energy grids for peak shaving and compensating for the building's energy costs over the year. The dynamic simulation is carried out for a multi-family building complex in Stockholm, Sweden, using TRNSYS. The most favorable operating condition is determined via an artificial neural network-assisted tri-objective optimizer based on the grey wolf algorithm in MATLAB. The comparison of the proposed smart model with the conventional system in Sweden results in 332%, 203%, and 190% primary energy reduction, cost saving, and carbon dioxide emission mitigation, respectively. As indicated by the parametric results, the conflicting fluctuation between desirable and unfavorable indicators highlights the importance of multi-objective optimization. The grey wolf optimizer obtains 12% higher efficiency, 1.2 MWh lower annual bought energy, 24 $/MWh lower unit cost, and 5.1 MWh more yearly sold energy than the design condition. The scattered distribution reveals that tank volume and subcooling degree are sensitive parameters. According to the transient results, the suggested smart system can independently satisfy the building's heating, cooling, and electricity demands for more than 81% of the year, thanks to the two-way connection with the electricity and heating networks via the rule-based controller.