ABSTRACT
During the SARS-CoV-2 (COVID-19) pandemic, governments around the world have formulated policies requiring ventilation systems to operate at a higher outdoor fresh air flow rate for a sufficient time, which has led to a sharp increase in building energy consumption. Therefore, it is necessary to identify an energy-efficient ventilation strategy to reduce the risk of infection. In this study, we developed an occupant-number-based model predictive control (OBMPC) algorithm for building ventilation systems. First, we collected the occupancy and Heating, ventilation, and air conditioning system (HVAC) data from March to July 2021. Then, four different models (Auto regression moving average-based multilayer perceptron (ARMA_MLP), Recurrent neural networks (RNN), Long short-term memory networks (LSTM), and Nonhomogeneous Markov with change points detection (NH_Markov)) were used to predict the number of room occupants from 15 min to 24 h ahead with an interval output. We found that each model could predict the number of occupants with 85 % accuracy using a one-person offset. The accuracy of 15 min of the ahead prediction could reach 95 % with a one-person offset, but none of them could track abrupt changes. The occupancy prediction results were used to calculate the ventilation demand using the Wells-Riley equation, and the upper bound can maintain an infection risk lower than 2 % for 93 % of the day. This OBMPC model could reduce the coil load by 52.44 % and shift the peak load by 3 h up to 5 kW compared with 24 × 7 h full outdoor air (OA) system when people wear masks in the space. The occupancy prediction uncertainty could cause a 9 % to 26 % difference in demand ventilation, a 0.3 °C to 2.4 °C difference in zone temperature, a 28.5 % to 44.5 % difference in outdoor airflow rate, and a 10.7 % to 28.2 % difference in coil load.
ABSTRACT
The infection and spread of pathogens (e.g., COVID-19) pose an enormous threat to the safety of human beings and animals all over the world. The rapid and accurate monitoring and determination of pathogens are of great significance to clinical diagnosis, food safety and environmental evaluation. In recent years, with the evolution of nanotechnology, nano-sized graphene and graphene derivatives have been frequently introduced into the construction of biosensors due to their unique physicochemical properties and biocompatibility. The combination of biomolecules with specific recognition capabilities and graphene materials provides a promising strategy to construct more stable and sensitive biosensors for the detection of pathogens. This review tracks the development of graphene biosensors for the detection of bacterial and viral pathogens, mainly including the preparation of graphene biosensors and their working mechanism. The challenges involved in this field have been discussed, and the perspective for further development has been put forward, aiming to promote the development of pathogens sensing and the contribution to epidemic prevention.