# US 2024/0202902 A1 · Systems, apparatuses, methods, and computer program products for determining a flaring efficiency estimate based on captured image data and operations data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2024/0202902 A1 - Document ID: US20240202902A1 - Status: Published application - Kind: A1 - Application number: 18/183,629 - Filed: 2023-03-14 - Published: 2024-06-20 - Earliest claimed: 2022-12-20 - Assignees: Honeywell International Inc. - Applicants: Honeywell International Inc. - Inventors: Shahrul Azman Bin Zainal Abidin; Madhukar Madhavamurthy Gundappa; Ramesh Murugan Natarajan - Web page: https://patentgrep.com/patent/US20240202902A1 ## CPC - G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) - G06T 7/0004: {Industrial image inspection} - G06T 7/50: Depth or shape recovery - G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80) - G06T 7/90: Determination of colour characteristics - G06V 10/70: using pattern recognition or machine learning (optical pattern recognition or electronic computations therefor G06V10/88) - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - F23G 7/085: {in stacks} - F23N 2223/40: Simulation - F23N 2229/20: Camera viewing - G06T 2207/20081: Training; Learning - G06T 2207/30108: Industrial image inspection - G06V 2201/06: Recognition of objects for industrial automation - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - F23G: CREMATION FURNACES; CONSUMING WASTE PRODUCTS BY COMBUSTION - F23N: REGULATING OR CONTROLLING COMBUSTION (control devices specially adapted for combustion apparatus in which combustion takes place in a fluidised bed of fuel or other particles F23C10/28; condition responsive controls for regulating combustion in domestic stoves with open fires for solid fuel F24B1/187) ## Abstract Systems, apparatuses, methods, and computer program products for determining flaring efficiency based on captured image data and operations data are provided herein. For example, a computer-implemented method for determining flaring efficiency may include receiving operations data representing operations of a plant. The computer-implemented method may include generating a simulation model of the plant based at least in part on the operations data. The computer-implemented method may include applying the simulation model of the plant to determine a first flaring efficiency estimate. The computer-implemented method may include receiving captured image data of a flare flame associated with the plant. The computer-implemented method may include analyzing the captured image data to determine a second flaring efficiency estimate. The computer-implemented method may include generating a flaring efficiency estimate based on at least one of the first flaring efficiency estimate and the second flaring efficiency estimate. ## Claims ### Claim 1 A computer-implemented method for determining a flaring efficiency estimate, the computer-implemented method comprising: receiving operations data representing operations of a plant; generating a simulation model of the plant based at least in part on the operations data; applying the simulation model of the plant to determine a first flaring efficiency estimate; receiving captured image data of a flare flame associated with the plant; analyzing the captured image data to determine a second flaring efficiency estimate; and determining the flaring efficiency estimate based on at least one of the first flaring efficiency estimate and the second flaring efficiency estimate. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the operations data comprises one or more of plant component identifiers, plant production capacity data, plant efficiency data, plant process data, flow rate data, temperature data, pressure data, volume data, venting data, oxygen input data, density data, material composition data, and weather data. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the operations data representing operations of the plant is received in real-time. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein the simulation model comprises one or more well components, fracking components, heater components, pump components, storage tank components, flash venting components, compressor components, cooler components, sensor components, and flare components. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the flaring efficiency estimate is an average of the first flaring efficiency estimate and the second flaring efficiency estimate. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the flaring efficiency estimate is equal to the first flaring efficiency estimate. ### Claim 7 (depends on 6) The computer-implemented method of claim 6, wherein the flaring efficiency estimate is equal to the first flaring efficiency estimate when the operations data indicates that the second flaring efficiency estimate is inaccurate. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein analyzing the captured image data comprises: applying an image recognition model to analyze the captured image data to determine one or more of a flame tilt, a flame color, and a flame shape. ### Claim 9 (depends on 8) The computer-implemented method of claim 8, wherein the second flaring efficiency estimate is determined based at least in part on one or more of the flame tilt, the flame color, and the flame shape. ### Claim 10 (depends on 8) The computer-implemented method of claim 8, wherein the image recognition model is a machine learning model trained on a historical captured image dataset and a historical operations data dataset. ### Claim 11 An apparatus for determining a flaring efficiency estimate, the apparatus comprising at least one processor and at least one memory coupled to the at least one processor, wherein the at least one processor is configured to: receive operations data representing operations of a plant; generate a simulation model of the plant based at least in part on the operations data; apply the simulation model of the plant to determine a first flaring efficiency estimate; receive captured image data of a flare flame associated with the plant; analyze the captured image data to determine a second flaring efficiency estimate; and determine the flaring efficiency estimate based on at least one of the first flaring efficiency estimate and the second flaring efficiency estimate. ### Claim 12 (depends on 11) The apparatus of claim 11, wherein the operations data comprises one or more of plant component identifiers, plant production capacity data, plant efficiency data, plant process data, flow rate data, temperature data, pressure data, volume data, venting data, oxygen input data, density data, material composition data, and weather data. ### Claim 13 (depends on 11) The apparatus of claim 11, wherein the simulation model comprises one or more well components, fracking components, heater components, pump components, storage tank components, flash venting components, compressor components, cooler components, sensor components, and flare components. ### Claim 14 (depends on 11) The apparatus of claim 11, wherein the flaring efficiency estimate is an average of the first flaring efficiency estimate and the second flaring efficiency estimate. ### Claim 15 (depends on 11) The apparatus of claim 11, wherein the flaring efficiency estimate is equal to the first flaring efficiency estimate. ### Claim 16 (depends on 15) The apparatus of claim 15, wherein the flaring efficiency estimate is equal to the first flaring efficiency estimate when the operations data indicates that the second flaring efficiency estimate is inaccurate. ### Claim 17 (depends on 11) The apparatus of claim 11, wherein analyzing the captured image data comprises the at least one processor being configured to: apply an image recognition model to analyze the captured image data to determine one or more of a flame tilt, a flame color, and a flame shape. ### Claim 18 (depends on 17) The apparatus of claim 17, wherein the second flaring efficiency estimate is determined based at least in part on one or more of the flame tilt, the flame color, and the flame shape. ### Claim 19 (depends on 17) The apparatus of claim 17, wherein the image recognition model is a machine learning model trained on a historical captured image dataset and a historical operations data dataset. ### Claim 20 A non-transitory computer-readable storage medium for determining a flaring efficiency estimate comprising computer program code for execution by one or more processors of a device, the computer program code configured to, when executed by the one or more processors, cause the device to: receive operations data representing operations of a plant; generate a simulation model of the plant based at least in part on the operations data; apply the simulation model of the plant to determine a first flaring efficiency estimate; receive captured image data of a flare flame associated with the plant; analyze the captured image data to determine a second flaring efficiency estimate; and determine the flaring efficiency estimate based on at least one of the first flaring efficiency estimate and the second flaring efficiency estimate.