METHOD AND SYSTEM FOR LOCATING AND QUANTIFYING FUGITIVE EMISSION LEAKS

A method and system for locating and quantifying fugitive gas emission leaks includes obtaining gas sensor data and wind direction data from a plurality of sensors and weather stations located proximate a given area of interest. The gas sensor data and the wind direction data is validated 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 time synchronized gas sensor data and wind direction data is segmented for each gas sensor location into wind direction bins containing a concentration of the gas levels in each bin. The area of interest is divided into a grid of cells and the bins projected 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.

TECHNICAL FIELD

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.

BACKGROUND

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.

SUMMARY

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.

DETAILED DESCRIPTION

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 toFIGS.1and2the system for locating and quantifying fugitive gas emissions leaked into the atmosphere is illustrated. Multiple gas sensors110are geospatially distributed in around external area near assets of a process plant, well-head, factory, or other refining or chemical manufacturing facility that may contain potential leak sources, hereinafter referred to as a plant101. One or more weather stations120are also located near or amongst the gas sensors120. The weather stations120are used to sense and report wind direction, wind speed, temperature and optionally humidity.FIG.1illustrates a collection of gas sensors110distributed around a plant101, together with weather stations120. The gas sensors110measure the gas concentration levels at a specific time intervals such as for example, between 2 and 30 seconds and the weather stations120also record the wind direction and wind speed at the corresponding gas sensor reading intervals.

FIG.2illustrates an exemplary system100for gathering information from the gas sensors110and weather stations120and for processing the information gathered to locate and quantify any fugitive gas emissions from the plant101. At a device layer of the system100the gas sensors110and weather stations120are connected either to a wireless gateway130or through a wired network140to an I/O module145. More than one wireless gateway130may be deployed in the device layer each wireless gateway having a plurality of wireless gas sensors110and wireless weather stations120connected to the wireless gateway130. Similarly, more than one I/O module145may be used to connect to a plurality of wired gas sensors110and wired weather stations120using a wire network140.

At a control layer, the system100may include one or more controllers150connected to wireless gateway130and I/O module145via a control network151. The controller150can be used in the system100to perform various functions in order to control the data gathering process from the gas sensors110and weather stations120. For example, the controller150may act as a supervisor to control the transfer of sensor data and weather data from weather data collected by the gateway130and I/O module145. The controller150can also be used to provide diagnostic information to the system100of the operational health of the wireless gateway130, I/O module145and the gas sensors110and weather stations120connected to them.

The controller150transfers the collected data to a plant server160via plant network161located at a plant101operations layer of the system100. The server160denotes 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 server160could represent a computing device running a WINDOWS operating system or other operating system. Note that while shown as being local within system100, the functionality of the server160could be remote from the system100. For instance, the functionality of the server160could be implemented in a computing cloud or a remote server communicatively coupled to the control and automation system100via a gateway.

Operator access to and interaction with the controller150and other components of the system100can occur via one or more operator consoles165connected to plant network161. Each operator console165could be used to provide information to an operator and receive information from an operator. For example, each operator console165could provide information identifying a current state of a plant process, such as the reported values of gas sensors110and weather data from the weather stations120and various displays associated with quantification of the fugitive gas emissions process of the present disclosure. Each operator console165could also receive information affecting how the industrial process is controlled, such as by receiving setpoints or control modes for the gas sensors110and weather stations120that alters or affects how the controller150controls the system100. Each operator console165includes any suitable structure for displaying information to and interacting with an operator. For example, each operator console165could represent a computing device running a WINDOWS operating system or other operating system.

The plant operations layer of system100also includes at least one historian170. The historian170represents a component that stores various information about the system100. The historian170could, for instance, store information that is gathered by the gas sensors110and weather stations120for processing by server160. The historian170includes any suitable structure for storing and facilitating retrieval of information. Although shown as a single component here, the historian170could be located elsewhere in the system100, or multiple historians could be distributed in separate locations in the system100.

AlthoughFIG.1illustrates one example of a system100, various changes may be made toFIG.1. For example, the system100could include any number of sensors, actuators, controllers, networks, operator consoles, control rooms, historians, servers, wireless devices, and other components. Also, the makeup and arrangement of the system100inFIG.1are for illustration only. Components could be added, omitted, combined, further subdivided, or placed in any other suitable configuration according to particular needs. Further, particular functions have been described as being performed by particular components of the system100. This is for illustration only. In general, control systems are highly configurable and can be configured in any suitable manner according to particular needs.

With reference toFIG.3, the initial data collection and pre-processing workflow300of the method of the present disclosure is illustrated. The sensor data301read from the gas sensors110and the weather data304read from weather stations120are transmitted in operation306wirelessly to the local gateway130or via wired network145to I/O module140. The sensor data301and weather data304are then transferred by controller150and acquired308by the server160. In operation310the sensor data301and weather data304are validated to remove erroneous values defined as bad data, including but not limited to: (i) the quality status for the gas sensor data301and weather data304that are outside of a high and low range; (ii) the quality status for the gas sensor data301and weather data304that exhibit a high rate of change above a pre-defined limit for the current timestamp and a configurable number of subsequent sample intervals; (iii) the quality status for the gas sensor data301and weather data304that remain frozen at for example, a non-zero value for more than a pre-defined period of time; and (iv) the quality status for the gas sensor data301and weather data304that do not send a new value update from a gas sensor110or weather station120within a predefined period of time. The validated gas sensor and weather station data is stored in historian170in operation312for later retrieval by the aggregation workflow ofFIG.4.

FIG.4illustrates the initial data aggregation workflow400of the method of the present disclosure. The server160fetches the validated data stored in historian170by the initial data collection and pre-processing workflow300. The valid weather data404consists of: windspeed; windspeed data quality; wind direction; wind direction data quality; temperature; temperature data quality; humidity; humidity data quality; pressure; pressure data quality; for each weather station120together with the weather stations location such as the latitude, longitude, and altitude of the weather station.

The valid gas sensor data404from the historian170is sampled in operation406for 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 operation408the weather data401and sensor data404are 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 stations120are available, the values of the gas sensors data401are paired with the geographically nearest valid wind direction and wind speed data that is nearest to each gas sensor120in operation410.

In operation412a moving time window is defined based on the wind speed for each gas sensor110. 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 operation414to 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 operation414is 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 operation416. 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 row1of 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 row1of 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 operation418for 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 ofFIG.4. The method of the present disclosure uses a geospatial triangulation workflow to manifest a cone shaped area for each sensor location for every wind direction bin with a non-zero accumulation of gas data readings. With references toFIGS.5-7the geospatial triangulation method500of the disclosure is shown. Depending on whether there is significant height change across the potential gas emission locations and different sensor positions, either a two-dimensional planar grid or three-dimensional array of volumes is defined in order to describe potential emission locations within the facility. The individual grid/volume resolution is configurable in operation506which develops a plurality of conical projections610denoted by boundaries615illustrated inFIG.6. The conical projections are then displayed on a display monitor of the operator station165.

A two-dimensional planar grid composed of grid cells will be used to in explaining the cone projections shown inFIG.6. For each conical projection610shown inFIG.6, the workflow ofFIG.5computes in operation508intersections for the cone projections with all the plant/site grid cells. In each case, the aggregated gas sensor data readings are added to an accumulated gas concentration value within each grid cell based on the area of intersection. Any element wholly within the cone receives a full allocation. Partial intersections receive an allocation based on the area of overlap. InFIG.6, the accumulated gas concentration values in each grid cell are illustrated by the degree of shading concentration. On a colour display monitor of an operator station for example, the degree of shading may be illustrated to a user using concentrations of a specific colour to show the accumulated gas concentrations.

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 operation510the 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 operation512into 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 operation514.

Finally, the grid cells in each agglomerated area are reduced to those with the greatest, for example top 20% gas concentration in operation518and a boundary730shown inFIG.7is drawn around each set of filtered cells. In some cases, this results in two or more distinct sub-groups of grid cells and are delineated by the grid cells that fall below the threshold of gas concentration.

For each newly identified leak area identified based on the last historical window of data of the workflow ofFIG.5, a match is sought with a previously identified leak area from prior data sets stored in the historian170. This matching process is primarily based on heuristics including: (i) if there is a significant area of overlap (as defined by a % limit) between a newly identified leak area and an existing leak area, then the new leak is matched with the old leak and the existing leak area is updated based on a weighted combination of the intersection and the union of the two areas; (ii) if the new leak area significantly overlaps multiple existing leak areas, then the existing leak areas are combined otherwise, the newly identified leak is assigned a new leak id and added to the list of existing leak areas; and (iii) leak areas that have not been observed for a configurable period of time are not reported to the leak detection method. Their location is persisted for another configurable period of time. A centroid750is then calculated for the leak area source based on the shape and level of gas estimated for each grid cell area.

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.

FIG.8illustrates the leak size quantification workflow method800used by the present disclosure. An initial estimate of the leak rate (for each leak area) is calculated based on a simplified form of the gas dispersion model described by Turner (Turner, D. B. 1970 Workbook of Atmospheric Dispersion Estimates, AP-26. Research Triangle Park, North Carolina: Environmental Protection Agency, Office of Air programs). This requires determining in operation805one or more sets of peak gas measurements, estimated straight-line distances from the sensors and the leak area centroids for the matching wind direction bins, and a maximum gas reading is calculated for each sensor location and wind direction bin. For the cases where the wind direction bin aligns with the direction of the gas flow from the source to the sensor (for non-zero gas levels), the gas level and sensor to source distance is recorded.

The simplified equation is then solved in operation810for 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:c(x,y,z)=mean (time average) concentration of diffusing substance at a point (x,y,z) [kg/m3]x=downwind distance [m]y=crosswind distance [m]z=vertical distance above ground [m]Q=gas (contaminant) emission rate [kg/s]σy=lateral dispersion coefficient function [m]σz=vertical dispersion coefficient function [m]U=mean wind velocity in downwind direction [m/s]H=effective leak source or stack height [m] (which is the actual height but can be adjusted for plume rise for buoyancy and thermal effects)

The crosswind distance (y) and vertical distance terms (z, H) are ignored in the simplified Equation 2 below for a horizontal straight-line assumption.

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:Extremely UnstableModerately UnstableSlightly UnstableNeutralSlightly Stable
As a result, Equation 3 can be simplified by 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 operation815ofFIG.8, each leak source initial estimate is used with a full Gaussian dispersion model and a complete data set to estimate the leak rate. A simplifying assumption is made that a fixed wind stability condition (mid-range in the meteorologic wind stability conditions) can be used to compute a base value of σz, σy for any given x,y,z distance from the leak source, namely σzb, σyb. A continuous model parameter k is then determined by the method to calculate actual values of σz, σy which gives the best fit of the model to the observed sensor readings, while estimating the leak rate, for example:

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.

Error⁢squared⁢for⁢single⁢observation:(Cm-Q⁡(e?+e?)⁢e?2⁢Uk2⁢πσ⁢yb⁢σ⁢zb)2Equation⁢8?indicates text missing or illegible when filed

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.

θn+1=θn-α⁢1m⁢∑i=1m⁢∂?∂θEquation⁢9?indicates text missing or illegible when filed

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.