Building management system with clean air and infection reduction features

Patent metadata

Field	Value
Publication number	US-12584648-B2
Application number	US-202318106934-A
Country	US
Kind code	B2
Filing date	Feb 7, 2023
Priority date	Feb 8, 2022
Publication date	Mar 24, 2026
Grant date	Mar 24, 2026

Abstract

Systems and methods for executing an IAQ analysis of a building. One system includes a controller including memory and one or more processors configured to obtain IAQ data from one or more sensors within the building, wherein the IAQ data is associated with at least one of a plurality of environment species, obtain BAS data, identify one or more unknown parameters from the IAQ data and BAS data of two or more of the plurality of environment species, estimate the one or more unknown parameters based on inputting the IAQ data and the BAS data into an optimization model, and wherein the optimization model analyzes predicted concentrations of the plurality of environment species subject to the two or more of the plurality of environment species evolving according to a single-species concentration model, and provide the estimated one or more unknown parameters to one or more predictive models.

First claim

What is claimed is:

1 . A building management system (BMS) for executing an indoor air quality (IAQ) analysis of a building, the BMS comprising:
a controller comprising memory and one or more processors configured to:
obtain IAQ data from one or more sensors within the building, wherein the IAQ data is associated with at least one of a plurality of environment species;
obtain building automation system (BAS) data;
estimate at least one unmeasured time-series airflow parameter for a plurality of single-species concentration models based on enriching two or more of the plurality of environment species according to an occupancy parameter over a time-series based on inputting the IAQ data and the BAS data into an optimization model;
provide the estimated at least one unmeasured time-series airflow parameter to one or more predictive models configured to predict values of a control objective for one or more building zones as a function of control decision variables for HVAC equipment; and
operate the HVAC equipment to affect an environmental condition of the building in accordance with a selected set of optimization results from the one or more predictive models.

2 . The BMS of claim 1 , the one or more processors further configured to:
in response to estimating the at least one unmeasured time-series airflow parameter, modify a control strategy or ventilation schedule for the one or more building zones based on the estimated at least one unmeasured time-series airflow parameter.

3 . The BMS of claim 1 , wherein the estimated at least one an unmeasured time-series airflow parameter is an estimated ventilation rate, and wherein the one or more processors are further configured to:
compare an expected ventilation rate of HVAC system with the estimated ventilation rate; and in response to the expected ventilation rate and the estimated ventilation rate diverging from each other, modify a control strategy for the one or more building zones based on the estimated ventilation rate and a ventilation schedule for the one or more building zones.

4 . The BMS of claim 3 , wherein an occupancy estimate and a particle generation rate are back calculated based on:
calculating a time series particle disturbance based on the estimated ventilation rate and the IAQ data, wherein an increase in a portion of the time series particle disturbance indicates an increase in occupancy of the one or more building zones of the building; and calculating the particle generation rate based on an occupancy dataset comprising occupant ages and occupant metabolic rates.

5 . The BMS of claim 1 , wherein the occupancy parameter is a peak occupancy over a given time period, and wherein the occupancy parameter is scaled according to a scaling factor.

6 . The BMS of claim 1 , wherein the two or more of the plurality of single-species concentration models are an ordinary differential equation model, and wherein at least one value of at least one environment species of the plurality of environment species is updated according to a single-species concentration model using one or more basis function expansions of the at least one unmeasured time-series airflow parameter.

7 . The BMS of claim 6 , wherein a predicted error of the optimization model of the two or more of the plurality of environment species of the plurality of environment species is scaled according to one or more scaling coefficients of the optimization model, wherein the two or more of the plurality of environment species is collected from a sensor of the one or more sensors.

8 . The BMS of claim 1 , wherein the plurality of environment species comprises at least two of a carbon dioxide species, a particulate matter species, a volatile organic compounds species, and a humidity species.

9 . The BMS of claim 1 , wherein the optimization model comprises an objective function, and wherein the objective function is minimized by adjusting the at least one unmeasured time-series airflow parameter according to predicted concentrations matching one or more measured time series concentrations.

10 . The BMS of claim 9 , wherein the at least one unmeasured time-series airflow parameter comprises at least one of a time series ventilation rate, or a time series recirculation rate, and wherein the two or more of the plurality of environment species are associated with one or more of the predicted concentrations.

11 . The BMS of claim 10 , wherein the predicted concentrations are different for each of the plurality of single-species concentration models.

12 . The BMS of claim 1 , the one or more processors further configured to execute the one or more predictive models to:
scale at least one of a first control objective or a second control objective based on the estimated at least one unmeasured time-series airflow parameter and at least one hospitalization metric; execute an optimization process using the one or more predictive models to produce multiple sets of optimization results of the control decision variables and corresponding sets of optimal values of the first control objective and the second control objective for a time period; select one or more of the sets of optimization results; and operate the HVAC equipment to affect the environmental condition of the building in accordance with the values of the control decision variables corresponding to a selected set of the optimization results.

13 . A building management system (BMS) for executing an indoor air quality (IAQ) analysis of a building, the BMS comprising:
a controller comprising memory and one or more processors configured to:
obtain IAQ data from one or more sensors within the building, wherein the IAQ data is associated with an environment species;
obtain building automation system (BAS) data;
identify a ventilation rate and a recirculate rate as a plurality of unknown parameters from the IAQ data and the BAS data the environment species;
estimate the ventilation rate and the recirculate rate based on inputting the IAQ data and the BAS data into an optimization model, and wherein the optimization model analyzes predicted concentrations of the environment species subject to the environment species evolving according to a single-species concentration model;
provide the estimated ventilation rate and the estimated recirculate rate to one or more predictive models configured to predict values of a control objective for one or more building zones as a function of control decision variables for HVAC equipment; and
operate the HVAC equipment to affect an environmental condition of the building in accordance with a selected set of optimization results from the one or more predictive models.

14 . The BMS of claim 13 , wherein the single-species concentration model is an ordinary differential equation model, and wherein the environment species is subject to the environment species evolving according to the single-species concentration model using one or more basis function expansions of the plurality of unknown parameters.

15 . The BMS of claim 13 , wherein the optimization model comprises an objective function, and wherein the objective function is minimized by adjusting the plurality of unknown parameters according to the predicted concentrations matching one or more measured time series concentrations.

16 . The BMS of claim 13 , the one or more processors further configured to:
in response to estimating the ventilation rate and the recirculate rate, modify a control strategy for the one or more building zones based on improving a value of the predicted values.

Assignees

• Tyco Fire & Security Gmbh

Inventors

• Risbeck Michael J
• Lee Young M
• Douglas Jonathan D
• Bazant Martin Z

Classifications

• F24F2110/64

  Airborne particle content · CPC title

• G05B19/042

  using digital processors (G05B19/05 takes precedence) · CPC title

• G05B2219/2614

  HVAC, heating, ventillation, climate control · CPC title

• F24F8/22

  using UV light · CPC title

• F24F2120/10

  Occupancy · CPC title