Ventilation online monitoring and control system from the perspectives of technology application

Dynamic optimal airflow ventilation can have a great impact on the indoor air distribution and pollutant removal to improve the indoor air quality while saving energy. An online monitoring and control ventilation system has been developed and evaluated using fast prediction models and micro-control. An environmental chamber (1.8 m3) was used for the evaluation to monitor the CO2 dispersion under different air change rates and air speed. Specifically, an artificial neural network model based on a low-dimensional linear ventilation model was introduced and validated to provide environmental control and rapid prediction of pollutant concentration distribution in the indoor environment, which can save computing time and significantly enhance energy saving efficiency up to 16–47%. The validation was carried out by comparison with measurement data of the chamber experiment. An induction system was applied to locate and monitor the personnel in the office due to pollution that are generated by people. A ZigBee wireless module would transmit location information of pollutant source (i.e. CO2 generated by occupants) and to determine the optimal ventilation mode based on ventilation assessment to achieve automatic control of indoor air quality to ensure the wellbeing of occupants while saving energy.

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