Autonomous burner
AssigneeSchlumberger Technology Corporation
InventorsHugues Trifol, Hakim Arabi
Life of this patent
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Abstract
Methods of autonomously controlling hydrocarbon burners described herein include capturing an image, for example from a video feed, of an operating burner; processing the image to form an image data set; capturing sensor data of the operating burner; forming a data set comprising the sensor data and the image data set; providing the data set to a machine learning model system; outputting, from the machine learning model system, an air control parameter of the burner; and applying the air control parameter to the burner.
Claims20
A method, comprising:
- capturing an image of an operating burner;
- processing the image to form an image data set;
- capturing sensor data of the operating burner;
- forming a data set comprising the sensor data and the image data set;
- providing the data set to a machine learning model system;
- outputting, from the machine learning model system, an air control parameter of the burner;
- and
- applying the air control parameter to the burner.
The method of claim 8, wherein the parameter is one of overall brightness across the spectrum, overall brightness at one or more selected wavelengths, and brightness variation at one or more selected wavelengths.
A burner control system, comprising:
- an imaging system for capturing burner images as image data;
- an image processing system comprising a digital processor with non-transitory medium containing instructions to perform a classification process on the image data representing images of the burner captured by the imaging system to produce classification data;
- and
- a control system comprising a digital processor with non-transitory medium containing instructions to compute an air control action based on the classification data and a neural network burner model.
A method of controlling a burner, comprising:
- capturing a broad-spectrum image of an operating burner;
- processing the image to form an image data set including spectral content of each pixel of the image;
- capturing sensor data of the operating burner;
- forming a data set comprising the sensor data and the image data set;
- providing the data set to a machine learning model system;
- outputting, from the machine learning model system, an air control parameter of the burner;
- applying the air control parameter to the burner;
- comparing the image data to a standard to define a score;
- and
- adjusting the machine learning model based on the score.
Dependencies are inferred from the claim text.