RESUMO
An 8-year-old male patient was presented with a chief complaint of visual perception of a spherical object located in the visual field of his right eye. According to the patient's prior medical records, they exhibited a sole incident of minor visual impairment in the right eye, characterized by blurred vision (best-corrected visual acuity: 8/10), which first manifested during preschool assessments at the age of 5 years. During the fundoscopic examination of the patient, a pigmented, lobulated, and mobile vitreous cyst was observed in the right eye, whereas the examination of the patient's left eye revealed no abnormalities. A video of a smartphone slit-lamp imaging was also captured to facilitate subsequent examinations and patient follow-up.
RESUMO
COVID-19 has affected energy consumption and production pattern in various sectors in both rural and urban areas. Consequently, energy demand has increased. Therefore, most health care centers report a shortage of energy, particularly during the summer seasons. Therefore, integrating renewable energies into hospitals is a promising method that can generate electricity demand reliably and emits less CO2. In this research paper, a hybrid renewable energy system (HRES) with hydrogen energy storage is simulated to cover the energy demand of sections and wards of a hospital that dealt with COVID-19 patients. Produced Oxygen from the hydrogen storage system is captured and stored in medical capsules to generate the oxygen demand for the patients. Results indicate that 29.64% of the annual consumed energy is utilized in COVID-19 sections. Afterward, modeled system has been optimized with a neural network-genetic algorithm to compute the optimum amount of the demand power from the grid, CO2 emission, oxygen capsules, and cost rate. Results determine that by having 976 PV panels, 179 kW fuel cell, and 171.2 kW electrolyzer, annual CO2 emission is 315.8 tons and 67,833 filled medical oxygen capsules can be achieved. The cost rate and demand electricity from the grid for the described system configuration are 469.07 MWh/year and 18.930 EUR/hr, respectively.
RESUMO
In the current research, an innovative biomass-based energy system is proposed for power and desalinated water production. The plant's primary components consist of a gasifier, a compressor, a heat exchanger, a gas turbine, a combustion chamber, and a Multi-effect desalination with thermal vapor compression (MED-TVC) unit. A comprehensive thermodynamic and thermoeconomic assessment is conducted on the proposed system. Besides, a parametric study is conducted to determine the effect of primary decision variables on the system performance. Multiple objective optimization using the multi-objective grey wolf optimizer (MOGWO) algorithm is applied to obtain the optimal solution with the highest exergy efficiency and the minimum amount of total cost rate. The artificial neural network (ANN) has an intermediary role in the optimization process to decrease computational time and enhance optimization speed. The relation between the objective function and decision variables is investigated, employing ANN to determine the energy system's optimum point. The generation rate for power and freshwater at the optimal point is equal to 5127 kW and 38.6 kg/s, respectively. Besides, the optimum value of the exergy efficiency and total cost rate are computed as 15.61% and 206.78 $/h, respectively. The results also revealed that the number of effects of the desalination unit does not affect the carbon dioxide emissions. Moreover, the scatter distribution of the key decision variable indicates that the air compressor pressure ratio is not a sensible variable, and their optimum points are distributed across the entire domain.
