Providing comfortable indoor air quality control in residential construction is an exceedingly important issue.This is due to the structure of the fast response controller of air quality.The presented work shows the b...Providing comfortable indoor air quality control in residential construction is an exceedingly important issue.This is due to the structure of the fast response controller of air quality.The presented work shows the breakdown and creation of a mathematical model for an interactive,nonlinear system for the required comfortable air quality.Furthermore,the paper refers to designing traditional proportional integral derivative regulators and proportional,integral,derivative regulators with independent parameters based on a backpropagation neural network.In the end,we perform the experimental outputs of a suggested backpropagation neural network-based proportional,integral,derivative controller and analyze model results by applying the proposed system.The obtained results demonstrated that the proposed controller can provide the required level of clean air in the room.The proposed developed model takes into consideration international Heating,Refrigerating,and air conditioning standards as ASHRAE AND ISO.Based on the findings,we concluded that it is possible to implement a proposed system in homes and offer equivalent indoor air quality with continuous mechanical ventilation without a profuse amount of energy.展开更多
As infectious respiratory diseases are highly transmissible through the air,researchers have improved traditional total volume air distribution systems to reduce infection risk.Multi-vent module-based adaptive ventila...As infectious respiratory diseases are highly transmissible through the air,researchers have improved traditional total volume air distribution systems to reduce infection risk.Multi-vent module-based adaptive ventilation(MAV)is a novel ventilation type that facilitates the switching of inlets and outlets to suit different indoor scenarios without changing ductwork layout.However,little research has evaluated MAV module sizing and air velocity selection,both related to MAV system efficiency in removing contaminants and the corresponding level of protection for occupants in the ventilated room.Therefore,the module-source offset ratio(MSOR)is proposed,based on the MAV module size and its distance from an infected occupant,to inform selection of optimal MAV module parameters.Computational fluid dynamics simulations illustrated contaminant distribution in a two-person MAV equipped office.Discrete phase particles modelled respiratory contaminants from the infected occupant,and contaminant concentration distributions were compared under four MAV air distribution layouts,three air velocities,and three module sizes considered using the MsOR.Results indicate that lower air velocities favour rising contaminant levels,provided the ventilation rate is met.Optimal contaminant discharge can be achieved when the line of outlets is located directly above the infected occupant.Using this parameter to guide MAV system design,85.7% of contaminants may be rendered harmless to the human body within 120 s using the default air vent layout.A more appropriate supply air velocity and air vent layout increases this value to 91.4%.These results are expected to inform the deployment of MAV systems to reduce airborne infection risk.展开更多
Since the coronavirus disease 2019,the extended time indoors makes people more concerned about indoor air quality,while the increased ventilation in seeks of reducing infection probability has increased the energy usa...Since the coronavirus disease 2019,the extended time indoors makes people more concerned about indoor air quality,while the increased ventilation in seeks of reducing infection probability has increased the energy usage from heating,ventilation,and air-conditioning systems.In this study,to represent the dynamics of indoor temperature and air quality,a coupled grey-box model is developed.The model is identified and validated using a data-driven approach and real-time measured data of a campus office.To manage building energy usage and indoor air quality,a model predictive control strategy is proposed and developed.The simulation study demonstrated 18.92%energy saving while maintaining good indoor air quality at the testing site.Two nationwide simulation studies assessed the overall energy saving potential and the impact on the infection probability of the proposed strategy in different climate zones.The results showed 20%–40%energy saving in general while maintaining a predetermined indoor air quality setpoint.Although the infection risk is increased due to the reduced ventilation rate,it is still less than the suggested threshold(2%)in general.展开更多
Indoor CO2 concentration depends on the number of persons, their metabolic rates, other sources of indoor pollution, ventilation rate and ventilation efficiency. These factors are not considered by the Spanish technic...Indoor CO2 concentration depends on the number of persons, their metabolic rates, other sources of indoor pollution, ventilation rate and ventilation efficiency. These factors are not considered by the Spanish technical building code since ventilation is set only by a fixed air change rate. This paper aims to explore the possibilities of DCVS （demand controlled ventilation systems） to ensure adequate and sustainable ventilation. It is based on a research project carried out by the University of the Basque Country （EHU-UPV） and Euskadi Public Housing and Soil Join-Stock Company （VISESA）： the living rooms of 90 dwellings were provided with DCVS, where CO2 sensors were used to dynamically control the ventilation rate. Tests were carried out using tracer gas techniques, with results showing the air age to be adequate at every point of the occupied zones and free of stagnant areas, therefore proving the system＇s effectiveness and rapid response, and its energy savings.展开更多
High concentrations of indoor CO_(2)pose severe health risks to building occupants.Often,mechanical equipment is used to provide sufficient ventilation as a remedy to high indoor CO_(2)concentrations.However,such equi...High concentrations of indoor CO_(2)pose severe health risks to building occupants.Often,mechanical equipment is used to provide sufficient ventilation as a remedy to high indoor CO_(2)concentrations.However,such equipment consumes large amounts of energy,substantially increasing building energy consumption.In the end,the issue becomes an optimization problem that revolves around maintaining CO_(2)levels below a certain threshold while utilizing the minimum amount of energy possible.To that end,we propose an intelligent approach that consists of a supervised learning-based virtual sensor that interacts with a deep reinforcement learning(DRL)-based control to efficiently control indoor CO_(2)while utilizing the minimum amount of energy possible.The data used to train and test the DRL agent is based on a 3-month field experiment conducted at a kindergarten equipped with a heat recovery ventilator.The results show that,unlike the manual control initially employed at the kindergarten,the DRL agent could always maintain the CO_(2)concentrations below sufficient levels.Furthermore,a 58%reduction in the energy consumption of the ventilator under the DRL control compared to the manual control was estimated.The demonstrated approach illustrates the potential leveraging of Internet of Things and machine learning algorithms to create comfortable and healthy indoor environments with minimal energy requirements.展开更多
The present study examined the association between residential indoor remodeling and poor semen quality. Sperm donors aged 18-45 years old were recruited in Shanghai, China. Semen specimens were collected and analyzed...The present study examined the association between residential indoor remodeling and poor semen quality. Sperm donors aged 18-45 years old were recruited in Shanghai, China. Semen specimens were collected and analyzed. An in-person interview was conducted to obtain information on the history of indoor remodeling and potential confounders. A total of 70 participants with abnormal semen quality （case group） and 68 controls were examined. A total of 20 subjects reported indoor remodeling in the recent 24 months, and among them 17 subjects reported indoor remodeling in the recent 12 months. Compared with participants with no history of indoor remodeling, participants with a history of indoor remodeling in the recent 24 months were more than three times as likely to have poor sperm quality （adjusted odds ratio = 3.8, 95% confidence interval： 1.3-12.0） after controlling for potential confounders. The association was strengthened when the analysis was restricted to those who had indoor remodeling in the recent 12 months. Our findings provide preliminary evidence that indoor remodeling has an adverse effect on semen quality.展开更多
文摘Providing comfortable indoor air quality control in residential construction is an exceedingly important issue.This is due to the structure of the fast response controller of air quality.The presented work shows the breakdown and creation of a mathematical model for an interactive,nonlinear system for the required comfortable air quality.Furthermore,the paper refers to designing traditional proportional integral derivative regulators and proportional,integral,derivative regulators with independent parameters based on a backpropagation neural network.In the end,we perform the experimental outputs of a suggested backpropagation neural network-based proportional,integral,derivative controller and analyze model results by applying the proposed system.The obtained results demonstrated that the proposed controller can provide the required level of clean air in the room.The proposed developed model takes into consideration international Heating,Refrigerating,and air conditioning standards as ASHRAE AND ISO.Based on the findings,we concluded that it is possible to implement a proposed system in homes and offer equivalent indoor air quality with continuous mechanical ventilation without a profuse amount of energy.
基金supported by the National Natural Science Foundation of China[No.52078009]the special fund of Beijing Key Laboratory of Indoor Air Quality Evaluation and Control[No.BZ0344KF20-05]the joint research project of the Wind Engineering Research Center,Tokyo Polytechnic University(MEXT(Japan)Promotion of Distinctive Joint ResearchCenter Program)[No.JPMXP0619217840,No.JURC20202007].
文摘As infectious respiratory diseases are highly transmissible through the air,researchers have improved traditional total volume air distribution systems to reduce infection risk.Multi-vent module-based adaptive ventilation(MAV)is a novel ventilation type that facilitates the switching of inlets and outlets to suit different indoor scenarios without changing ductwork layout.However,little research has evaluated MAV module sizing and air velocity selection,both related to MAV system efficiency in removing contaminants and the corresponding level of protection for occupants in the ventilated room.Therefore,the module-source offset ratio(MSOR)is proposed,based on the MAV module size and its distance from an infected occupant,to inform selection of optimal MAV module parameters.Computational fluid dynamics simulations illustrated contaminant distribution in a two-person MAV equipped office.Discrete phase particles modelled respiratory contaminants from the infected occupant,and contaminant concentration distributions were compared under four MAV air distribution layouts,three air velocities,and three module sizes considered using the MsOR.Results indicate that lower air velocities favour rising contaminant levels,provided the ventilation rate is met.Optimal contaminant discharge can be achieved when the line of outlets is located directly above the infected occupant.Using this parameter to guide MAV system design,85.7% of contaminants may be rendered harmless to the human body within 120 s using the default air vent layout.A more appropriate supply air velocity and air vent layout increases this value to 91.4%.These results are expected to inform the deployment of MAV systems to reduce airborne infection risk.
基金This research was jointly sponsored by Honeywell International Inc.and Syracuse University.
文摘Since the coronavirus disease 2019,the extended time indoors makes people more concerned about indoor air quality,while the increased ventilation in seeks of reducing infection probability has increased the energy usage from heating,ventilation,and air-conditioning systems.In this study,to represent the dynamics of indoor temperature and air quality,a coupled grey-box model is developed.The model is identified and validated using a data-driven approach and real-time measured data of a campus office.To manage building energy usage and indoor air quality,a model predictive control strategy is proposed and developed.The simulation study demonstrated 18.92%energy saving while maintaining good indoor air quality at the testing site.Two nationwide simulation studies assessed the overall energy saving potential and the impact on the infection probability of the proposed strategy in different climate zones.The results showed 20%–40%energy saving in general while maintaining a predetermined indoor air quality setpoint.Although the infection risk is increased due to the reduced ventilation rate,it is still less than the suggested threshold(2%)in general.
文摘Indoor CO2 concentration depends on the number of persons, their metabolic rates, other sources of indoor pollution, ventilation rate and ventilation efficiency. These factors are not considered by the Spanish technical building code since ventilation is set only by a fixed air change rate. This paper aims to explore the possibilities of DCVS （demand controlled ventilation systems） to ensure adequate and sustainable ventilation. It is based on a research project carried out by the University of the Basque Country （EHU-UPV） and Euskadi Public Housing and Soil Join-Stock Company （VISESA）： the living rooms of 90 dwellings were provided with DCVS, where CO2 sensors were used to dynamically control the ventilation rate. Tests were carried out using tracer gas techniques, with results showing the air age to be adequate at every point of the occupied zones and free of stagnant areas, therefore proving the system＇s effectiveness and rapid response, and its energy savings.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2020R1A2C1099611).
文摘High concentrations of indoor CO_(2)pose severe health risks to building occupants.Often,mechanical equipment is used to provide sufficient ventilation as a remedy to high indoor CO_(2)concentrations.However,such equipment consumes large amounts of energy,substantially increasing building energy consumption.In the end,the issue becomes an optimization problem that revolves around maintaining CO_(2)levels below a certain threshold while utilizing the minimum amount of energy possible.To that end,we propose an intelligent approach that consists of a supervised learning-based virtual sensor that interacts with a deep reinforcement learning(DRL)-based control to efficiently control indoor CO_(2)while utilizing the minimum amount of energy possible.The data used to train and test the DRL agent is based on a 3-month field experiment conducted at a kindergarten equipped with a heat recovery ventilator.The results show that,unlike the manual control initially employed at the kindergarten,the DRL agent could always maintain the CO_(2)concentrations below sufficient levels.Furthermore,a 58%reduction in the energy consumption of the ventilator under the DRL control compared to the manual control was estimated.The demonstrated approach illustrates the potential leveraging of Internet of Things and machine learning algorithms to create comfortable and healthy indoor environments with minimal energy requirements.
文摘The present study examined the association between residential indoor remodeling and poor semen quality. Sperm donors aged 18-45 years old were recruited in Shanghai, China. Semen specimens were collected and analyzed. An in-person interview was conducted to obtain information on the history of indoor remodeling and potential confounders. A total of 70 participants with abnormal semen quality （case group） and 68 controls were examined. A total of 20 subjects reported indoor remodeling in the recent 24 months, and among them 17 subjects reported indoor remodeling in the recent 12 months. Compared with participants with no history of indoor remodeling, participants with a history of indoor remodeling in the recent 24 months were more than three times as likely to have poor sperm quality （adjusted odds ratio = 3.8, 95% confidence interval： 1.3-12.0） after controlling for potential confounders. The association was strengthened when the analysis was restricted to those who had indoor remodeling in the recent 12 months. Our findings provide preliminary evidence that indoor remodeling has an adverse effect on semen quality.
