Modeling Carbon Dioxide Dispersion Indoors

Khalil, Hoda; Wainer, Gabriel

doi:10.1007/978-3-030-69480-7_23

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…We have calibrated the Cell-DEVS models based on ground truth data collected from sensors installed in the corresponding physical system. 8 We hereby present our findings and extend the work by introducing a complete multistep framework proposal, as shown in Figure 1. The main contribution of this research and the presented framework is to provide a solution to two problems.…”

Section: Introductionsupporting

confidence: 58%

“…5,6 We have shown that Modeling and Simulation (M&S) can solve this dilemma by modeling the indoor spaces and validating the models by showing the resemblance between the models' behavior and the real-life data. 7,8 Cell-DEVS is a combination of Cellular Automata (CA) and Discrete-Event systems Specifications that has several advantages described in section 3. Using the Cell-DEVS formalism, we have defined advanced 3-D models of real-life computer laboratories that consider CO 2 sources (i.e., occupants), CO 2 sinks (i.e., windows and ventilation ports), and the configuration of the closed space (e.g., dimensions).…”

Section: Introductionmentioning

confidence: 99%

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A framework for modeling, generating, simulating, and predicting carbon dioxide dispersion indoors using cell-DEVS and deep learning

Khalil,

Wainer

2023

SIMULATION

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Carbon dioxide concentration in enclosed spaces is an air quality indicator that affects occupants’ well-being. To maintain healthy carbon dioxide levels indoors, enclosed space settings must be adjusted to maximize air quality while minimizing energy consumption. Studying the effect of these settings on carbon dioxide concentration levels is not feasible through physical experimentation and data collection. This problem can be solved by using validated simulation models, generating indoor settings scenarios, simulating those scenarios, and studying results. In previous work, we presented a formal Cellular Discrete Event System Specifications simulation model for studying carbon dioxide dispersion in rooms with various settings. However, designers may need to predict the results of altering large combinations of settings on air quality. Generating and simulating multiple scenarios with different combinations of space settings to test their effect on indoor air quality is time-consuming. In this research, we solve the two problems of the lack of ground truth data and the inefficiency of producing and studying simulation results for many combinations of settings by proposing a novel framework. The framework utilizes a Cellular Discrete Event System Specifications model, simulates different scenarios of enclosed spaces with various settings, and collects simulation results to form a data set to train a deep neural network. Without needing to generate all possible scenarios, the trained deep neural network is used to predict unknown settings of the closed space when other settings are altered. The framework facilitates configuring enclosed spaces to enhance air quality. We illustrate the framework uses through a case study.

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Section: Introductionsupporting

confidence: 58%