Control of indoor CO2 concentration based on a process model

Škrjanc, Igor; Šubic, Barbara

doi:10.1016/j.autcon.2014.02.012

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…One instrument in each office was placed away from the breathing zone, windows, and doors at a height of six feet from ground level to get representative data of the room [ 6 ]. Relaying on altitude and microclimatic conditions, outdoor CO 2 concentration range between 400 and 450 ppm [ 43 ]. However, in the present study, outdoor CO 2 levels were assumed as 400 ppm [ 19 , 38 , 44 ].…”

Section: Methodsmentioning

confidence: 99%

Comparative analysis of indoor air quality in offices with different ventilation mechanisms and simulation of ventilation process utilizing system dynamics tool

Asif¹,

Zeeshan²

2023

Journal of Building Engineering

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Section: Methodsmentioning

confidence: 99%

Comparative analysis of indoor air quality in offices with different ventilation mechanisms and simulation of ventilation process utilizing system dynamics tool

Asif¹,

Zeeshan²

2023

Journal of Building Engineering

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“…Toub et al [27] proposed a nonlinear model predictive controller to control MicroCSP and building HVAC systems to optimize energy usage and reduce costs. Skrjanc et al [28] designed an internal model controller (IMC) with an internal loop to control the CO 2 level in an indoor environment. Cho et al [29] developed an integrated model based on an artificial neural network to predict the indoor concentrations of CO 2 , PM 10 , and PM 2.5 .…”

Section: Introductionmentioning

confidence: 99%

“…1. Classification of the control methods in HVAC systems including some of the prior studies[20][21][22][23][24][25][26][27][28][29][30].…”

mentioning

confidence: 99%

Energy Efficiency and Optimization Strategies in a Building to Minimize Airborne Infection Risks

Samadi

Shahbakhti

2023

Energies

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Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in either increasing or decreasing the risk of airborne disease transmission. High ventilation, for instance, is a common method used to control and reduce the infection risk of airborne diseases such as COVID-19. On the other hand, high ventilation will increase energy consumption and cost. This paper proposes an optimal HVAC controller to assess the trade-off between energy consumption and indoor infection risk of COVID-19. To achieve this goal, a nonlinear model predictive controller (NMPC) is designed to control the HVAC systems of a university building to minimize the risk of COVID-19 transmission while reducing building energy consumption. The NMPC controller uses dynamic models to predict future outputs while meeting system constraints. To this end, a set of dynamic physics-based models are created to capture heat transfer and conservation of mass, which are used in the NMPC controller. Then, the developed models are experimentally validated by conducting experiments in the ETLC building at the University of Alberta, Canada. A classroom in the building is equipped with a number of sensors to measure indoor and outdoor environmental parameters such as temperature, relative humidity, and CO2 concentration. The validation results show that the model can predict room temperature and CO2 concentration by 0.8%, and 2.4% mean absolute average errors, respectively. Based on the validated models, the NMPC controller is designed to calculate the optimal airflow and supply air temperature for every 15 min. The results for real case studies show that the NMPC controller can reduce the infection risk of COVID-19 transmission below 1% while reducing energy consumption by 55% when compared to the existing building controller.

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“…During a building's operation, one of the aims is to ensure good indoor air quality: i.e., a healthy and comfortable indoor environment [1]. This goal should be attained using low-energy solutions [2]. In this context, knowledge of the ventilation air change rate is crucial to efficiently monitor buildings' indoor air quality [3].…”

Section: Introductionmentioning

confidence: 99%

Estimation of a room ventilation air change rate using a stochastic grey-box modelling approach

et al. 2018

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Control of indoor CO2 concentration based on a process model

Cited by 13 publications

References 18 publications

Comparative analysis of indoor air quality in offices with different ventilation mechanisms and simulation of ventilation process utilizing system dynamics tool

Comparative analysis of indoor air quality in offices with different ventilation mechanisms and simulation of ventilation process utilizing system dynamics tool

Energy Efficiency and Optimization Strategies in a Building to Minimize Airborne Infection Risks

Estimation of a room ventilation air change rate using a stochastic grey-box modelling approach

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