Patent Application
20230375431
This disclosure is generally directed to the field of atmospheric monitoring for emission leaks. More specifically, it relates to a method and system for locating and quantifying fugitive gas emissions leaking to the atmosphere.
In many jurisdictions there are strict controls on industrial greenhouse gas emissions, both from combustion sources and from fugitive gas emissions emanating from refinery plant equipment. While there is a greater quantity of combustion related emissions, fugitive gas emissions of uncombusted plant chemicals, such as for example methane gas have a much greater global warming potential for a given mass of emission.
Chemical manufacturing plants, petroleum refineries and other industrial facilities in developed nations are required to make an inventory of all plant equipment assets, such as valves, pumps, flanges, burners, etc., that could potentially be a source of fugitive gas emissions. A plant's operating company is required to periodically monitor the gas concentrations near each asset to ensure there is no significant leak of uncombusted gas from the plant's assets into the atmosphere. This is normally done manually. A technician using a hand-held leak-detection device, as defined by the applicable standards, uses the leak-detection device to inventory the assets for possible fugitive gas emissions. This approach is very time consuming and hence expensive. At large refineries and plants up to 25 technicians are employed full time to monitor all the equipment and assets to make observations and quantifications of emission leaks. The individual assets are only monitored infrequently, such as for example, once a quarter or once a year due to the substantial number of assets required to be monitored. Additionally, the manual methods fail to accurately monitor the rate of emissions being expelled into the atmosphere due to the manual process being error prone. Even in cases where the manual methods are successfully performed, the resulting observations may be too vague or inaccurate to provide a meaningful quantification of the emissions.
This disclosure relates to a method and system for locating and quantifying fugitive gas, emissions leaking into the atmosphere.
In a first embodiment a method for locating and quantifying fugitive gas emission leaks is disclosed, the method including obtaining gas sensor data from a plurality of gas sensors and wind direction data from at least one weather station, the plurality of sensors and the at least one weather station located proximate a given area of interest. The method further includes, validating the gas sensor data and the wind direction data to remove erroneous values and to merge the gas sensor data with the wind direction data to provide time synchronized gas sensor data and wind direction data over a given time interval. The method also incudes, segmenting the time synchronized gas sensor data and wind direction data for each location of the plurality of gas sensors into wind direction bins containing a concentration of gas levels contained in each bin and divide the area of interest into a grid of cells, projecting the bins on the grid cells for each gas sensor location along with the level of gas contained in the bins. The grid cells are then grouped into one or more contiguous grid cells having gas levels above a predefined level and a boundary area is calculated containing the grid cells with a gas level above a threshold to identify a potential leak area. The potential leak area is matched with a prior calculated leak area to identify the source location of the emission leak.
In a second embodiment, a system for locating and quantifying fugitive gas emission leaks is disclosed. The system comprising a plurality of gas sensors and at least one weather station located proximate an area of interest in a manufacturing plant. A data server operating a data processing program is communicatively coupled to each of the plurality of gas sensors and to the at least one weather station, the data server receiving gas sensor data from each of the plurality of gas sensors and wind direction data from the at least one weather station. The data processing program operates to validate the gas sensor data and the wind direction data to remove erroneous values and store the validated gas sensor data and wind direction data in a historian communicatively coupled to the data server. The gas sensor data and wind direction are fetched from the historian and validated to merge the gas sensor data with the wind direction data to provide time synchronized gas sensor data and wind direction data over a given time interval. The data processing program next operates to segment the time synchronized gas sensor data and wind direction data for each location of the plurality of gas sensors into wind direction bins containing a gas level in each bin and divide the area of interest into a grid of cell. The grid of cells are projected on a display monitor as bins for each gas sensor location along with a representation of the concentration of the level of gas contained in the bins. The data processing program further operates to group the grid cells into one or more contiguous grid cells having gas levels above a predefined level and calculate a boundary area containing the grid cells with a gas level above a threshold and projects the boundary area on the display monitor to identify a potential leak area. A prior calculated leak area is fetched from the historian to match the potential leak area with the prior calculated leak area to identify the source location of the emission leak.
In a third embodiment, a non-transitory computer readable medium is disclosed containing instructions that when executed by a data processing device, causes the data processing device to locate and quantify fugitive gas emission leaks by obtaining gas sensor data from a plurality of gas sensors and wind direction data from at least one weather station. The plurality of sensors and the at least one weather station located proximate a given area of interest. Next instructions are executed that validates the gas sensor data and the wind direction data to remove erroneous values and to merge the gas sensor data with the wind direction data to provide time synchronized as sensor data and wind direction data over a given time interval. The instructions of the computer readable program, segmenting the time synchronized gas sensor data and wind direction data for each location of the plurality of gas sensors into wind direction bins containing a concentration of gas levels contained in each bin and divide the area of interest into a grid of cells, projecting the bins on the grid cells for each gas sensor location along with the level of gas contained in the bins. The grid cells are then grouped into one or more contiguous grid cells having gas levels above a predefined level and a boundary area is calculated containing the grid cells with a gas level above a threshold to identify a potential leak area. The potential leak area is matched with a prior calculated leak area to identify the source location of the emission leak.
Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and claims.
For a more complete understanding of this disclosure, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which:
The figures, discussed below, and the various embodiments used to describe the principles of the present invention in this patent document are by way of illustration only and should not be construed in any way to limit the scope of the invention. Those skilled in the art will understand that the principles of the invention may be implemented in any type of suitably arranged device or system.
The embodiment of the present disclosure describes a discretized geospatial model that aggregates wind direction and fugitive gas emission readings from multiple sensors, over a period of time, to build up an estimate of the location of one or more simultaneous gas leaks. The method and system of the model uses plurality of geographically distributed gas sensors, and one or more weather stations located about an industrial facility. As a model-based approach, there is no need for a lengthy training and validation phase following sensor deployment.
Using the wind direction data provided by the weather stations, the method calculates an average wind direction over a given time interval. A maximum and minimum wind direction is calculated over a moving time window. The length of the moving timer window of sample data is based on the wind velocity, the maximum expected gas detection range of the sensors and the frequency of the discrete sampled data. In practice, this is implemented by computing multiple maximum and minimum wind directions, for a range of different moving window sizes, from which the most appropriate range is selected at any sampled time. The method of the present disclosure takes account of the variability in the wind direction. Periods of very high wind variability can be excluded from the analysis data set as part of a data cleansing step.
Time synchronized gas sensor data and wind direction data is segmented for each unique sensor location into small angular wind direction bins, such as, for example every 5 degrees of wind direction. The gas measurement readings for each non-zero sample is linearly divided between all the wind direction bins that contain the maximum and minimum wind direction. In another embodiment the method may use an implementation that divides the gas measurement readings based on a probability distribution of the wind direction between a maximum and minimum wind direction. The values in each bin, for each sensor location, are summed over a longer-term moving analysis window such as for example a 24-hour period. The bins are then used to project or back propagate a probable area of a leak source implemented as a cone shape from each sensor in the opposite direction to the wind direction. This approach enables the uncertainty in the wind direction measurement and the wind variability to be considered. The wind direction binning and summation approach reduces the computational burden by aggregating similar sensor information to reduce the number of samples considered in subsequent analysis steps.
The monitored area of a plant or facility is further divided into a two- or three-dimension grid cells comprised of a plurality of small volumes, which is common for simulation-based approaches such as computational fluid dynamics (CFD) models where one or more leak locations are known. In this disclosure, the grid cells are not being used for simulation, but rather to identify one or more unknown leak locations. In the current implementation a plurality of two-dimensional grid cells are used. A probable leak area is manifested as a cone shaped area and projected into the wind for each sensor location for every wind direction bin with a non-zero accumulation of gas readings. The intersection of each projected cone area with the plant grid cell is allocated a gas level score based on the product of the intersection area and the gas score for the projected cone area. The result provides a gas leakage score for each grid cell and a weighting of a gas level across a geospatial area comprised of multiple grid cells.
The grid cells that fall below a defined threshold of aggregated gas level are eliminated based on the percentile of all grid cells with non-zero aggregated gas levels. The grid cells are then grouped into one or more areas of contiguous cells with a non-zero aggregated gas level. For each grid cell grouping a boundary is then calculated containing the grid cells with a gas level above a predefined threshold.
With reference to
At a control layer, the system 100 may include one or more controllers 150 connected to wireless gateway 130 and I/O module 145 via a control network 151. The controller 150 can be used in the system 100 to perform various functions in order to control the data gathering process from the gas sensors 110 and weather stations 120. For example, the controller 150 may act as a supervisor to control the transfer of sensor data and weather data from weather data collected by the gateway 130 and I/O module 145. The controller 150 can also be used to provide diagnostic information to the system 100 of the operational health of the wireless gateway 130, I/O module 145 and the gas sensors 110 and weather stations 120 connected to them.
The controller 150 transfers the collected data to a plant server 160 via plant network 161 located at a plant 101 operations layer of the system 100. The server 160 denotes a computing device that executes data processing programs and applications including the method for locating and quantifying fugiWive gas emissions of the present disclosure. The server 160 could represent a computing device running a WINDOWS operating system or other operating system. Note that while shown as being local within system 100, the functionality of the server 160 could be remote from the system 100. For instance, the functionality of the server 160 could be implemented in a computing cloud or a remote server communicatively coupled to the control and automation system 100 via a gateway.
Operator access to and interaction with the controller 150 and other components of the system 100 can occur via one or more operator consoles 165 connected to plant network 161. Each operator console 165 could be used to provide information to an operator and receive information from an operator. For example, each operator console 165 could provide information identifying a current state of a plant process, such as the reported values of gas sensors 110 and weather data from the weather stations 120 and various displays associated with quantification of the fugitive gas emissions process of the present disclosure. Each operator console 165 could also receive information affecting how the industrial process is controlled, such as by receiving setpoints or control modes for the gas sensors 110 and weather stations 120 that alters or affects how the controller 150 controls the system 100. Each operator console 165 includes any suitable structure for displaying information to and interacting with an operator. For example, each operator console 165 could represent a computing device running a WINDOWS operating system or other operating system.
The plant operations layer of system 100 also includes at least one historian 170. The historian 170 represents a component that stores various information about the system 100. The historian 170 could, for instance, store information that is gathered by the gas sensors 110 and weather stations 120 for processing by server 160. The historian 170 includes any suitable structure for storing and facilitating retrieval of information. Although shown as a single component here, the historian 170 could be located elsewhere in the system 100, or multiple historians could be distributed in separate locations in the system 100.
Although
With reference to
The valid gas sensor data 404 from the historian 170 is sampled in operation 406 for a historical time window of data, typically ending at the current time, except for post analysis use cases. The duration of the historical time window can be adapted based on the information content and the accuracy of results required. For example, if the gas detection events are infrequent a longer time windows will generally be required to estimate one or more leak sources.
Next in operation 408 the weather data 401 and sensor data 404 are merged. Non-zero gas sensor data is paired with the corresponding wind direction and windspeed data on the basis of the nearest timestamp. If multiple weather stations 120 are available, the values of the gas sensors data 401 are paired with the geographically nearest valid wind direction and wind speed data that is nearest to each gas sensor 120 in operation 410.
In operation 412 a moving time window is defined based on the wind speed for each gas sensor 110. The window length chosen for the moving time window can be adjusted based on the sensor detection time and an expected travel time duration, typically of 30 seconds. The window length is used in operation 414 to calculate the maximum and minimum wind direction within the moving time window for every event in the data set, taking into account wind speed. At high windspeeds, the travel time of the molecules from leak to sensor is shorter so there is a lesser effect of the stochastic wind variations and thus, a shorter moving time window is used. At low wind speeds, travel time is longer and the moving time window can be set to a longer moving time window. Periods of extremely low wind speed and high wind direction variability are excluded from the analysis. It should be noted that there is transportation delay (dead time) between leaked gas and sensing by remote sensors. The dead time depends on leak rate, sensor proximity, gas plume characteristics, and wind speed and direction. Triangulation of leak locations requires sensor readings from at least two sensors, which for a fixed leak location requires at least two wind directions. Because of the stochastic nature of wind and lag during the travel (dead time) of a gas molecule from a leak to sensors and the need to acquire data over multiple wind directions, it is important to integrate sensor readings for some period before triangulation can take place. Longer integration periods result in increased confidence when triangulating at the expense of a delay in producing the prediction. This disclosure uses an adaptive process for time windowing of these sensor values. The integration period can be redefined from a fixed window of time to a period required to meet certain conditions required for confident predictions.
The result of operation 414 is a timeseries table (data frame) for each sensor/location, shown in Table 1 below.
For each unique gas sensor and gas sensor location combination (allowing for sensors to be moved), the wind direction is converted to a categorical variable by “cutting” or “binning” the wind direction into multiple discrete intervals, such as 0 to <5°, 5° to <10°, . . . , 355° to <360 by operation 416. The discrete interval range is configurable. When the max (maximum) and min (minimum) wind direction range is greater than the discrete interval range (as is the case for row 1 of Table 1), the gas sensor readings are divided and allocated equally across all the discrete intervals within the max-min wind direction range. For example, Table 2 illustrates how the data in row 1 of Table 1 would be linearly (evenly) distributed between the wind direction bins in the range of 5° to <100 to 350 to <40°. In other embodiments, different distributions such as a Gaussian or historical wind variations, can also be utilized.
In operation 418 for each unique gas sensor and gas sensor location combination, the gas readings are totalized for each wind direction bin. This operation enables a large amount of similar gas sensor data readings to be reduced to a smaller data set without losing the triangulation information provided by the binning. The result is a timeseries table (data frame) for each sensor/location shown in Table 3.
Upon completion of the data workflow aggregation of
A two-dimensional planar grid composed of grid cells will be used to in explaining the cone projections shown in
After all the aggregated gas sensor readings have been projected for the discretized wind directions, the accumulated gas concentrations within each grid cell are used to rank order the cells. In operation 510 the grid cells below a minimum threshold, for example based on the lowest 10% of all the cell concentrations, are dropped from the methods analysis. The remaining grid cells are then grouped together in operation 512 into agglomerations. More specifically, the grid cells are combined together if they are adjacent and have a non-zero (above a threshold) gas concentrations. Grid cells with a zero concentration delineate the agglomerations. Each cell agglomeration is extended by a small configurable buffer zone and the agglomerations are then checked for overlaps. Overlapping agglomerations are then merged to minimise small, fragmented agglomerations in operation 514.
Finally, the grid cells in each agglomerated area are reduced to those with the greatest, for example top 20% gas concentration in operation 518 and a boundary 730 shown in
For each newly identified leak area identified based on the last historical window of data of the workflow of
The estimated location of the leak(s), together with the magnitudes of the fugitive gas emissions detected around the suspected leak is used with a sensor type specific calibration model to provide an initial estimate of the leak size. The initial estimate of a leak size is then used as the initial conditions for a non-linear first principles model that more accurately quantifies the leak rate.
The simplified equation is then solved in operation 810 for the recorded data sets to provide the initial estimate of the leak rate(s). This is based on the Gaussian plume model of Turner.
Where:
The crosswind distance (y) and vertical distance terms (z, H) are ignored in the simplified Equation 2 below for a horizontal straight-line assumption.
Which Rearranges to:
Q=C(x,y,z)πUσyσz Equation 3
From Koch & Thayer (Koch, R. C., & Thayer, S. D. (1971). Validation and Sensitivity Analysis of the Gaussian Plume Mutiple-Source Urban Diffusion Model. Research Triangle Park, North Carolina: Environmental protection Agency, National Environmental Research Center) Table 1, σy and σz can be estimated:
σy=ax0.903 Equation 4
σz=bxq Equation 5
Where parameters a,b and q depend on the meteorologic wind stability condition. (Koch & Thayer, 1971) Table 1 defines estimates of a,b and q for the meteorologic wind stability conditions defined by Gifford (Gifford, 1961). These conditions are:
Q=aC(x,y,z)πUxβ Equation 6
For the mid-range weather condition of slightly unstable, a=0.0222 and β=1.81. However, these parameters can be can also be experimentally determined from sensor calibration data or CFD simulations where available.
In operation 815 of
σy=k*σyb
σz=k*σzb Equation 7
The k factor effectively compensates for the unknown wind stability condition.
For a known measurement of gas concentration at an estimated distance x,y,z from an emission source, the following prediction error estimate can be formulated with two unknowns, the leak rate Q, and the wind stability model parameter k.
This error can be summed over multiple estimates of sensor to source distances and corresponding gas concentration ppm levels to define an objective function using Equation 8. The objective function is then differentiated with respect to the two unknown variables by solving for a partial derivative of the squared error with respect to parameter k and to the squared error with respect to the leak rate Q. The derivatives are used to formulate an update to the estimates of the model parameters based on the standard gradient descent method, using Equation 9.
A key challenge implementing the gradient descent method with the derivatives of the Gaussian Plume model is that that they are very non-linear and the convergence time to a minimum error is very dependent on the initial conditions. The initial conditions provided by the sensor calibration curve enable the equation to be solved robustly in a timely fashion.
It may be advantageous to set forth definitions of certain words and phrases used throughout this patent document. The term “communicate,” as well as derivatives thereof, encompasses both direct and indirect communication. The terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation. The term “or” is inclusive, meaning and/or. The phrase “associated with,” as well as derivatives thereof, may mean to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, have a relationship to or with, or the like. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more of the listed items may be used, and only one item in the list may be needed. For example, “at least one of: A, B, and C” includes any of the following combinations: A, B, C, A and B, A and C, B and C, and A and B and C.
The description in the present application should not be read as implying that any particular element, step, or function is an essential or critical element that must be included in the claim scope. The scope of patented subject matter is defined only by the allowed claims. Moreover, none of the claims is intended to invoke 35 U.S.C. § 112(f) with respect to any of the appended claims or claim elements unless the exact words “means for” or “step for” are explicitly used in the particular claim, followed by a participle phrase identifying a function. Use of terms such as (but not limited to) “mechanism,” “module,” “device,” “unit,” “component,” “element,” “member,” “apparatus,” “machine,” “system,” or “controller” within a claim is understood and intended to refer to structures known to those skilled in the relevant art, as further modified or enhanced by the features of the claims themselves and is not intended to invoke 35 U.S.C. § 112(f).
While this disclosure has described certain embodiments and generally associated methods, alterations and permutations of these embodiments and methods will be apparent to those skilled in the art. Accordingly, the above description of example embodiments does not define or constrain this disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of this disclosure, as defined by the following claims.
This application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 63/342,951 filed on May 17, 2022. This provisional application is hereby incorporated by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63342951 | May 2022 | US |
