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The concept of maintaining indoor environmental quality comprising building indoor temperature, relative humidity, CO2, and volatile organic compound (VOC) level poses new challenges to the optimal operation of heating, ventilation and air-conditioning (HVAC) systems. While existing case studies demonstrate the energy-saving potentials for efficient HVAC operation, there is a lack of studies quantifying energy savings whilst considering indoor environmental conditions. This study proposes a state-of-the-art modelling and optimization approach to minimize the energy consumption of the HVAC systems without compromising indoor environmental quality. While the primary objective of ensuring optimal operation of HVAC systems is to minimize energy consumption, controlling indoor environmental parameters to remain within the acceptable range imposes excess energy use. These two conflicting objectives constitute a multi-variable constrained optimization problem that has been solved using a particle swarm optimization (PSO) algorithm. Real-time predictive models are developed for the individual indoor environmental parameters and HVAC energy consumption using a Nonlinear Autoregressive Exogenous (NARX) neural network (NN). During model development, models' performance is optimized in terms of complexity, predictive accuracy, and ease of application to a real system. The proposed predictive models are then optimized to provide an optimal control setting for the HVAC systems considering seasonal variations. The results indicate that it is possible to reduce 7.8% of total energy, without negotiating indoor environmental conditions, e.g., air temperature 19.60–28.20°C and relative humidity 30–65% as per ASHRAE Standard 55, and CO2 ≤ 800 ppm and VOC ≤1000 ppm as per AS 1668.2.

BACKGROUND: As of mid-2021, Australia's only nationwide coronavirus disease 2019 (COVID-19) epidemic occurred in the first 6 months of the pandemic. Subsequently, there has been limited transmission in most states and territories. Understanding community spread during the first wave was hampered by initial limitations on testing and surveillance. To characterize the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific antibody seroprevalence generated during this time, we undertook Australia's largest national SARS-CoV-2 serosurvey. METHODS: Between June 19 and August 6, 2020, residual specimens were sampled from people undergoing general pathology testing (all ages), women attending antenatal screening (20-39 years), and blood donors (20-69 years) based on the Australian population's age and geographic distributions. Specimens were tested by Wantai total SARS-CoV-2-antibody assay. Seroprevalence estimates adjusted for test performance were produced. The SARS-CoV-2 antibody-positive specimens were characterized with microneutralization assays. RESULTS: Of 11 317 specimens (5132 general pathology; 2972 antenatal; 3213 blood-donors), 71 were positive for SARS-CoV-2-specific antibodies. Seroprevalence estimates were 0.47% (95% credible interval [CrI], 0.04%-0.89%), 0.25% (CrI, 0.03%-0.54%), and 0.23% (CrI, 0.04%-0.54%), respectively. No seropositive specimens had neutralizing antibodies. CONCLUSIONS: Australia's seroprevalence was extremely low (<0.5%) after the only national COVID-19 wave thus far. These data and the subsequent limited community transmission highlight the population's naivety to SARS-CoV-2 and the urgency of increasing vaccine-derived protection.

The concept of maintaining indoor environmental quality comprising building indoor temperature, relative humidity, CO2, and volatile organic compound (VOC) level poses new challenges to the optimal operation of heating, ventilation and air-conditioning (HVAC) systems. While existing case studies demonstrate the energy-saving potentials for efficient HVAC operation, there is a lack of studies quantifying energy savings whilst considering indoor environmental conditions. This study proposes a state-of-the-art modelling and optimization approach to minimize the energy consumption of the HVAC systems without compromising indoor environmental quality. While the primary objective of ensuring optimal operation of HVAC systems is to minimize energy consumption, controlling indoor environmental parameters to remain within the acceptable range imposes excess energy use. These two conflicting objectives constitute a multi-variable constrained optimization problem that has been solved using a particle swarm optimization (PSO) algorithm. Real-time predictive models are developed for the individual indoor environmental parameters and HVAC energy consumption using a Nonlinear Autoregressive Exogenous (NARX) neural network (NN). During model development, models' performance is optimized in terms of complexity, predictive accuracy, and ease of application to a real system. The proposed predictive models are then optimized to provide an optimal control setting for the HVAC systems considering seasonal variations. The results indicate that it is possible to reduce 7.8% of total energy, without negotiating indoor environmental conditions, e.g., air temperature 19.60–28.20°C and relative humidity 30–65% as per ASHRAE Standard 55, and CO2 ≤ 800 ppm and VOC ≤1000 ppm as per AS 1668.2.

The ventilation component is one of the critical parts in an heating, ventilation and air-conditioning systems (HVAC) system and holds prime importance in ensuring operational efficacy and efficiency in terms of energy consumption and indoor environmental conditions. A true demand-based ventilation system with an economy air cycle not only offers reduced amounts of energy consumption but also can take part in a healthy indoor environment. This study provides true insight into the performance of a typical variable-air-volume air handling unit system under different operating conditions through a rigorous case study analysis in a real commercial building. To highlight pragmatic solutions to existing complexity within demand control ventilation strategies of the case building this study sets up real-time dynamic scenarios and investigates those scenarios in the context of indoor environment where both carbon dioxide (CO2) and volatile organic compound (VOC) concentrations are linked. To predict and monitor the indoor air CO2 concentration for the stated real time scenarios this study uses the mass balance equation for CO2 concentration. This study demonstrates that integrating the CO2 mass balance equation along with CO2 sensor data into the building management control system not only assists in reducing energy consumption but also takes part in a healthy indoor environment. This study suggests including the mass balance equation for CO2 concentration into the Australian ventilation standard AS 1668.2.