Abstract:
A video analytics system for characterization of a flare. A video of a flare may be taken for obtaining information so as to appropriately control the flare in an interest of reducing emissions not necessarily favorable to the environment. The system may incorporate a control scenario involving one or more parameters of a flare which are to be controlled in view of a flare characterization from an algorithmic analysis of the video.

Description:
BACKGROUND 
     The invention pertains to sensors, and particularly to flare sensors. More particularly, the invention pertains to flare detection, evaluation and control. 
     SUMMARY 
     The invention is a flare management system using video capture and analysis of the flare for automatic control of the flare to attain minimum emissions of the flare. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING 
         FIG. 1  is a diagram of a flare management system; 
         FIG. 2  shows an example of the use of a generalized cylinder (GC) for representing the three-dimensional (3D) surface of a flare; 
         FIG. 3  shows an example of a complex flare modeled by a GC; 
         FIG. 4  shows a 3D approximation of the geometry of the flare using a GC; 
         FIG. 5  shows a top view of the 3D GC-based approximation of the geometry of the flare from several views; 
         FIG. 6  is a diagram of a process control system that illustrated a flare control strategy; 
         FIG. 7  is a diagram of various actions of the flare management system it relates to video observation, analysis and control of a flare; and 
         FIG. 8  is a diagram of activities related to a video analytics server module of the flare management system. 
     
    
    
     DESCRIPTION 
     The present invention relates to a flare management system that uses video to manage, control and record flares used in industrial processes. Many industrial manufacturing plants generate substantial quantities of waste gases. A frequently used method for disposing of these waste materials is to burn them in what is known as a flare system. Flare systems may be a critical component of industrial plants such as hydrocarbon processing plants because they not only prevent unwanted emissions to be released to the atmosphere, they also eliminate dangerous pressure buildup in processing units during emergency or upset conditions. 
     A flare system may be distributed throughout the entire plant and includes various elements. The flare system may include control valves that prevent processing units from exceeding a particular design pressure limit, or emergency relief valves or rupture disks that are installed to provide critical protection of a process unit&#39;s operating pressure. 
     The flare system may include a pilot ignition source which ignites the hydrocarbon waste gases. The gases need to be able to completely combust. Incomplete combustions may result in dark smoke emitting from the stack and the release of waste gases into the atmosphere. Depending on the flare type, steam, air or other gases may be injected with the waste gases to improve combustion. For example steam may reduce the flame temperatures to a point that provides optimal burning and assists with the conversion process of flammable hydrocarbons. 
     Typically, the volume and consistency of the combustible excess gases in the flare system may vary significantly during normal operations, startups and shutdowns of process units and equipment, as well as during emergency or upset conditions, and hence a variable amount of steam may be required. In addition, cross winds may alter the combustion efficiency. 
     The steam injection control therefore represents significant challenges to the operator that is managing the plant. The results of not adding enough steam are discussed herein. The results of adding too much steam can also be undesirable because a sound pressure wave or “shock wave” may be created resulting in significant noise pollution, and the flame temperature may become below optimal temperature for destruction of some waste gases, and in severe cases oversupply of steam may even snuff the flame. 
     Flare management approaches aim to reduce air pollution, excessive noise and shockwaves that are associated with the burning of the flare gases. Operational design reviews and flare reports may indicate major deficiencies with the management of flares. 
     Flare emissions currently tend to be very difficult to measure. Problems may include the effect of the high temperatures on the sensors, the meandering and irregular nature of flare flame, and the high levels of condensations, corrosion and drag forces that occur near the flare that increase mechanical wear on the sensors. 
     In addition, due to the safety concerns surrounding the flare stack, and the possibility that in the event of a serious plant upset, liquids could actually overflow from the top of the flare stack, a “safety perimeter” may be created around the flare stack. Thus, while the flare stack is in service, no personnel are allowed inside this perimeter. In addition, no instrumentation, including control valves, transmitters, or other sensors should be placed inside this perimeter, since these instruments cannot be serviced or maintained during normal operation of the flare. This means that all of the control and instrumentation equipment need to be installed outside of this safety perimeter. In some processing plants, this perimeter may be as far as 75 to 100 meters from the actual flare stack, resulting in significant dead time in the control response. Related art remote flare sensors typically only measure the existing or non-existence of a pilot flame, and whether the flame includes smoke or not, but fail to detect other key flare characteristics. Without proper monitoring it appears impossible to know whether flares are performing as expected. 
     The present system and approach may use electro-optics (i.e. visual) and infrared information, captured by one or more video cameras, to determine flare characteristics and to improve flare management. The video cameras may be located at a safe distance of the flare. The flare characteristics may be derived from the cameras using video analytics algorithms and be combined with other plant information such as wind speed and process control system sensors. The flare management system may then present the flare information to the operator controlling the process. The flare management system may provide input to the flare process control loop. The flare management system may accurately record and store flare information for later retrieval and regulatory purposes. 
     Relate art video images of the flare appear not to be correlated to the process control system that is used to control the flare. Without correlation, an operator would need to view the flare video images and then manually change the process control strategies to optimize the flare when required. During a plant upset, for example, as caused by a blocked valve in a process unit, requiring gas to be diverted to the flare, manual flare management may be particularly difficult for an operator to manage because of information overload that may occur during plant upsets. In this example, the operator would need to manage both the effects of the blocked valve on the process unit affected, as well as the changed flare conditions caused by this plant upset. The present system and approach may automate flare management by automatically using the video images to manage the flare without operator intervention. The present system and approach may automatically capture and store the video images of the flare that are associated with the upset for later retrieval and review and regulatory purposes. The video images may be captured with a digital signature such that they cannot be altered. In this example, the system would allow the operator to focus all attention on the blocked valve that appears to be causing the problem. 
       FIG. 1  is a diagram of a flare monitoring/management system  10  using video cameras  24  as a process sensor. Flare  23  management may be achieved with a user interface  13 , camera server  20 , video database server  17 , and a video analytics server  11 . The user interface  13  of the flare management solution may provide a visual sense of the flare characteristics to the user on a display  14 . Users may include the operator that is controlling the plant  21 , the process control engineer, plant manager, and others. The user interface may integrate live video images of the flare  23  with the derived flare characteristics and relevant real-time process information including process alarm  42 , process value and process trends. 
     The flare management system  10  may be configured to automatically show the flare  23  to the operator when an alarm  42  occurs in the plant  21  that is related to the flare, thus increasing the operator effectiveness during plant upsets. 
     The user interface  13  may include an alarm summary display  14  that combines alarms  42  that are associated with the flare monitoring and with the process. This can enable a view of both the process as well as scenes from the cameras  24  with a single user interface  13 . The user interface may also enable the immediate retrieval of previously recorded video. Recorded video may enable the operator to compare the current flare characteristics with previously analyzed flare characteristics. Using the user interface, a user may store the current frame of video (snapshot) as a bitmap image file. The file name may be automatically generated by the solution software and include the camera name, date and time of the recording (to millisecond precision). The user interface may be protected by passwords and supports different levels of access ranging from view only to full control of the flare. 
     The flare monitoring system  10  may include a quick search method for all video that is recorded. The user may select the time indicator which shows a calendar and time line. The user selects the required search period. Video recorded during the selected period will be returned by the search. The operator can add comments to each recording and can later use these comments or part thereof as part of the search strategy. 
     The flare monitoring system  10  may include a machine learning-based analysis of the flare for automatically relating observed flare to past recording or to specific status of the process. Such mapping of flare observations to process status allows for the automatic characterization of the process status based on the analysis of the observed flare. 
     User actions may be recorded in a log file. User actions may include cameras  24  that are viewed, video that is replayed, and video control. The log may also include the status of the flare monitoring system components including cameras  24 , servers  11 ,  17  and  20 , and other system components. The log of user and system actions may be available in text format and automatically included with any video recordings that are exported. 
     A camera server  20  may account for communications with the video cameras  24 , manage recordings, store recordings, and serve live and recorded video to the user interface  13 . Specifically, the camera server  20  may manage live video from the cameras  24 , transmit live video to the user interface  13 , receive camera control commands from the user interface and then send the commands to cameras  24  (for example, camera pan, tilt and zoom commands), store live video of the cameras, transmit previously stored video to the user interface, archive previously stored video to off-line  30  storage media, retrieve archived video from off-line storage media, and export the recordings so that they can be viewed using standard video player software. 
     The flare management system  10  may support multiple camera servers  20 . More than one camera server may be required when multiple cameras are being used and the number and individual configurations exceed the capacity of a single camera server, or when cameras are geographically distributed. 
     The video database server  17  may include the information database and run the programs of the solution. The database server may contain a database of the network-connected cameras and their configurations. The server may manage the system database, containing details including the system configuration, camera configuration and settings, recording configuration and settings, details of recordings, schedules, user security details, configuration of the user interface  13 , configuration of video analytics that are used to analyse the flare, and communication between the user interface and the camera servers. 
     The server may also enable alarms or events in the process control system  12  to initiate high resolution recordings, report any camera failures or recording failures to the process control system as an alarm  42 , and provide a full audit log of all system status (camera and server availability) and operator actions. 
     All exported recordings and exported audit logs may be digitally signed. This can ensure proven authentication (origin of the recording and audit log) and integrity (exported recording and audit log have not been altered or tampered with) of the video. 
     The database server  17  may optionally be used in a redundant configuration to ensure availability for critical flare management applications, using two separate database servers (being executed on separate computers). The backup database server may be continuously synchronised with the master database server to ensure that it is always up-to-date and ready for a fail-over, when required. 
     The video analytics server component  11  may process video analytics that are used for the analyses of the flare video streams. The video analytics server module  11  may have several modules which perform flame monitoring, flare volume estimation, and heat content and combustion efficiency analysis. In flare monitoring, the use of video cameras  24  (one or multiple) monitoring the flare may permit one to characterize the properties of the underlying physical process. Analysis of the flare color, shape and their variation over time appear critical in real-time analysis of the combustion efficiency. Video cameras  24  may be used as a remote sensing tool that allows the operator to optimize the combustion process. 
     In flare volume estimation, the use of a video sensor  24  for monitoring the flare may be used to infer the volume of the flare. Two approaches may be used to estimate the volume of the flare and its temporal variations. 
     From a single camera  24 , the computation of the silhouette  71  (i.e., outline) of the flare (by motion detection, or by spatio-temporal segmentation of the edges in the scene, or by layer-based segmentation of the image, . . . ) may be used for estimation of the volume of the flare  23 . Second, one may use an approximation of the 3D shape of the flare using a generalized cylinder (GC). GC is uniquely defined by its medial axis  72  and edges or a silhouette  71 . The 3D surface of flare may then be parameterized by the following equation.
 
 S ( u,v )= A ( u,v )+ R ( u,v )* B ( u,v )
 
where A(u,v) is a parameterization of the medial axis, B(u,v) is the “binormal” at the point A(u,v) and R(u,v) is the radius of the cross-section  73  at A(u,v). The medial axis  72  may be defined as points inside the flare  23  that are at equidistance from the edges  71  of the flare (i.e., the silhouette  71 ). The cross-sections  73  of the “generalized cylinder” (GC) may be inferred from the silhouette  71  of the flare  23 .
 
     An example is shown in  FIG. 2  where a simple medial axis  72  is displayed.  FIG. 3  shows a more complex case, where an implicit representation of the flare  23  appears more suitable due to the complexity of the topology of the flare. 
     In these cases, a parameterized or an implicit flare  23  surface description may be inferred from the silhouette  71  and the medial axis  72 . Using intrinsic camera  24  calibration information (i.e., pixel size, pixel ratio, center of projection), may allow one to map pixel size to physical measurement. This analytical representation of the surface, along with intrinsic camera parameters, may then be used for estimating the volume the flare  23  by estimating the volume of the corresponding GC.  FIG. 2  shows an example of the use of a generalized cylinder for representing the 3D surface of a flare.  FIG. 3  shows an example of a complex flare modeled by a generalized cylinder. 
     From multiple cameras, another approach may consist of using two or more cameras  24  for monitoring the flare. These cameras may correspond to various vantage points and provide a more accurate estimation of the 3D shape for the flare. 
     In the case of two synchronized cameras  24 , one may again use a GC-based representation in the case of an anisotropic GC model which appears appropriate for accurately characterizing the shape of the flare in the presence of strong crosswinds, or during transitional phases of the flame  23 . In this case, one may integrate the information from two silhouettes  71  by matching corresponding cross sections  73 . The matching may be driven by the knowledge of the epipolar geometry relating the two views. Epipolar geometry appears sufficiently described herein in conjunction with the description for the present invention. However, another description may be found in Hartley and Zisserman, “Multiple View Geometry in Computer Vision”, Cambridge University Press. 
     The epipolar geometry may be the intrinsic projective geometry between two views. It may depend on the camera  24  intrinsic parameters and relative pose, and be independent of the scene structure. The epipolar geometry may be encapsulated by a fundamental matrix which is a 3×3 matrix of rank  2 . If a point P in 3D space is imaged at p in the first camera  24 , and p′ in the second camera  24 , then the image points may satisfy p′ T Fp=0. 
     The estimation of the epipolar geometry may be done manually by the operator by selecting seven or more matching anchor points across the cameras, or using an automatic estimation of the epipolar geometry based on the automatic extraction and matching of salient feature points in the scene. 
     The epipolar geometry constraint may hold for any 3D point in the scene viewed by two or more cameras  24 . If one considers an approximation of the 3D surface in the scene by a GC, one can relax the epipolar geometry constraint and derive similar equations for points in the 3D surface that lie along the same cross section  73 . This approach is depicted in  FIG. 4 . 
       FIG. 4  shows a 3D approximation of the 3D geometry of the flare using a GC. The Figure is an illustration of the projection of the flare  23  into two views  74  and  75  of first and second cameras  24 , and the corresponding cross sections  76  and  77  of the projected silhouette  71  with the epipolar lines  78  and  79 , respectively. 
     The cross sections  76  and  77  may here be defined by the intersection of the projected silhouette  71  with the epipolar lines  78  and  79 , respectively. This is illustrated in  FIG. 5 . This result is valuable in that it may allow the reconstruction of GC from various projection of its 3D silhouette  71 . This is illustrated in that it depicts a top view of the present approach. One may use an analytical representation of the GC to estimate the volume of the flare  23 .  FIG. 5  shows the top view of the 3D GC-based approximation of the 3D geometry of the flare from two views  74  and  75 . 
     In a case having a larger number of synchronized cameras  24  monitoring the flare  23 , 3D shape estimation from silhouettes  71 ,  81  may be used for estimating accurately the volume of the flare. Silhouette  81  is in essence a cross section of the 3D flare  23  viewed from above. Cross section  73  is that of a GC approximation of the 3D surface of the flare. In this case, one may rely on a triangulation of the 3D surface to estimate the corresponding volume. 
     Another module of the video analytics  11  may include heat content and combustion efficiency. The combination of the estimated 3D shape of the flare  23 , the analysis of its shape, and the analysis of the colors can be used for characterizing the flame properties. Color analysis, along with 3D shape characterization can provide an accurate assessment of the heat content of the gas, the combustion efficiency, the NO x  emissions . . . . 
     The present approach may be based on a localized analysis of the shape and color information provided by the video sensors  24 . One may use a covariance-based metric that allows taking into account several point-based measurements, and developing a location-dependent analysis of the color. The definition of a location-dependent descriptor of the colors of the flare may provide the capability to analyze several color profiles, and identify specific behaviors that are dependent of the position the color features. These measurements may include the following.
 
 M ( i,j )=└ d ( i,j ), R,G,B,R   x   ,R   y   ,G   x   ,G   y   ,B   x   ,B   y ┘
 
where i,j correspond to the position  82  in the image of  FIG. 3  at the location that the measurement is taken, d(i,j) corresponds to the distance  83  of the pixel i,j to the closest medial axis  72  of the flame  23 , the corresponding R, G, B color, their gradients. One may also consider better color representation such as “hue saturation value” representation of the color as indicated by the following.
 
 M ( i,j )=└ d ( i,j ), H,S,V,H   x   ,H   y   ,S   x   ,S   y   ,V   x   ,V   y ┘
 
where i,j correspond to the position  82  in the image at the location where the measurement is taken, d(i,j) corresponds to the distance  83  of the pixel i,j to the closest medial axis  72  of the flame  23 , the corresponding H,S,V color, and their gradients. This feature vector may be augmented with other geometric descriptors of the flare such as the distance to the medial axis  72  of the 2D silhouette . . . .
 
     The color descriptor of the flare  23  may then be encoded in the auto-covariance matrix, 
               C   =       ∑     i   ,   j       ⁢       (       M   ⁡     (     i   ,   j     )       -     M   _       )     ⁢       (       M   ⁡     (     i   ,   j     )       -     M   _       )     T           ,         
where  M  is the average vector.
 
     The color properties of the flare may fluctuate dependent on the chemical content and the underlying combustion process. The monitoring of the flare status may be performed by comparing the observed color properties (represented through the auto covariance matrix) and a set of past observations or reference profiles. For this purpose, one may use the Frostener distance measure to compare two matrices using the following metric, 
                 d   ⁡     (     A   ,   B     )       =         ∑     i   =     1   ⁢           ⁢   …   ⁢           ⁢   N         ⁢       ln   2     ⁢       λ   i     ⁡     (     A   ,   B     )               ,         
where, A and B are two instances of the flare color descriptors, and λ i (A,B) includes the generalized eigenvalues obtained by solving |λA−B|=0.
 
     A classification of the color properties of the flare  23  may be done using methods such as “support vector machine”, “k-nearest neighbor clustering” or similar tools that allow comparing new observations to models or templates built from a collection of previous observations. 
     The solution may support one or more cameras  24 . There appears to be no limit to the number or type of cameras that can be supported. The cameras may communicate with the other components of the flare monitoring system  10  via wireless communication. The flare  23  and associated cameras  24  may be located at unmanned or remote locations. 
     The process control system  85  of a plant may monitor and control industrial sites using integrated controllers. It may provide a real-time view of the status of the control system to operators. The process control system may control the flare system. An example of a flare control strategy is illustrated by the process control system  85  in  FIG. 6 . In the strategy shown, the liquid knockout drum  86  may be installed to remove any unwanted liquids that may have entered the flare gases or flare gas stream. These liquids are usually returned to the plant&#39;s slop system for reprocessing. 
     The flare gases may then be transported through a seal drum  87 . This drum is essentially a simple liquid filled tank, providing a seal that requires a specific amount of pressure for the vapour to break through or “burp” prior to entering the flare stack  22  piping. The seal drum may ensure a minimum amount of pressure on the flare header  91  and prevent air from entering the header, which could lower the hydrocarbon vapour concentration in the header below the upper explosive level (UEL) and result in an explosion. Unit pressure control  92 , unit quality control  93  and unit overpressure protection control  94  may be connected to flare header  91 . A safety perimeter  97  may be placed around the flare stack  22 . No personnel are to be allowed between the perimeter and the flare stack while the flare  23  is in operation. The distance between the perimeter  97  and the stack  22  may be from 75 meter to 100 meters. 
     The present control strategy may use the information collected around the seal drum  87  as a method of providing a feed-forward signal to the steam control valve  88 . The flare characteristics that are derived from the cameras  24  using video analysis algorithms may be combined with an accurate measurement of hydrocarbon in the flare piping  89 , thereby providing an excellent way of trimming the amount of steam injection. Steam controlled by valve  88  may go to a steam manifold  95  and tips via line  96 . 
       FIG. 1  is a diagram of the flare management system  10 . A plant  21  with a stack  22  and corresponding flare  23  from it to be managed is shown. A camera or several cameras  24  may be directed toward the flare for video observation. The video observation may be recorded in system  10 . A connection between system  10  and camera or cameras  24  may be wired or wireless. Signals  31 , digital and/or analog, from the camera or cameras  24 , may go to a video analytics module  11 . These cameras may detect visible, infrared and/or ultra-violet light from flare  23 . Module  11  may record the video. The video from the camera or cameras  24  may be converted to a digital format, if received in an analog format. A camera server may be connected to cameras  24  and control system for camera and video data control. The video may be analyzed to determine contents and parameters of flare  23 . The module  11  may provide analysis results to a user interface  13  via a control system  12  or directly to the interface  13 . The user interface  13  may have a video screen or display  14  and a keyboard  15 . Keyboard or mechanism  15  may include a mouse, a joystick, a touch screen, and/or the like. A user may monitor and control the plant  21  and flare  23  at the interface  13 . The monitoring and controlling may be via the control system  12  and plant controller or controllers  16 , including valve mechanisms. Control system  12  may have a processor to facilitate an interaction between the user interface  13 , the analytics module  11  and controllers  16 . The user interface  13  may contain a computer or it may have electronics just sufficient to effectively connect the display  14  and keyboard  15  with control system  12 . Controller or controllers  16  may have processors for control and monitoring of various plant  21  parameters. Control of the parameters may include control of those having an effect on the flare  23 . These parameters may be detected by sensors  25  which may be connected to control system  12 . 
     Also connected to control system  12  may be a video database server and video logging module  17  with or without a digital signature. Video information may be recorded, labeled and the like in module  17  for comparison and analysis by module  11 . The user interface  13 , controller  16  and video logging module  17  may have wireless or wired connections to control system  12 . Further, wireless or wired connections may be among interface  13 , controller  16 , module  17  and camera server  20 . 
     Describing flare management from another aspect using video may be achieved via a combination of various components which includes the user interface  13 , a camera server  20 , a video database server  17 , video analytics server  11 , one or more cameras  24 , sensors  25 , alarm system  42  and a process control system  12 , as noted in  FIG. 1 . 
     The user interface  13  for the flare management solution may provide a visual sense of the flare characteristics to the user. The user interface may integrate live video images of the flare with the derived flare characteristics and relevant real-time process information and process trends. The user interface may also enable the immediate retrieval of previously recorded video. Recorded video can permit the user to compare the current flare characteristics with previously analyzed flare characteristics. The user interface  13  may be protected by passwords and support different levels of access ranging from just viewing to full control of the flare  23 . 
     The camera server  20  may cover communication with the video cameras  24 , manage recordings, store recordings and serve live and recorded video to the user interface  13 . Particularly, the camera server  20  may manage live video from the cameras  24 , transmit live video to the user interface  13 , receive camera control commands from the user interface and then send the commands to cameras  24  (for example, camera pan, tilt and zoom commands). The server  20  may also store live video to hard disk, such as that of module  17 , transmit previously stored video to the user interface  13 , archive previously stored video to off-line storage media at outside  30 , retrieve archived video from off-line storage media, and export to the outside  30  the recordings so that they can be viewed using third party video player software. The camera server  20  may rely on the database server  17  for camera database information. The outside  30  may be connected by camera server  20  or other components of system  10  via wireless, wire, or a combination of wire and wireless. Such connections may use cable, internet, phone, optics, RF, and so forth. 
     The present system  10  may support multiple camera servers. More than one camera server  20  may be required when multiple cameras  24  are being used and when the number and individual configurations exceed the capacity of a single camera server, or when the cameras are geographically distributed. 
     The video database server  17  may include the information database and run programs of the system. The server may contain a database of the network-connected cameras  24  and their configurations. The database server  17  may manage the system database, containing details including the system configuration, camera configurations and settings, recording configuration and settings, details of recordings, schedules, user security details, configuration of the user interface  13 , and the configuration of video analytics  11  that are used to analyse the flare  23 . 
     The video database server  17  may also manage communications between the user interface  13  and the camera servers  20 , enable alarms  42  or events in the process control system  12  to initiate recordings, report any camera failures or recording failures to the process control system, and provide a full audit log of all system status (camera and server availability) and operator actions. 
     The database server  17  may optionally be used in a redundant configuration to ensure availability for critical flare management applications, using two separate database servers  17  (being executed on separate computers). The backup database server may be continuously synchronised with a master database server to ensure that it is always up-to-date and ready for a fail-over, when required. There generally may be just one database server or a redundant database server pair in the flare management system  10 . 
     The video analytics or analysis server module  11  may process the video analytics that are used for the analyses of the flare video streams. The system  10  may support one or more cameras  24 . The process control system  12  may monitor and control industrial sites using integrated controllers  16 . It may provide a real-time view of the status of the control system to operators. 
     The flare management system  10  may use digital video. Live output from cameras  24  may be viewed through the user interface  13 . The user interface may support a flexible variety of views to enable the user to select the view that best fits the situation of the flare  23 . For example, during normal situations, the user may be using a view that only shows alarms of system  42  associated with the flare, while during an abnormal situation the user may want to view the flare constantly. 
     The system  10  may support the following situations. When an alarm  42  occurs in the process control system that is related to the flare  23  management, the live video output of the camera  24  associated with that alarm may be switched directly to a dedicated monitor  14 . The user may acknowledge the alarm to clear the monitor. 
     There may be various aspects to controlling the flare cameras  24  and camera stream storage. From the user interface  13 , the user may zoom, turn or focus the camera using a joystick or other pointing device such as a mouse or touch screen of mechanism  15 , and the user may start and stop the recording of live video. From the interface or station  13 , the user may store the current frame of video (snapshot) as a bitmap image file. The file name can be automatically generated by the solution software and include the camera name, date and time of the recording (to millisecond precision). From the user interface, the user may zoom, turn or focus the camera using a joystick or other pointing or directing device such as a mouse or touch screen of mechanism  15 . 
       FIG. 7  is a diagram of various actions of system  10  as it relates to observation, analysis and control of flare  23 . Video inputs  31  may come from camera or cameras  24  and become part of a digital video stream capture  32 . If the inputs  31  may be converted to a digital format if in an analog format. The digital video stream may become a part of a video stream analysis  33 . Also, incorporated in the video stream analysis  33  may be an analytics configuration  34  which includes, at least in part, configuration information  35  of a video processor  36 . The digital video stream capture  32 , video stream analysis  33 , analytics configuration  34 , video processor configuration  35  and video processor  36  may be a part of the video analytics module  11 . Analysis information  33  about the flare  23 , along with other relevant information  37  from control system  12 , may be provided to an automated flare control module  38  which is connected to control system  12 . Information  33  and  37  may also be provided as a graphical presentation  39  to the user at the interface  13 . Information  33  and  37  may be provided as alarm information  41  for the user and an alarm system  42 . Information  33  and  37  may be saved in a video storage  43  which is managed by a video server  17 . Video storage  43  and the video server  17  may be a part of the video logging module  17 . Storage  43  and server  17  may also be, in some designs, a part of a storage area in the video analytics module  11 , the control system  12  and/or the user interface  13 . 
       FIG. 8  is a diagram of activities related to and in the video stream analysis  33  of module  11 . From the video inputs  31  and corresponding digital stream  32 , various or spectrally different images may be calibrated and fused at step or stage  51 . Then at stage  52 , flare segments of interest may be extracted from the images of stage  51 . These segments are analyzed and characterized according to color  61 , temperature  62 , shape  63 , variance  64  and persistence  65  at stage  53 . These characteristics are used to identify and classify user specified events at stage  54 . At stage  55 , events from multiple flare segments of interest may be linked temporally and spatially. In stage  56 , results and readings from multiple sensors may be linked. These sensors, among others, may include those for wind  66 , temperatures  67 , flares  68  and pressures  69 . With information of the sensors at stage  56 , segments of interest in stage  55  and the identification and classification at stage  54 , the user specified events of stage  53  may be interpreted into a sequence of cause-effect events at stage  57 . This information obtained at stages  54 ,  55 ,  56  and  57  may be placed into storage  43  and retrieved as needed at stage  58 . The information at stage  58  may be provided to the user interface  13  at stage  59  and at stage  60  for control purposes by a user at interface  13  via control system  12  and plant controllers  16 . 
     In the present specification, some of the matter may be of a hypothetical or prophetic nature although stated in another manner or tense. 
     Although the invention has been described with respect to at least one illustrative example, many variations and modifications will become apparent to those skilled in the art upon reading the present specification. It is therefore the intention that the appended claims be interpreted as broadly as possible in view of the prior art to include all such variations and modifications.