US 12,392,488 B2
Method of assessment of the quality of the burn of the gases in the flare and adjustment to the vapor flow rate in a continuous and constant way
Andre Seichi Ribeiro Kuramoto, São José dos Campos (BR); André Davys Carvalho Melo De Oliveira, Rio de Janeiro (BR); Pedro Henrique Lopes Torres, Rio de Janeiro (BR); William Paulo Ducca Fernandes, Rio de Janeiro (BR); Hélio Côrtes Vieira Lopes, Rio de Janeiro (BR); Wolfgang Kosteke Schwaner, Araucária (BR); Bruno Itagyba Paravidino, Rio de Janeiro (BR); Cristiane Salgado Pereira, Rio de Janeiro (BR); Patrick Nigri Happ, Rio de Janeiro (BR); and Sidney Comandulli, Araucária (BR)
Assigned to Petróleo Brasileiro S.A.—Petrobras, Rio de Janeiro (BR); and Faculdades Católicas, Rio de Janeiro (BR)
Filed by Petróleo Brasileiro S.A.—Petrobras, Rio de Janeiro (BR); and Faculdades Católicas, Rio de Janeiro (BR)
Filed on Aug. 18, 2022, as Appl. No. 17/890,539.
Claims priority of application No. 10 2021 020663 2 (BR), filed on Oct. 14, 2021.
Prior Publication US 2023/0120460 A1, Apr. 20, 2023
Int. Cl. F23N 5/26 (2006.01); F23G 7/08 (2006.01); F23L 7/00 (2006.01); G06V 20/52 (2022.01); G06V 20/60 (2022.01)
CPC F23N 5/265 (2013.01) [F23G 7/08 (2013.01); F23L 7/005 (2013.01); G06V 20/52 (2022.01); G06V 20/60 (2022.01); F23N 2229/20 (2020.01); F23N 2237/22 (2020.01)] 15 Claims
OG exemplary drawing
 
1. A method of assessment of the quality of the burn of gases in a flare system and adjustment to water vapor flow rate in a continuous and constant way, the method comprising:
a. obtaining a flare flame image set comprising images;
b. using computer vision techniques, quantifying a flare flame height of each of the images;
c. using a deep learning model, classifying the images into four states: flame with excess vapor, optimized flame, flame with soot and images with insufficient information;
d. combining the quantification of step “b” with the results of step “c” to classify the image set, obtained in step “a”, in one of the four states of the flare flame;
e. using retraining of an image classification model;
f. adjusting the water vapor flow rate in a continuous, automated and integrated way to the water vapor injection control system in the flare system, by means of the information obtained in steps “a”, “b”, “c” and “e”;
g. employing the following components: flare, camera, image stream manager, edge computer, data historian, alert manager, information visualization panels, distributed digital control system (DDCS), and cloud storage and computing.