IP Library Granted Patent US 11,269,306
Granted Patent B2
US 11,269,306 · App. 16/927,759 · Granted Mar 8, 2022

HVAC system with building infection control

Inventors: Michael J. Risbeck (Madison, WI); Kirk H. Drees (Cedarburg, WI); Jonathan D. Douglas (Mequon, WI)
Assignee: Johnson Controls Tyco IP Holdings LLP
G05B19/042F24F8/10F24F11/47F24F11/52F24F11/64F24F11/70F24F8/22F24F2110/10F24F2110/20F24F2120/20F24F2140/60G05B15/02G05B19/04G05B2219/2614G06F30/20
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Quick Facts
Patent No.
US 11,269,306
App. No.
16/927,759
Granted
Mar 8, 2022
Kind
B2
Abstract

A heating, ventilation, or air conditioning (HVAC) system for one or more building zones includes airside HVAC equipment operable to provide clean air to the one or more building zones and a controller. The controller is configured to obtain a dynamic temperature model and a dynamic infectious quanta model for the one or more building zones, determine an infection probability, and generate control decisions for the airside HVAC equipment using the dynamic temperature model, the dynamic infectious quanta model, and the infection probability.

Claims (48)

1. A heating, ventilation, or air conditioning (HVAC) system for one or more building zones, the HVAC system comprising:

airside HVAC equipment operable to provide clean air to the one or more building zones; and

a controller configured to:

obtain a dynamic temperature model and a dynamic infectious quanta model for the one or more building zones, the dynamic infectious quanta model configured to predict a concentration of infectious quanta;

use the dynamic temperature model and the dynamic infectious quanta model to predict values of temperature and the concentration of infectious quanta in the one or more building zones predicted to result from corresponding sets of control decisions for the airside HVAC equipment, wherein the control decisions are generated by performing an optimization subject to optimization constraints based on the dynamic temperature model and the dynamic infectious quanta model to generate the control decisions as results of the optimization; and

select a set of the control decisions for use in operating the airside HVAC equipment based on the predicted values.

2. The HVAC system of claim 1 , wherein the airside HVAC equipment comprises:

disinfection lighting operable to disinfect the clean air before it is provided to the one or more building zones; and

one or more filters configured to filter the clean air before it is provided to the one or more building zones.

3. The HVAC system of claim 1 , wherein the controller is configured to receive a desired level of disinfection via a user interface and generate a threshold value for the concentration of infectious quanta using the desired level of disinfection.

4. The HVAC system of claim 1 , wherein the controller is configured to obtain a dynamic humidity model for the one or more building zones and use the dynamic humidity model, in addition to the dynamic temperature model and the dynamic infectious quanta model, to generate the control decisions.

5. The HVAC system of claim 1 , wherein the one or more building zones comprise a plurality of building zones and the dynamic temperature model and the dynamic infectious quanta model are either:

individual dynamic models for each of the plurality of building zones; or

aggregate dynamic models for the plurality of building zones based on a weighted volume average of the plurality of zones.

6. A controller for a heating, ventilation, or air conditioning (HVAC) system of a building, the controller comprising:

one or more processors; and

memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

obtaining a dynamic temperature model and a dynamic infectious quanta model for one or more building zones of the building, the dynamic infectious quanta model configured to predict a concentration of infectious quanta;

determining an infection probability;

generating control decisions by performing an optimization subject to optimization constraints based on the dynamic temperature model, the dynamic infectious quanta model, and the infection probability to generate the control decisions as results of the optimization; and

using the control decisions to operate at least one of disinfection lighting, a variable air volume (VAV) unit, or an air handling unit (AHU) of the HVAC system.

7. The controller of claim 6 , wherein the controller is configured to receive a desired level of disinfection via a user interface and generate a threshold value for the infection probability using the desired level of disinfection.

8. The controller of claim 6 , wherein the control signals are generated using a constraint on infectious quanta concentration based on a Wells-Riley Equation.

9. The controller of claim 6 , wherein the operations further comprise:

obtaining a dynamic humidity model for the one or more building zones; and

using the dynamic humidity model, in addition to the dynamic temperature model and the dynamic infectious quanta model, to generate the control decisions.

10. The controller of claim 6 , wherein the one or more building zones comprise a plurality of building zones and the dynamic temperature model and the dynamic infectious quanta model are either:

individual dynamic models for each of the plurality of building zones; or

aggregate dynamic models based on a weighted volume average of the plurality of building zones.

11. The controller of claim 6 , wherein the control decisions indicate an amount of clean air to be provided to the one or more building zones and using the control decisions to operate the VAV unit comprises:

generating both a temperature setpoint and a minimum airflow constraint for the VAV unit, the minimum airflow constraint based the amount of clean air to be provided to the one or more building zones; and

operating the VAV unit to control a temperature of the one or more building zones based on the temperature setpoint, subject to the minimum airflow constraint.

12. A method for controlling building equipment to provide a desired level of disinfection, the method comprising:

obtaining a dynamic temperature model and dynamic infectious quanta model for one or more building zones;

determining a relationship between infection probability and infectious quanta defined by the dynamic infectious quanta model;

generating control decisions by performing an optimization subject to optimization constraints based on the dynamic temperature model, the dynamic infectious quanta model, and the relationship between the infection probability and the infectious quanta to generate the control decisions as results of the optimization; and

using the control decisions to operate the building equipment to provide an amount of clean air to the one or more building zones.

13. The method of claim 12 , wherein the controller is configured to receive the desired level of disinfection via a user interface and generate a threshold value for the infection probability using the desired level of disinfection.

14. The method of claim 12 , further comprising:

obtaining a dynamic humidity model for the one or more building zones; and

using the dynamic humidity model, in addition to the dynamic temperature model and the dynamic infectious quanta model, to generate the control decisions.

15. The method of claim 12 , wherein the one or more building zones comprise a plurality of building zones and the dynamic temperature model and the dynamic infectious quanta model are either:

individual dynamic models for each of the plurality of building zones; or

aggregate dynamic models based on a weighted volume average of the plurality of building zones.

16. The method of claim 12 , wherein the building equipment comprise at least one of disinfection lighting, a filter, an air handling unit (AHU), or a variable air volume (VAV) unit.

17. The method of claim 16 , wherein the control decisions comprise at least one of commands to actuate the disinfection lighting between an on state and an off state, a fresh air intake fraction of the AHU, or an amount of airflow for the VAV unit to provide to the one or more building zones.

18. The HVAC system of claim 1 , wherein the dynamic infectious quanta model predicts the concentration of infectious quanta of the one or more building zones as a function of a set of potential control decisions for the airside HVAC equipment over a future time period.

19. The HVAC system of claim 1 , wherein the dynamic infectious quanta model predicts the concentration of infectious quanta of the one or more building zones as a function of an air-intake fraction of the one or more building zones, an operation of an ultraviolet (UV) light of the one or more building zones, and a filtration of air provided to the one or more building zones.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 26, 2026
From: JOHNSON CONTROLS TYCO IP HOLDINGS LLP
To: TYCO FIRE & SECURITY GMBH
Reel/Frame 074497/0885 →
NUNC PRO TUNC ASSIGNMENT Recorded Feb 4, 2022
From: JOHNSON CONTROLS TECHNOLOGY COMPANY
To: JOHNSON CONTROLS TYCO IP HOLDINGS LLP
Reel/Frame 058959/0764 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 23, 2020
From: RISBECK, MICHAEL J.; DREES, KIRK H.; DOUGLAS, JONATHAN D.
To: JOHNSON CONTROLS TECHNOLOGY COMPANY
Reel/Frame 053863/0664 →
Cited By (9)
US 12,222,124 US 12,264,828 US 12,265,366 US 12,372,934 US 12,393,992 US 12,520,446 US 12,529,490 US 12,595,919 US 12,687,314