Method and device for estimating a combustion efficiency value during flaring

Abstract

The invention relates to a computing device and method for estimating ( 100 ) a combustion efficiency value during flaring, over a time period, said method comprising the following steps: —Acquiring ( 140 ) a video stream of the flare flame ( 61 ) over the time period; —Segmenting ( 150 ) the video stream in several video segments, each video segments being associated with a video segment duration; —Analyzing ( 160 ) the video segments, using a correlation model, so as to classify each video segments in at least one flame state category; and—Computing ( 170 ) the combustion efficiency value, said computing ( 170 ) step using the video segment durations and a plurality of unburned reduction index values, each of said unburned reduction index values being specific to one of the flame state categories, specific to the industrial plant and calculated using computational fluid dynamics.

1 . A method for estimating a combustion efficiency value during flaring by a flare flame in an industrial plant, over a time period, said method being implemented by one or more processors and comprising the following steps:
 Acquiring a video stream of the flare flame over the time period;   Segmenting the video stream in several video segments, each video segment being associated with a video segment duration;   Analyzing the video segments, using a correlation model, so as to classify each video segment in at least one flame state category; and   Computing the combustion efficiency value accordingly: said computing step using the video segment durations and a plurality of unburned reduction index values, each of said unburned reduction index values being specific to one of the flame state categories, specific to the industrial plant and calculated using computational fluid dynamics.   
     
     
         2 . The method according to  claim 1 , wherein the combustion efficiency value is computed after less than five minutes after the acquisition of the video stream. 
     
     
         3 . The method according to  claim 1 , wherein the unburned reduction index values are correlated to a quantity of unburned gas released. 
     
     
         4 . The method according to  claim 1 , further comprising a step of initial calibration, said initial calibration comprising the following steps:
 Acquisition of video streams of the flare flame of the industrial plant over several flame behaviors;   Segmentation of the video streams in a plurality of video segments each associated with a flame state category; and   Computation of an unburned reduction index value for each of at least four flame state categories, using computational fluid dynamics.   
     
     
         5 . The method according to  claim 1 , wherein it comprises a step of initial calibration, said initial calibration comprising the following steps:
 Acquisition of video streams of the flare flame of the industrial plant over several flame behaviors;   Acquisition of a weather parameter value, a parameter value of the flare flame, a parameter value of the flare structure and/or a flare processing parameter value;   Segmentation of the video stream in a plurality of video segments each associated with a flame state category; and   Computation of an unburned reduction index value for each of at least four flame state categories, using computational fluid dynamics with the weather parameter value, the parameter value of the flare flame, the parameter value of the flare structure and/or the flare processing parameter value.   
     
     
         6 . The method according to  claim 4 , wherein the step of initial calibration further comprises, after the segmentation, a selection of the most representative flame state categories, each of said representative flame state categories being associated with a representative weather parameter value and/or a representative flare processing parameter value, said representative weather parameter value and/or representative flare processing parameter value being calculated from the acquired weather parameter and/or flare processing parameter values; and wherein the computation of the unburned reduction index values is done for the most representative flame state categories using computational fluid dynamics with the representative weather parameter value and/or the representative flare processing parameter value. 
     
     
         7 . The method according to  claim 1 , further comprising a step of calibrating the correlation model, said correlation model being configured to classify the segments of the video stream of the flare flame according to a reduced number of flame states based on the flame behavior as monitored by a camera, preferably, the correlation model being configured to classify the segments of the video stream according parameter value(s) of the flare flame. 
     
     
         8 . The method according to  claim 7 , wherein the correlation model is configured to classify the segments of the video stream also according to flare processing parameter value(s), parameter value(s) of the flare structure and/or weather parameter value(s). 
     
     
         9 . The method according to  claim 1 , further comprising a step of recalibration, said recalibration comprising measuring of unburned gases released by the industrial plant by a dedicated mean, and a comparison of the measured unburned gases released and a computed quantity of unburned gases released from the computed combustion efficiency value. 
     
     
         10 . The method according to  claim 9 , wherein, when there is an inconsistency between the measured unburned gases released and the computed quantity of unburned gases released, the step of recalibration further comprises a step of modifying the unburned reduction index values, preferably according to said inconsistency. 
     
     
         11 . The method according to  claim 1 , wherein the segmentation of the video stream is based on a predetermined duration value or wherein the segmentation of the video stream is based on a duration value which is calculated according to flare processing parameter value(s), parameter value(s) of the flare flame, parameter value(s) of the flare structure and/or weather parameter value(s). 
     
     
         12 . The method according to  claim 1 , wherein the segmentation of the video stream uses at least one non-image-based value, such as a weather parameter value, a flare processing parameter value and/or a parameter value of the flare structure. 
     
     
         13 . The method according to  claim 1 , wherein the correlation model comprises a supervised, unsupervised or reinforcement-based machine learning model, such as a convolutional neural network. 
     
     
         14 . The method according to  claim 1 , wherein the correlation model is configured to compute a value of at least one parameter of the flare flame such as: size of the flame, flame to smoke ratio, temperature of the flare, colorimetry, a soot built up, a flame detachment, smoke of the flare, an angle of inclination of the flame, a flame opening angle, a length of visible plume, coloration of flame, coloration of smoke and/or a soot content. 
     
     
         15 . The method according to  claim 1 , wherein the step of analyzing the video segments comprises a combination of computing a value of at least one parameter of the flare flame such as: a size of the flame, a flame to smoke ratio, a temperature of the flare, colorimetry, a soot built up, a flame detachment, a smoke of the flare, an angle of inclination of the flame, a flame opening angle, a length of visible plume; and using a machine learning model. 
     
     
         16 . The method according to  claim 1 , wherein the step of segmenting the video stream comprises using the correlation model. 
     
     
         17 . The method according to  claim 1 , wherein the step of segmenting and the step of analyzing the video segments, are performed simultaneously and each comprises use of the correlation model. 
     
     
         18 . The method according to  claim 1 , wherein the step of analyzing the video segments further comprises a sub step of differentiating flame and smoke from environment. 
     
     
         19 . The method according to  claim 1 , further comprising use of at most one hundred flames state categories, fifty, preferably at most forty flames state categories, more preferably at most thirty flames state categories and even more preferably at most twenty flames state categories. 
     
     
         20 . The method according to  claim 1 , further comprising use of at least four flames state categories, preferably at least five flames state categories, more preferably at least ten flames state categories and even more preferably at least fifteen flames state categories. 
     
     
         21 . The method according to  claim 1 , wherein, when a video segment is classified in several flame state categories, preferably each of the flame state categories associated with this video segment is associated with a percentage, a sum of the percentages being equal to 100%; said percentage being preferably a percentage of duration of each flame state category in the video segment. 
     
     
         22 . The method according to  claim 1 , wherein the unburned reduction index values have been calculated through computational fluid dynamics, preferably reactive computational fluid dynamics large eddy simulation. 
     
     
         23 . The method according to  claim 1 , wherein during computing the combustion efficiency value, only a part of the video segments are associated with one or several unburned reduction index values. 
     
     
         24 . The method according to  claim 1 , wherein computing the combustion efficiency value further comprises use of at least one non-image-based value, said non-image-based value being selected among a weather parameter value, a plant value, or a current process condition information. 
     
     
         25 . The method according to  claim 1 , wherein the unburned reduction indexes have been modified according to a measured quantity of unburned gases released obtained through a sampling campaign, for example with drones, of the unburned gases quantities released into the atmosphere. 
     
     
         26 . The method according to  claim 1 , further comprising obtaining an audio stream of the flare flame, in particular the audio stream comprise vibration recording of a flare burner generating the flare flame, corresponding to the video stream and wherein computing the combustion efficiency value further uses the obtained audio stream. 
     
     
         27 . The method according to  claim 1 , wherein the flame state categories are associated with an unburned reduction index value based on a quantity of unburned gases previously calculated for a similar flame state category with a computational fluid dynamics. 
     
     
         28 . The method according to  claim 1 , further comprising a step of capturing said video stream of the flare emitted by a flare burner of the industrial plant with at least one camera. 
     
     
         29 . The method according to  claim 1 , further comprising a step of storing the video stream of the flare flame, with the computed combustion efficiency value along with a date and time stamp. 
     
     
         30 . Method of operating a flare burner comprising a modification of at least one flare processing parameter value based on the computed combustion efficiency value obtained according to the method of  claim 1 . 
     
     
         31 . The method of operating a flare burner according to  claim 30 , wherein the modification of at least one flare processing parameter value is also based one at least one weather parameter value. 
     
     
         32 . A computer program product having computer-executable instructions which, when carried out on a computer system, perform the method according to  claim 31 . 
     
     
         33 . A computing device for estimating a combustion efficiency value during flaring by a flare flame in an industrial plant, over a time period, said computing device comprising:
 a memory component configured to store a correlation model configured to classify each video segments in at least one flame state category;   a communication interface configured to acquire a video stream of the flare flame;   one or more processors configured to:
 Acquire a video stream of the flare flame over the time period; 
 Segment the video stream into several video segments, each video segment being associated with a video segment duration; 
 Analyze the video segments, using a correlation model, so as to classify each video segment in at least one flame state category; and 
 Compute the combustion efficiency value, said computing step being based on the video segment durations and a plurality of unburned reduction indexes, each of said unburned reduction indexes being specific to one of the flame state categories, specific to the industrial plant and calculated using computational fluid dynamics. 
   
     
     
         34 . A computing system comprising the computing device according to  claim 33  and at least one image capturing device arranged to generate a video stream of the flare flame in the industrial plant. 
     
     
         35 . The computing system according to  claim 34 , wherein the image capturing device is oriented toward at least one flare burner and is configured for obtaining said video stream of the flare flame emitted by the industrial plant, preferably said image capturing device being a digital video, a high-definition digital video, or a 3D video. 
     
     
         36 . The computing system according to  claim 34 , wherein the at least one image capturing device includes a visible camera, and optionally an infrared camera, a near-infrared camera and/or a broad-spectrum infrared camera. 
     
     
         37 . The computing system according to  claim 34 , further comprising an audio recorder or a vibration recorder configured to measure vibration of the flare burner during flaring.