# US 12,112,392 B2 · Air quality monitors minimization system and methods

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- Number: US 12,112,392 B2
- Document ID: US12112392B2
- Status: Granted
- Kind: B2
- Application number: 18/525,474
- Filed: 2023-11-30
- Published: 2024-08-01
- Granted: 2024-10-08
- Earliest claimed: 2023-02-01
- Assignees: Project Canary, Pbc
- Applicants: Project Canary, PBC
- Inventors: William J. Foiles; Nathan C. Eichenlaub; Kieran J. Lynn; Ray K. Mistry
- Web page: https://patentgrep.com/patent/US12112392B2

## CPC

- G06Q 50/26: Government or public services (business processes related to the transportation industry G06Q50/40)
- G01W 1/02: Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed (G01W1/10 takes precedence)
- G01W 1/10: Devices for predicting weather conditions (computers per se G06; display devices G09)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G01W: METEOROLOGY (radar, sonar, lidar or analogous systems, designed for meteorological use G01S13/95, G01S15/88, G01S17/95)

## Abstract

In one illustrative configuration, a system and method of air quality monitor minimization/optimization is disclosed. The method may include providing at least a first air quality monitor on a site. The first air quality monitor may be configured to generate a first set of attached parameters. The method may further include providing a SCADA system, on the site, configured to generate a set of SCADA data. The SCADA data, the first set of attached parameters may be processed to determine a redundant/sub-optimized air quality monitor, which may be removed. In other illustrative configurations, the system and method may be utilized to locate and/or quantify emissions.

## Claims

### Claim 1

An event aborting method for aborting forthcoming events at a monitored site, the event aborting method comprising:
providing a first air quality monitor comprising:
a first event monitor responsive to events at the monitored site;
detecting at least one event at the monitored site with the first event monitor;
generating a first set of event parameters indicative of occurrence of the at least one event;
transmitting the first set of event parameters to a first server;
providing a supervisory control and data acquisition system (SCADA system) at the monitored site;
sensing a set of SCADA parameters comprising:
a physical factor of a component at the monitored site; and
an operational factor of the component;
transmitting the set of SCADA parameters to the first server;
training an emission-prediction-machine-learning model to create a trained emission-prediction-machine-learning model with:
the first set of event parameters; and
the set of SCADA parameters,
monitoring over a predefined time period, the first set of event parameters and the set of SCADA parameters to create a refined first set of event parameters and a refined set of SCADA parameters;
refining, iteratively and over the predefined time period, the trained emission-prediction-machine-learning model with the refined first set of event parameters and the refined set of SCADA parameters to:
create a refined emission-prediction-machine-learning model, wherein the refined emission-prediction-machine-learning model generates:
a refined predicted emission parameter corresponding to the component;
predicting emissions fugitively associated with the component with the refined predicted emission parameter;
establishing a set of rules limiting the emissions fugitively associated with the component;
determining a forthcoming breach of the set of rules;
determining at least one action abortive of the forthcoming breach of the set of rules; and
implementing the at least one action abortive of the forthcoming breach with the SCADA system.

### Claim 2 (depends on 1)

The event aborting method of claim 1, wherein determining at least one action comprises:
the at least one action comprising:
shutting down the component, or
switching operation to alternate components at the monitored site.

### Claim 3 (depends on 1)

The event aborting method of claim 1, wherein sensing the set of SCADA parameters further comprises:
the physical factor associated with the component, comprising:
an orientation of an access portal thereto;
a position of a valve; and
physical damage.

### Claim 4 (depends on 1)

The event aborting method of claim 1, wherein sensing the set of SCADA parameters further comprises:
the operational factor associated with a fluid processed in the component, comprising:
pressure;
temperature;
flow rate;
density; and
volume.

### Claim 5 (depends on 1)

The event aborting method of claim 1, wherein the refined predicted emission parameter corresponding to the component further comprises:
at least one potential emission sources;
a location of each of the at least one potential emission sources; and
a source flux associated with each of the at least one potential emission sources.

### Claim 6 (depends on 1)

The event aborting method of claim 1, comprising:
providing a second air quality monitor comprising:
a second event monitor responsive to events at the monitored site;
detecting at least one event at the monitored site with the first event monitor;
generating a second set of event parameters indicative of occurrence of the at least one event; and
transmitting the second set of event parameters, and the first set of event parameters from the first air quality monitor to the first server.

### Claim 7 (depends on 6)

The event aborting method of claim 6, further comprising:
training the emission-prediction-machine-learning model with:
the first set of event parameters;
the second set of event parameters; and
the set of SCADA parameters,
monitoring, over a predefined time period, the first set of event parameters, the second set of event parameters, and the set of SCADA parameters to create:
a refined first set of event parameters;
a refined second set of event parameters; and
a refined set of SCADA parameters;
refining, iteratively and over the predefined time period, the trained emission-prediction-machine-learning model with the refined first set of event parameters, the refined second set of event parameters, and the refined set of SCADA parameters to:
create a refined emission-prediction-machine-learning model, wherein the refined emission-prediction-machine-learning model generates:
the refined predicted emission parameter corresponding to the component; and
predicting emissions fugitively associated with the component with the refined predicted emission parameter.

### Claim 8 (depends on 6)

The event aborting method of claim 6, wherein providing the first air quality monitor and providing the second air quality monitor further comprises:
the first event monitor and the second event monitor comprising at least one of:
an aerial monitoring device;
an image-capturing device,
at least one sound sensor,
a communication module; and
an alarm system.

### Claim 9 (depends on 8)

The event aborting method of claim 8, comprising:
sensing, with the first air quality monitor and the second air quality monitor:
a substance concentration of a target substance at the monitored site; and
a set of atmospheric readings;
sensing, with the image-capturing device:
at least one image frame of the monitored site for identifying:
a human activity, or
emissions occurring at the monitored site; and
sensing, with the at least one sound sensor, a sound generated indicative of the events at the monitored site.

### Claim 10 (depends on 9)

The event aborting method of claim 9, wherein sensing the set of atmospheric readings comprises:
sensing at least one atmospheric reading from the set of atmospheric readings comprising:
a barometric pressure,
an air temperature, and
a humidity level.

### Claim 11 (depends on 10)

The event aborting method of claim 10, wherein training the emission-prediction-machine-learning model further comprises:
training the emission-prediction-machine-learning model with the set of atmospheric readings.

### Claim 12

An event aborting system for aborting forthcoming events at a monitored site, the event aborting system comprising:
a first air quality monitor, comprising:
a first event monitor responsive to events at the monitored site, to:
detect at least one event at the monitored site;
generate a first set of event parameters indicative of occurrence of the at least one event; and
transmit the first set of event parameters to a first server;
a supervisory control and data acquisition system (SCADA system) installed at the monitored site to:
sense a set of SCADA parameters comprising:
a physical factor of a component at the monitored site; and
an operational factor of the component; and
transmit the set of SCADA parameters to the first server; and
a logic control unit connected to the first server, to:
train an emission-prediction-machine-learning model to create a trained emission-prediction-machine-learning model with:
the first set of event parameters; and
the set of SCADA parameters,
monitor over a predefined time period, the first set of event parameters and the set of SCADA parameters to create a refined first set of event parameters and a refined set of SCADA parameters; and
refine, iteratively and over the predefined time period, the trained emission-prediction-machine-learning model with the refined first set of event parameters and the refined set of SCADA parameters to:
create a refined emission-prediction-machine-learning model, wherein the refined emission-prediction-machine-learning model generates:
a refined predicted emission parameter corresponding to the component;
predict emissions fugitively associated with the component with the refined predicted emission parameter;
establish a set of rules limiting the emissions fugitively associated to the component;
determine a forthcoming breach of the set of rules;
determine at least one action abortive of the forthcoming breach of the set of rules; and
implement the at least one action abortive of the forthcoming breach with the SCADA system.

### Claim 13 (depends on 12)

The event aborting system of claim 12, wherein the set of SCADA parameters further comprises:
the physical factor associated with the component, comprising:
an orientation of an access portal thereto;
a position of a valve; and
physical damage.

### Claim 14 (depends on 12)

The event aborting system of claim 12, wherein the operational factor is associated with a fluid processed in the component, and comprising at least one of:
pressure;
temperature;
flow rate;
density; and
volume.

### Claim 15 (depends on 12)

The event aborting system of claim 12, wherein the refined predicted emission parameter corresponding to the component further comprises:
at least one potential emission sources;
a location of each of the at least one potential emission sources; and
a source flux associated with each of the at least one potential emission sources.

### Claim 16 (depends on 15)

The event aborting system of claim 15, comprising:
a second air quality monitor comprising:
a second event monitor responsive to events at the monitored site to:
detect at least one event at the monitored site with the first event monitor;
generate a second set of event parameters indicative of occurrence of the at least one event; and
transmit, along with the first set of event parameters from the first air quality monitor, the second set of event parameters to the first server.

### Claim 17 (depends on 16)

The event aborting system of claim 16, wherein the logic control unit is further configured to:
train the emission-prediction-machine-learning model with:
the first set of event parameters;
the second set of event parameters; and
the set of SCADA parameters,
monitor, over a predefined time period, the first set of event parameters, the second set of event parameters, and the set of SCADA parameters to create:
a refined first set of event parameters;
a refined second set of event parameters; and
a refined set of SCADA parameters;
refine, iteratively and over the predefined time period, the trained emission-prediction-machine-learning model with the refined first set of event parameters, the refined second set of event parameters, and the refined set of SCADA parameters to:
create a refined emission-prediction-machine-learning model, wherein the refined emission-prediction-machine-learning model generates:
the refined predicted emission parameter corresponding to the component; and
predict the emissions fugitively associated with the component with the refined predicted emission parameter.

### Claim 18 (depends on 16)

The event aborting system of claim 16, wherein the first event monitor and the second event monitor comprises at least one of:
an aerial monitoring device;
an image-capturing device,
at least one sound sensor,
a communication module; and
an alarm system.

### Claim 19 (depends on 18)

The event aborting system of claim 18, wherein:
the first event monitor and the second event monitor are configured to sense the occurrence of at least one event at the monitored site, the at least one event comprising:
a substance concentration of a target substance at the monitored site;
a set of atmospheric readings;
at least one image frame of the monitored site sensed with the image-capturing device, to identify:
a human activity, or
emissions occurring at the monitored site; and
a sound sensor generated indicative of the events at the monitored site sensed with the at least one sound sensor.

### Claim 20 (depends on 19)

The event aborting system of claim 19, wherein the set of atmospheric readings comprises:
at least one atmospheric reading from the set of atmospheric readings comprising:
a barometric pressure,
an air temperature, and
a humidity level.

### Claim 21 (depends on 20)

The event aborting system of claim 20, wherein the emission-prediction-machine-learning model is further trained with:
the set of atmospheric readings.