US 11,810,216 B1
Air quality monitors minimization system and methods
William J. Foiles, Denver, CO (US); Nathan C. Eichenlaub, Denver, CO (US); Kieran J. Lynn, Denver, CO (US); and Ray K. Mistry, Denver, CO (US)
Assigned to PROJECT CANARY, PBC, Denver, CO (US)
Filed by Project Canary, PBC, Denver, CO (US)
Filed on Jun. 2, 2023, as Appl. No. 18/205,465.
Application 18/205,465 is a continuation of application No. 18/104,746, filed on Feb. 1, 2023, granted, now 11,727,519.
Int. Cl. G06Q 50/26 (2012.01); G01W 1/10 (2006.01)
CPC G06Q 50/26 (2013.01) [G01W 1/10 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An emission quantification method for quantifying an emission from an emission source of a target substance at a site, the emission quantification method comprising:
providing a first air quality monitor comprising:
a first sensor responsive to an emissions event of the target substance; and
a first location at which the first air quality monitor is located on the site;
sensing, by the first air quality monitor, a first set of attached parameters at the first location;
transmitting the first set of attached parameters to a first server;
providing a supervisory control and data acquisition system (SCADA system) at the site, the SCADA system configured to:
supervise a physical factor and an operational factor of at least a first device at the site; and
acquire the physical factor and the operational factor from the first device at the site;
acquiring, with the SCADA system, a set of SCADA data from the first device;
transmitting the set of SCADA data to the first server;
training a emissions quantification machine learning model with:
the first server,
the first set of attached parameters sensed by the first air quality monitor, and
the set of SCADA data,
wherein the emissions quantification machine learning model generates a trained emissions quantification model parameter;
generating an emissions quantification simulation model of a plume of the target substance using the trained emissions quantification model parameter;
monitoring, over a predefined time period and with the emissions quantification simulation model, the set of SCADA data and the first set of attached parameters;
refining, iteratively and over the predefined time period, the emissions quantification simulation model based on monitoring to a refined emissions quantification simulation model;
locating the emission source of the target substance at the site with:
the refined emissions quantification simulation model,
the set of SCADA data, and
the trained emissions quantification model parameter; and
quantifying the emission from the emission source of the target substance at the site based on the refined emissions quantification simulation model, the set of SCADA data and the trained emissions quantification model parameter.