Improving CO2 concentration, CO2 pollutant and occupants' thermal comfort in a residential building using genetic algorithm optimization

Environmental air pollutants pose a serious threat to human society's health and way of life, and their amount is rising quickly. Given that we spend the majority of our time indoors, good indoor air quality (IAQ) is essential. When the concentration of pollutants that have seeped into the building space, like CO2, is reduced, this favorable air quality becomes significant. 39 design factors, including the temperature setting of the air condi-tioning system's (Acs) thermostat for cooling and heating, the insulation level of the residents' clothing and the air speed for all months of the year, the level of activity envelope building, and the amount of clean air that is transferred from the outside to the inside of the building by the air conditioning, have been taken into account in this research in order to solve this significant problem in the residential sector. The objective is to simultaneously lower CO2 concentration (CCO2) and CO2 pollutant (PCO2) and raise thermal comfort (Tc) levels for building occupants. Design variables and objective functions are defined in JEPLUS software. Using EnergyPlus software, they are subsequently examined in terms of the building's energy efficiency. The extracted data is then sent to the JEPLUS EA software. In this software, Morris sensitivity analysis (MSA) is used to determine how input variables affect particular objective functions, and the NSGA-II algorithm is then used to optimize the results, which allows users to select any mode that best suits their objectives. According to the optimization's findings in terms of CCO2, PCO2 and the residents' Tc were improved by 17-30, 15-37, and 52-80 percent, respectively. The levels of additional pollutants (CH4, N2O, NOx, SO2) that decreased were also examined.