Hybrid Ventilation System and Soft-Sensors for Maintaining Indoor Air Quality and Thermal Comfort in Buildings

Abstract

Maintaining both indoor air quality (IAQ) and thermal comfort in buildings along with optimized energy consumption is a challenging problem. This investigation presents a novel design for hybrid ventilation system enabled by predictive control and soft-sensors to achieve both IAQ and thermal comfort by combining predictive control with demand controlled ventilation (DCV). First, we show that the problem of maintaining IAQ, thermal comfort and optimal energy is a multi-objective optimization problem with competing objectives, and a predictive control approach is required to smartly control the system. This leads to many implementation challenges which are addressed by designing a hybrid ventilation scheme supported by predictive control and soft-sensors. The main idea of the hybrid ventilation system is to achieve thermal comfort by varying the ON/OFF times of the air conditioners to maintain the temperature within user-defined bands using a predictive control and IAQ is maintained using Healthbox 3.0, a DCV device. Furthermore, this study also designs soft-sensors by combining the Internet of Things (IoT)-based sensors with deep-learning tools. The hardware realization of the control and IoT prototype is also discussed. The proposed novel hybrid ventilation system and the soft-sensors are demonstrated in a real research laboratory, i.e., Center for Research in Automatic Control Engineering (C-RACE) located at Kalasalingam University, India. Our results show the perceived benefits of hybrid ventilation, predictive control, and soft-sensors.

Nonequilibrium sensing of volatile compounds using active and passive analyte delivery

“…Many industries that require a simple, portable, low-cost sensor could benefit from our approaches. Example applications include identifying hazards on site, monitoring air quality, ensuring healthy building spaces ( 62 ), and point-of-care breath analyzers for rapid diagnosis of diseases for which characteristic volatiles have been identified ( 63 , 64 ). In fact, the need to acquire real-time information about the air we breathe, both indoors and outdoors, has been brought into the spotlight by recent events such as wildfires, the SARS-CoV-2 pandemic, large spills, and growing concerns as well as regulations regarding air pollution ( 65 ).…”

“…In fact, the need to acquire real-time information about the air we breathe, both indoors and outdoors, has been brought into the spotlight by recent events such as wildfires, the SARS-CoV-2 pandemic, large spills, and growing concerns as well as regulations regarding air pollution ( 65 ). It has thus become abundantly clear that sensing-based ventilation and cleaning will be the future to ensure healthier building spaces ( 62 ), targeting both acute events as well as persistent issues such as presence of mold and other pollutants. Using sniffing for sensing is an exciting direction, opening doors to employing sniffing-related behaviors seen in nature, such as adapting sniffing frequencies to detect specific odorant types, for sensing capabilities and applications.…”

Soft-Sensor Modeling of Temperature Variation in a Room under Cooling Conditions

“…Recently, some reports have focused on the applications of soft sensors in heating, ventilation, and air-conditioning systems. Vadamalraj et al [14] designed a hybrid ventilation system implemented by soft sensors and predictive control. The cooling load and indoor air quality were predicted using soft sensors based on neural networks.…”

A review of hybrid ventilation on humid tropics climate

“…Demand control ventilation (DCV) is a mechanical ventilation system that is controlled automatically based on sensors. It can provide savings of approximately 21.4% [38].…”