Computer system and method for estimating changes in fugitive emissions

Abstract

Computer systems, methods, and apparatuses for estimating changes in gas emissions are described. A computer system can monitor overall emission levels based on sensor output from a plurality of gas sensors in a facility. The computer system can estimate a total emission level over a time interval based on accumulated gas response factor weighted detections of the plurality of gas sensors. Emissions in maintenance activities can be excluded as appropriate. The total emission level can be compared to estimated total emission levels for different time intervals and / or different facilities. The computer system can also be used to compare emissions across multiple facilities or to compare emissions of multiple facilities across multiple regions.

Description

[0001] Cross Reference to Related Applications

[0002] This application claims the benefit of priority of U.S. provisional application US 62 / 938972, filed November 22, 2019. The above cited application is hereby incorporated by reference in its entirety. TECHNICAL FIELD

[0003] Aspects described herein relate generally to gas detection systems and more particularly to monitoring fugitive gas emissions. Aspects of the present disclosure relate to an intelligent digital platform for collection, analysis and appropriate information making for fugitive gas emissions identified by a sensor network based emission monitoring system in a facility. BACKGROUND

[0004] Concern for clean living, working and industrial environments has increased in recent decades. As part of a leak detection and repair (LDAR) program, the United States Environmental Protection Agency (EPA) promulgated Method 21 to determine and limit fugitive emissions of gases from industrial facilities, such as oil refineries, chemical manufacturing facilities, etc. Fugitive gases can include, but are not limited to, volatile organic compounds (VOCs) and volatile hazardous air pollutants (VHAPs). Such LDAR programs are widely adopted in the United States.

[0005] The EPA has specified techniques for measuring / estimating / monitoring fugitive emissions in a document entitled "Protocol for Equipment Leak Survey Emission Estimates" published in November 1995 as EPA-453 / R-95-017 (available online at https: / / www3.epa.gov / ttnchie1 / efdocs / equipiiks.pdf). Generally, an industrial facility must employ a portable gas monitoring equipment, such as a VOC analyzer, to conduct manual Method 21 prescribed inspections at individual components of the facility and record the highest measured value for each component. Correlation factors prescribed by the EPA are then applied to the measured values to approximate the total emissions for the facility.

[0006] In the performance of EPA Method 21, an inspector at the time of testing places an extractive hand-held probe in direct contact with the component and follows the perimeter of the component, waiting an appropriate amount of time to register a reading of leak concentration (mixing ratio of flammable parts). If the highest concentration reading is above a control limit (typically 500-2000 ppm), the component is tagged for repair. The concentrations determined by EPA Method 21 are sometimes used to approximate mass flow rates by correlation equations to estimate annual emission leak rates for the facility - a procedure with several sources of uncertainty. It is well known that manual leak detection methods of monitoring and repairing sources of fugitive emissions are resource intensive and difficult to apply to inaccessible sources. Additionally, EPA Method 21 is expensive to perform and poses safety concerns for the inspector. This manual inspection procedure only surveys a subset of potential emission points within a facility and has a high time delay because some components can not be visited for over a year, resulting in potential leaks undetected for long periods of time.

[0007] Many LDAR programs rely heavily on EPA Method 21. However, as noted above, Method 21 has many deficiencies, including: (a) heavy reliance on manual inspection with a portable instrument; (b) extreme inefficiency (e.g., only a small percentage of all inspected components can have active leaks); (c) safety concerns associated with manual measurements (e.g., technicians can have to climb towers, can be exposed to hostile conditions such as high temperatures, and / or can need to access hard-to-reach components); (d) high labor costs; and (e) long time periods between LDAR cycles (e.g., during which some large leaks can remain undetected). For example, due to infrequent monitoring programs, some large leaks can not be detected in a timely manner, and thus, total emission estimates can be inaccurate. SUMMARY

[0008] In the following description of various illustrative embodiments, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration various embodiments in which aspects of the disclosure can be practiced. It is to be understood that other embodiments can be utilized and structural and functional modifications can be made without departing from the scope of the present disclosure. It is further noted that various connections are discussed in the following description that can be made directly or indirectly, wired or wireless, and that the specification is not intended to limit in any way the manner in which the disclosed embodiments can be implemented. It is to be understood that other specific arrangements can be utilized and structural and functional modifications can be made without departing from the scope of the present disclosure.

[0009] One or more computer systems can be configured, by virtue of having installed on the system software, firmware, hardware, or a combination thereof that in operation cause the system to perform particular operations or actions, the system performing the specific operations or actions. One or more computer programs can be configured, by virtue of including instructions that when executed by data processing apparatus, cause the apparatus to perform the operations or actions. One general aspect includes a system for estimating changes in overall fugitive emission levels at an industrial facility equipped with a plurality of sensors. The system further includes an emission monitoring platform that can include at least one first processor and a first memory storing first computer-readable instructions that, when executed by the at least one first processor, cause the emission monitoring platform to: select, based on a gas stream measured by the plurality of sensors, a response factor for the plurality of sensors using an algorithm disclosed herein; receive, from the plurality of sensors, sensor outputs associated with gas plume stream detections over a time interval; determine an emission indicator value based on detection events in a sensor output associated with a sensor of the plurality of sensors, wherein the determining excludes those detection events corresponding to a maintenance activity; determine a total emission indicator value for the industrial facility over the time interval based on corresponding emission indicator values for the plurality of sensors and corresponding response factors; determine a difference between the total emission indicator value for the industrial facility over the time interval and a second total emission indicator value for the facility over a second time interval; and send an indication of the difference. The system further includes a user computing device configured to display the indication of the difference on a display device coupled to the user computing device. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0010] Implementations can include one or more of the following features. In the system, the first memory stores first computer-readable instructions that, when executed by the at least one first processor, cause the emissions monitoring platform to: determine, for the sensor of the plurality of sensors, the detection event based on the sensor output and a modeled baseline value corresponding to the sensor output. The first memory stores first computer-readable instructions that, when executed by the at least one first processor, cause the emissions monitoring platform to: determine a detection event at a first time (T1) in the detection event based on a difference between the modeled baseline value at the first time and a value of a sensor detection peak at the first time based on the sensor output exceeding a threshold value. The first memory stores first computer-readable instructions that, when executed by the at least one first processor, cause the emissions monitoring platform to: determine, for each sensor, a signal-to-noise ratio, wherein the threshold value is a multiple of the signal-to-noise ratio. The first memory stores first computer-readable instructions that, when executed by the at least one first processor, cause the emissions monitoring platform to: determine the emissions indicator value for the sensor based on aggregating peak areas associated with the detection event in the sensor output. The first memory stores first computer-readable instructions that, when executed by the at least one first processor, cause the emissions monitoring platform to: determine the emissions indicator value for the sensor based on aggregating peak heights associated with the detection event in the sensor output. The location tracking device is configured to: send an indication of a location of the location tracking device and a timestamp associated with the location to the emissions monitoring platform; wherein the first memory stores first computer-readable instructions that, when executed by the at least one first processor, cause the emissions monitoring platform to: determine the sensor based on the location, determine one or more detection events in the sensor output based on the timestamp. The first computer-readable instructions, when executed by the at least one first processor, cause the emissions monitoring platform to: receive, from the user computing device, an indication of a maintenance time interval and a location associated with the maintenance activity; determine the sensor based on the location, determine one or more detection events in the sensor output based on the time interval.When the first computer-readable instructions are executed by the at least one first processor, the first computer-readable instructions cause the emissions monitoring platform to: determine a third total emissions indicator value for a second facility over the time interval; determine a second difference between the total emissions indicator value and the third total emissions indicator value; and send an indication of the second difference to the user computing device; and wherein the user computing device is configured to display the total emissions indicator value on the display device; one or more of the third total emissions indicator value and the second difference. The plurality of sensors can include at least one sensor selected from the group consisting of an electrochemical sensor, an infrared sensor, a catalytic bead sensor, a metal oxide semiconductor sensor, a photoionization detector, a flame ionization detector, a thermal conductivity sensor, a colorimetric sensor, and combinations thereof. The determining the total emissions indicator value for the industrial facility can include determining a sum of products of corresponding emissions indicator values and corresponding response factors for the plurality of sensors. Implementation of the described techniques can include hardware, a method or process, or computer software on a computer-accessible medium.

[0011] One general aspect includes a method for estimating a change in an overall fugitive emissions level at an industrial facility. The method further includes receiving, at an emissions monitoring platform, sensor outputs from a plurality of sensors associated with plume detection over a time interval. The method further includes determining, for a sensor of the plurality of sensors, one or more emissions values based on detection events in a sensor output associated with the sensor; and determining a response factor for a gas plume based on a composition of the gas plume and a response factor for a species of any gas in the gas plume. The method further includes determining a total emissions value for the facility over the time interval based on the one or more emissions values and corresponding response factors for the plurality of sensors. The method further includes determining a difference between the total emissions value for the facility over the time interval and a second total emissions value for the facility over a second time interval. The method further includes sending an indication of the difference to a user computing device. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0012] One general aspect includes a non-transitory computer-readable medium storing instructions that include receiving sensor outputs associated with gas concentration measurements over a time interval from a plurality of sensors in a facility. The instructions also include determining, for a sensor of the plurality of sensors, an emission value based on a plurality of detection events in a sensor output associated with the sensor, wherein the plurality of detection events excludes one or more detection events corresponding to a maintenance activity in the sensor output, and determining a response factor for a gas stream based on a composition of the gas stream and response factors for species of any gases in the gas stream. The instructions also include determining a total emission value for the facility over the time interval based on corresponding emission values and corresponding response factors for the plurality of sensors. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0013] Implementations can include one or more of the following features. The non-transitory computer-readable medium also stores instructions that, when executed, cause determining a difference between the total emission value for the facility over the time interval and a second total emission value for the facility over a second time interval, and / or sending an indication of the difference to a user computing device. Implementations of the described technology can include hardware, a method or process, or computer software on a computer-accessible medium.

[0014] One basic aspect includes an emissions monitoring platform configured to estimate changes in overall fugitive emissions levels at an industrial facility. The emissions monitoring platform can include at least one first processor and a first memory storing first computer-readable instructions that, when executed by the at least one first processor, cause the emissions monitoring platform to: select a response factor for a gas stream at the industrial facility based on at least one gas species measured by a plurality of sensors at the industrial facility; receive sensor outputs associated with gas plume detection from the plurality of sensors over a time interval; determine an emissions indicator value based on a detection event in a sensor output associated with a sensor of the plurality of sensors; determine a total emissions indicator value for the industrial facility over the time interval based on corresponding emissions indicator values and corresponding response factors for the plurality of sensors; and send the total emissions indicator value. The system also includes a user computing device configured to output the total emissions indicator value on a display device coupled to the user computing device. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0015] One broad aspect includes a method comprising: receiving, at an emissions monitoring platform, sensor outputs from a plurality of sensors associated with detection of a plume of gas emissions at an industrial facility over a time interval. The method further includes determining, for a sensor of the plurality of sensors, one or more emission indicator values based on detected events in a sensor output associated with the sensor; and determining a response factor for a plume of gas based on a composition of the plume of gas and response factors for any gases in the plume of gas. The method further includes determining maintenance activities occurring at the industrial facility during the time interval. The method further includes excluding, from the one or more emission indicator values, those detected events corresponding to the maintenance activities. The method further includes determining, after the excluding step, a total emission indicator value for the industrial facility over the time interval based on the one or more emission indicator values for the plurality of sensors and corresponding response factors. The method further includes determining a difference between the total emission indicator value for the industrial facility over the time interval and a second total emission indicator value for the industrial facility over a second time interval. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods. Implementations can include one or more of sending an indication of the difference to a user computing device. Implementations of the described techniques can include hardware, a method or process, or computer software on a computer-accessible medium.

[0016] One general aspect includes a method for comparing overall fugitive emission levels at a first facility and at a second facility. The method also includes, for the first facility: selecting a response factor for a gas plume based on at least one gas species measured by a plurality of sensors at the first facility; receiving sensor outputs from the plurality of sensors associated with gas plume detection over a time interval; determining an emission indicator value based on detection events in a sensor output associated with a sensor of the plurality of sensors; and determining a total emission indicator value for the first facility over the time interval based on corresponding emission indicator values for the plurality of sensors and corresponding response factors. The method also includes, for the second facility: selecting a response factor for a gas plume based on at least one gas species measured by a plurality of sensors at the second facility; receiving sensor outputs from the plurality of sensors associated with gas plume detection over a time interval; determining an emission indicator value based on detection events in a sensor output associated with a sensor of the plurality of sensors; and determining a total emission indicator value for the second facility over the time interval based on corresponding emission indicator values for the plurality of sensors and corresponding response factors. The method also includes determining a difference between the total emission value for the first facility over the time interval and the total emission value for the second facility over the time interval. The method also includes sending an indication of the difference to a user computing device for display by a display device. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0017] Methods, apparatus, and systems are described for monitoring gas emissions in a facility. An emission monitoring system can monitor overall emission levels based on sensor outputs from a plurality of gas sensors in a facility. The emission monitoring system can estimate a total emission level over a time interval based on cumulative gas response factor weighted detections of the plurality of gas sensors. Emissions in maintenance activities can be excluded as appropriate. The total emission level is compared to total emission levels estimated for different time intervals and / or different facilities. The emission monitoring system can also be used to compare emissions across multiple facilities or to compare multiple facilities across multiple regions.

[0018] For example, features disclosed herein contemplate a system for comparing rates of fugitive emissions at a first unit and at a second unit at one or more industrial facilities, where each of the one or more facilities is equipped with a plurality of sensors. The system can include one or more emission monitoring platforms and one or more user computing devices. In one embodiment, an emission monitoring platform includes at least one first processor and a first memory storing first computer-readable instructions that, when executed by the at least one first processor, cause the emission monitoring platform to perform a method including: (i) selecting a response factor for a gas stream based on at least one gas species in the gas stream measured by the plurality of sensors; (ii) receiving sensor outputs from the plurality of sensors associated with gas plume detection over a time interval; (iii) determining an emission indicator value based on a plurality of detection events in a sensor output associated with a sensor of the plurality of sensors, where the determining excludes those detection events corresponding to a maintenance activity; (iv) determining a total emission indicator value for the industrial facility over the time interval based on corresponding emission indicator values for the plurality of sensors and corresponding response factors; (v) determining a difference between the total emission indicator value for the industrial facility over the time interval and a second total emission indicator value for the facility over a second time interval; and / or (vi) transmitting an indication of the difference. One or more of steps (i)-(vi) can be optional and / or performed in a different order from the numbering. The one or more user computing devices can be configured to display the indication of the difference transmitted in step (vi) on a display device coupled to a user computing device.

[0019] In another example of the foregoing system involving comparing rates of fugitive emissions at a first unit and at a second unit at one or more industrial facilities, the system can also include one or more emission monitoring platforms and optionally one or more user computing devices. The one or more emission monitoring platforms can perform a method for the first unit including one or more steps of: (i) selecting a response factor for a gas stream based on at least one gas species measured by a plurality of sensors at the first unit; (ii) receiving sensor outputs from the plurality of sensors at the first unit associated with gas plume detection over a time interval; (iii) determining an emission indicator value based on a detection event in a sensor output associated with a sensor of the plurality of sensors at the first unit; and / or (iv) determining an average emission indicator value for the first unit over the time interval based on corresponding emission indicator values for the plurality of sensors and corresponding response factors. One or more of steps (i)-(iv) can be optional and / or performed in a different order than the numbering, as appropriate. Likewise, the same or a different one of the one or more emission monitoring platforms can perform the same method including one or more of the same foregoing steps (i)-(iv) for the second unit, but with a plurality of sensors at the second unit and based on a gas stream at least at the second unit. The system can then determine a difference between the average emission value for the first unit over the time interval and the average emission value for the second unit over the time interval. Further, an indication of the difference is then sent to an optional user computing device for display or other output by a display device. BRIEF DESCRIPTION OF DRAWINGS

[0020] The present disclosure is illustrated by way of example and not limitation in the figures of which like reference numerals refer to similar elements and in which:

[0021] Figure 1A and Figure 1B FIG. 1 (collectively, “FIG. 1”) is an illustration of a representative facility with a sensor network in accordance with various aspects of the present disclosure;

[0022] Figure 2 FIG. 2 shows a block diagram of an exemplary embodiment of a sensor network-based emission monitoring system in accordance with various aspects of the present disclosure;

[0023] Figure 3 FIG. 4 shows an exemplary embodiment of a plurality of examples of ionization potentials and response factors for some common gas species in accordance with various aspects of the present disclosure;

[0024] Figure 4 FIG. 1 shows output of a sensor over a time period, in accordance with various aspects of the present disclosure;

[0025] Figure 5A and Figure 5B FIG. 5 (collectively) shows an exemplary identification of a peak and a determination of a peak area based on output of a sensor, in accordance with various aspects of the present disclosure;

[0026] Figure 6A and Figure 6B FIG. 6 shows an exemplary operation of an emissions monitoring system, in accordance with various aspects of the present disclosure;

[0027] Figure 7 shows an exemplary GUI that can be displayed on a user computing device, in accordance with various aspects of the present disclosure;

[0028] Figure 8 shows exemplary sensor output observed over a time interval, in accordance with various aspects of the present disclosure;

[0029] Figure 9 shows an exemplary maintenance dashboard, in accordance with various aspects of the present disclosure;

[0030] Figure 10 is a flowchart of a method of monitoring fugitive gas emissions, in accordance with various aspects of the present disclosure; and

[0031] Figure 11 is a flowchart of another method of monitoring fugitive gas emissions, in accordance with various aspects of the present disclosure.

[0032] In the following description of various illustrative embodiments, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration various embodiments in which aspects of the present disclosure can be practiced. It is to be understood that other embodiments can be utilized and structural and functional modifications can be made without departing from the scope of the present disclosure. It is further noted that various connections are discussed in the following description that can be made directly or indirectly, wired or wireless, and that the specification is not intended to limit in this respect. DETAILED DESCRIPTION

[0033] The present disclosure describes many embodiments involving a monitoring system that collects, analyzes, and / or makes appropriate information about fugitive emissions identified by a network of sensors in one or more facilities. Various examples herein describe a network of sensors for gas detection and an emissions monitoring platform in communication with the network of sensors. In one example, the emissions monitoring platform can include a processor, a memory, and / or a communication interface. The processor can process and analyze data stored by the memory. In some embodiments, the memory can store computer-executable instructions that, when executed by the processor, cause an emissions monitoring platform to perform one or more of the steps disclosed herein. In some embodiments, the emissions monitoring platform can generate an emissions report based on values received through the communication interface, such as from one or more sensors. The emissions report can provide an estimate of fugitive emissions during a particular time interval. Additionally or alternatively, the emissions report can provide a comparison between total emissions estimated over multiple different time intervals and / or in multiple different facilities.

[0034] The emissions monitoring platform can output a graphical user interface (GUI) on a screen display. The emissions monitoring platform can analyze, filter, and transform collected sensor data into a visual output that can be made on a GUI on a screen display. In some embodiments, the emissions monitoring platform can include a desktop computer, a smartphone, a wireless device, a tablet, a laptop, etc. The emissions monitoring platform can be physically located on-site or remotely and can be connected to other devices / systems associated with a facility through one or more communication links.

[0035] Other embodiments are also disclosed herein relating to derivations and combinations of various method steps and system components disclosed herein. While the present disclosure can be susceptible to embodiment in different forms, specific embodiments are shown and described in the drawings and are herein detailed with the understanding that the disclosure is to be considered an exemplification of the principles of the disclosure and is not intended to limit the disclosure to that as illustrated and described herein. Thus, unless otherwise stated, the features disclosed herein can be combined in additional combinations that are not shown for purposes of brevity. It will also be understood that one or more elements shown in the drawings by way of example can be removed and / or replaced by alternative elements in the scope of the disclosure in some embodiments.

[0036] Remote detection of gas plumes resulting from component leaks provides an innovative way to monitor fugitive emissions of VOC compounds - faster and more effective in detecting large leaks and controlling total emissions from a plant. Thus, there is an effort to develop a sensor network based emissions monitoring system configured to detect plumes of VOCs or other gases of interest within the boundaries of a facility. The monitoring system generally includes sensor nodes placed throughout the facility, a meteorological station, and a data analysis and visualization platform. Multiple sensors are installed at fixed locations throughout the process units and in wireless communication with a central data platform in the cloud, which estimates leak locations by analyzing data with site-specific algorithms. With proper sensor placement, a system can quickly detect VOC leaks as low as a few grams / hour up to 60 feet away from the leak.

[0037] Figure 1A A representative facility 100 with a wireless (or wired) sensor network is shown in a sample two-dimensional layout. Facility 100 can include multiple LDAR components such as process units, buildings, etc. (e.g. Figure 1A depicted units 1-6, tank farm, terminal). Multiple gas sensors in the sensor network can be placed in the facility in an optimized manner to provide full (or at least substantially full) three-dimensional (3D) detection coverage of the multiple LDAR components in the facility, as shown in Figure 1B Figure 1B With sensors 102 shown as pentagons in , the higher the sensor density, the better the leak detection results that can be provided or even allow a dynamic plume profile to be constructed in a 3D space.

[0038] A sensor network can provide a simpler and more reliable solution for conducting LDAR and for monitoring total emission levels. Sensors of a network can be used to detect small plume streams created by gas leaks and wind sensors can be used to help triangulate sensor detections to the source of the leak. Multiple sensor nodes can be designed and distributed evenly to provide full coverage of an industrial facility. The sensor system can operate continuously and be able to detect large leaks from individual components in a timely manner, such as within hours / days compared to months to years between scheduled inspections based on method 21. In addition, the sensor system can be able to detect a collection of small leaks from multiple closely located components even if it cannot detect individual small leaks from individual components. A collection of small leaks can be identified by the sensor system as a large leak within an area, such as an area where multiple components can be closely located. A component-based approach for estimating / monitoring fugitive emissions for each component in an industrial facility that is measured independently and manually can no longer be necessary.

[0039] A detection zone of a sensor can be represented by a point representing the location of the sensor and a circle representing the zone within which the sensor is able to detect a gas plume. For simplicity, in some examples, the detection zone of each sensor can be represented by a circle (or a sphere in three dimensions). However, in other examples, the detection zone can be modified to accommodate one or more structures, obstacles, and / or openings in the facility. For example, under a three-dimensional digital representation, the height of an obstacle structure can have a direct bearing on the placement of a sensor, specifically whether the height of a structure can be futile for a sensor placed at a location to detect a gas plume originating from the opposite side of the obstacle structure. In addition, the detection zone of a sensor can be affected by the type of sensor used, the sensitivity of the sensor to a particular gas compound, etc.

[0040] A wide variety of sensor technologies can be used for gas detection in the various examples described herein. Gas sensors can include electrochemical sensors, infrared sensors, catalytic bead sensors, metal oxide semiconductor (MOS) sensors, photo ionization detectors (PIDs), flame ionization detectors (FIDs), thermal conductivity sensors, colorimetric sensors, sensors based on passive sampling technology, and / or any other sensor configured to measure the concentration of VOCs and / or other hazardous gases.

[0041] Open-air detection of gases requires high sensitivity (typically at parts-per-billion (ppb) levels of concentration) and fast response times (e.g., due to possible wind and changes in wind speed and direction). Several sensor technologies such as MOS and PID meet this requirement and can be used in fence-line and outdoor air quality monitoring applications.

[0042] A PID is equipped with a high-energy ultraviolet (UV) lamp and electrodes. Gas molecules with low ionization energy that enter a UV chamber in a PID are ionized. The flow of the resulting ions toward a collection electrode produces a current that is proportional to the concentration of the gas. Depending on the target gas to be measured, a PID can employ a 9.6 eV, 10.0 eV, 10.2 eV, 10.6 eV, or 11.7 eV lamp. The higher the lamp energy, the more gas species can be measured. A lower energy lamp can be preferred for the measurement of aromatic compounds (such as benzene) because aromatic compounds are more specific.

[0043] Gas sensors have varying sensitivities for different gas species and sometimes need to be properly calibrated before use. A known concentration of a surrogate gas can be used to calibrate the sensor. For measuring other gases, a cross-sensitivity factor, called a response factor, can be used to correct a sensor output to provide a measurement. For example, isobutylene is typically used to calibrate PIDs because of its moderate sensitivity and low toxicity. When measuring isobutylene concentration, a calibrated PID can directly provide a measurement of the concentration. For other gases, a response factor can be employed (e.g., by the emissions monitoring platform 260) to determine a concentration based on the measurement provided by the isobutylene-calibrated PID.

[0044] Figure 3 Examples of ionization potentials and response factors are shown for some common gas species. Figure 3 The values in Table 1 are one example of data based on publicly available data from PID manufacturer Honeywell; other manufacturers can provide their own data for PIDs, FIDs, MOS, or other sensors. Figure 3 The example response factors shown can correspond to a PID that is calibrated with isobutylene and employs a 10.6 eV UV lamp. The emissions monitoring platform 260 can determine a true gas concentration by scaling a sensor output with the response factor (F) for that gas.

[0045] Gas concentration = sensor output x response factor (F)

[0046] Equation (1)

[0047] For example, if a sensor is calibrated with isobutylene and used to measure isobutylene (isobutylene has a response factor of 1), then the concentration of isobutylene is considered the same as the sensor reading. If the sensor is used to measure n-octane (n-octane has a response factor value of 1.8) and the sensor output corresponds to a concentration of 10 ppm, then the actual concentration of n-octane is 10 ppm x 1.8 = 18 ppm. If the sensor is used to measure benzene (benzene has a response factor value of 0.53) and the sensor output corresponds to a concentration of 1 ppm, then the actual concentration of benzene is 1 ppm x 0.53 = 0.53 ppm. Based on Equation 1, a lower response factor for a gas means that the sensor is more sensitive to that gas. Conversely, a higher response factor for a gas means that the sensor is less sensitive to that gas. For example, a sensor with a response factor given by Figure 3 a sensor is more sensitive to isopropanol than butane.

[0048] Many sensor manufacturers set the response factors for different compounds in an instrument built-in software library. When the appropriate response factor is called up (such as via a user input), the sensor can determine a true concentration of a compound based on the sensor output. Additionally or alternatively, the emissions monitoring platform 260 can retrieve a response factor to be used for a sensor (such as from the data store 290) (such as based on the location of the sensor and / or the gases likely to be present in the detection zone of the sensor) and determine a true concentration based on the received sensor output.

[0049] For PID sensors, a gas must have an ionization potential below the output energy of the UV lamp to be detected. For example, a sensor with a response factor corresponding to Figure 3 formaldehyde can not be detected because the ionization potential of formaldehyde is above the output energy of the UV lamp (10.6 eV).

[0050] In an industrial facility, gases can often exist in the form of mixtures. For a gas mixture with known components, an overall response factor F Overall can be calculated based on the response factors of the sensor for each component in the mixture. For example, if a gas mixture to be measured by a sensor has n gases, the emissions monitoring platform 260 can determine F Overall as:

[0051]

[0052] where X1-Xn are the molar ratios of the various species in the gas mixture, and F1-Fn are the response factors of the sensor for the various species in the gas mixture. The molar ratios of the various species of the gas mixture can be retrieved from the data store 290, and / or can be input by a user (such as via the user computing device 285). The emissions monitoring platform 260 can determine F Overall The response factors can be retrieved from the data store 290. The term "gas mixture" as used herein in connection with an industrial facility also contemplates the transmission of a gas stream composed of only one gas or more than one gas.

[0053] Additionally or alternatively, the emissions monitoring platform 260 can retrieve (such as from the data store 290) an overall response factor for different sensors. For example, each of the plurality of sensors can be associated with a corresponding overall response factor F Overall The emissions monitoring platform 260 can determine an overall response factor for a sensor based on a location of the sensor and / or the gases / gas mixtures that can be present within the detection zone of the sensor.

[0054] Based on the sensor output, the emissions monitoring platform 260 can determine the concentration of a true gas of the gas stream / mixture as:

[0055] Gas mixture concentration = Sensor output x F Overall

[0056] Equation (3)

[0057] For example, a binary gas stream / mixture of benzene and n-octane in equal molar ratios can have an overall response factor F Overall of 1 / (0.5 / 0.53 + 0.5 / 1.8) = 0.82. A sensor output of 100 ppm would then correspond to an actual concentration of 82 ppm of the entire mixture (such as composed of 41 ppm of benzene and 41 ppm of n-octane).

[0058] For unknown gases or gas mixtures, a sensor can not be able to apply an appropriate factor or calculate an actual concentration. In such cases, the sensor output can be considered an "isobutylene equivalent" response.

[0059] Figure 4 A sensor output is shown over a period of about 15 minutes. Each peak in the sensor output can be classified as a sensor detection event and can be a representation of a plume flow detection. As Figure 4As shown, a plurality of peaks (P1-P22) can be identified over a time period (such as a time interval ΔΤ). While peak height detected for each peak provides a good measure of signal strength, a peak area can be a better measure of the size or emission of a plume of smoke passing the sensor corresponding to a sensor detection event.

[0060] Figure 5A And Figure 5B An exemplary identification of a peak and determination of a peak area based on a sensor's output (such as determined by the emission monitoring platform 260) is shown. Figure 5A An exemplary sensor output over a time period is shown. The emission monitoring platform 260 can employ a curve fitting model based on the sensor output to determine a modeled baseline curve.

[0061] The emission monitoring platform 260 can identify peaks in the determined sensor output over time. A peak can be identified when a sensor output value exceeds a modeled baseline value (such as by a threshold value). Figure 5B An exemplary identification of a peak in the sensor output is shown. The emission monitoring platform 260 can identify a peak at time T1, for example, if a difference between a sensor output value (C measured ) at time T1 and a modeled baseline value (C baseline ) at time T1 is greater than a threshold value.

[0062] The emission monitoring platform 260 can determine a signal-to-noise (S / N) ratio based on the sensor output over a time period. The threshold value can be a multiple (such as two, three, four, twelve, or other value) of the determined S / N ratio. For example, the emission monitoring platform 260 can identify a peak at time T1 if:

[0063] C measured - C baseline ≥ n x S / N ratio

[0064] Equation (4)

[0065] where n can be any suitable value (such as n = 1, 2, 3, etc.), C measured may be a sensor output value at time T1, and C baseline may be a value of the modeled baseline curve at time T1. The emission monitoring platform 260 can also determine a peak area (s) corresponding to the peak by determining an area under the detected peak and above a modeled baseline.

[0066] The emission monitoring platform 260 can determine an emission indicator value corresponding to a sensor for a time interval. The emission indicator value can be determined based on a determined peak in the sensor output values (as observed for the time interval) and a peak area associated with the determined peak. Referring to Figure 4 , an emission indicator value S i may be determined based on a plurality of peak areas corresponding to a plurality of peaks of individuals for the time interval AT. For example, if m peaks are detected by the sensor for the time interval AT, the emission monitoring platform 260 can determine an emission indicator value for the time interval AT as:

[0067]

[0068] where s k is a peak area for a detected peak k.

[0069] Throughout this application, the theme concept of many gas sensors is introduced and the innovative algorithm for comparing overall emissions or assessing changes in overall emissions is illustrated. Based on the detection events and in particular the sensor detections for a specific time interval and the corresponding response factors in the archived data, a total emission indicator value for an industrial facility or for a specific unit or units in a facility can be calculated by the following algorithm:

[0070]

[0071] where S i is an accumulative emission detection value from sensor i for the time interval and F i is an overall response factor for the gas stream at sensor i, where the determination excludes those detection events corresponding to a maintenance activity.

[0072] A plurality of emission indicator values corresponding to a plurality of sensors can be aggregated to generate an estimate of the emissions over an entire facility. Furthermore, the facility can be associated with a plurality of gases or gas mixtures. A gas stream at a particular location can be known and a response factor / overall response factor for a sensor at the location can be determined accordingly (such as based on Figure 3 or equation (2)). The emission monitoring platform 260 can determine a total emission indicator value from all sensors based on a plurality of individual emission indicator values from a plurality of sensors and corresponding response factors / overall response factors associated with the plurality of sensors. For example, the emission monitoring platform 260 can determine a total emission indicator value (for a time interval AT) at a facility with n sensors as:

[0073]

[0074]

[0075] where S i is an accumulated emission indicator value (for time interval AT) at sensor i and F i is a response factor or overall response factor for sensor i. Additionally or alternatively, the emission monitoring platform 260 can determine an average emission indicator value for a plurality of sensors as:

[0076]

[0077] F i may be based on a particular gas or a mixture of gases measured by sensor i. F i may be based on the gas (gases) processed by a particular component(s) / unit(s) within a detection zone of sensor i. For example, the emission monitoring platform 260 can retrieve (e.g., from data store 290) a value of F i corresponding to a sensor based on the location of the sensor and use the retrieved value to determine D total or D average .

[0078] D total or D average may be used as a general indication of overall emissions in the facility during time interval AT. The higher the value of D total may indicate a higher total emission in the facility during time interval AT. On the other hand, the higher D average may indicate a more "severe" or higher emission rate.

[0079] Consider an exemplary facility with a total of fifteen sensors with uniform distribution of sensors for ethylene and propylene. Five sensors can be installed in a propylene storage area and the remaining sensors can be installed in an ethylene storage area. As shown in Figure 3 the response factors for the sensors for propylene and ethylene can be 1.4 and 9, respectively. The emission monitoring platform 260 can determine a total emission indicator value D total for this facility as:

[0080]

[0081] Consider another exemplary facility having 3 cells with 200 sensors. The overall response factor for the area covered by sensors 1-40 can be 0.8, the overall response factor for the area covered by sensors 41-175 can be 1.5, and the overall response factor for the area covered by sensors 176-200 can be 4. The emissions monitoring platform 260 can determine a total emissions indicator value D total is:

[0082]

[0083] ΔT can be adjusted based on the usage of the emissions monitoring system 255. A long ΔT (such as several months, a year, etc.) can be used to determine the overall emissions level over a long time interval (such as monitoring regulatory compliance) and to reduce the impact of transient spikes / other variations on the emissions level determination. For example, weather conditions can affect sensor detection. If there is a sustained high wind speed, it can be unlikely that the sensors will have large detections because the gas is being diluted by the wind. Using a longer ΔT (such as a month, three months, or a year) and averaging the detected values over the ΔT can minimize these anomalies. On the other hand, a short ΔT (such as on the order of minutes or hours) can be used to estimate emissions in real-time or near real-time and to detect current issues that can need to be addressed.

[0084] Each cell in a facility can be used to process a particular gas or a particular mixture of gases. The emissions monitoring system 255 can be configured to determine not only a total emissions indicator value but also a particular emissions indicator value for an individual gas or gas mixture. To determine a particular emissions indicator value for an individual gas or gas mixture, the emissions monitoring platform 260 can aggregate emissions indicator values from multiple sensors in the corresponding detection area that have cells that process that gas or gas mixture. Returning to the exemplary facility that handles ethylene and propylene with a total of fifteen sensors with five sensors installed in a propylene storage area and the remaining sensors installed in an ethylene storage area, the emissions monitoring platform can determine a particular emissions indicator value for propylene based on the sensor output from the five sensors in the propylene storage area. The emissions monitoring platform 260 can determine a particular emissions indicator value D 丙烯 is:

[0085]

[0086] The data store 290 can store an association between sensors and corresponding gases in the detection area of each of the multiple sensors. The emissions monitoring platform 260 can select sensors for determining a particular emissions indicator value for a gas / gas mixture based on the association.

[0087] The emission levels can be determined, monitored, and compared in functional operation by, for example, a software platform (at the emission monitoring platform 260 and / or the user computing device 285) using a network of sensors (such as the gas sensors 265A). The software platform can present a GUI (such as at the user computing device 285) that can be used to receive input parameters and display an output based on the various calculations described herein.

[0088] The various values determined by the emission monitoring platform 260 can be browsed via a GUI on a display device associated with the user computing device 285. For example, as described herein, the sensors can provide sensor output values to the emission monitoring platform 260. The emission monitoring platform 260 can determine the concentration of the real gas / gas mixture, the emission value, the total / average emission indicator value, and / or the specific emission indicator value based on the sensor output values. The values determined by the emission monitoring platform 260 can be sent to the user computing device 285 and can be browsed via a GUI on a display at the user computing device 285.

[0089] Conventional techniques can rely only on a quantity of leaks detected in a facility (such as a sensor detecting a quantity of leaks) to determine a degree of fugitive gas emissions in the facility. However, this can not necessarily quantify a degree of fugitive gas emissions accurately. The quantitative determination of the total emission level as described herein enables accurate tracking of fugitive gas emissions and enables corrective action to be taken based on the measurements of the sensors. For example, repairs can be prioritized in detection zones of sensors that correspond to reporting high emission values.

[0090] Figure 6A and Figure 6B An exemplary operation of an emission monitoring system is shown. The emission monitoring system can correspond to a facility that includes nine sensors. Figure 6A and Figure 6B Exemplary output values determined for each of the plurality of sensors 605 based on a plurality of measurements by the plurality of sensors 605 are shown. Figure 6A and Figure 6B The plurality of exemplary output values shown can be determined using Equation (7) with a plurality of corresponding response factors or an overall response factor for the plurality of sensors (such as, S i and F i A product). A total emission value for the facility 100 can be equal to an aggregation of the output values for each of the plurality of sensors 605. Figure 6A Exemplary output values determined at time Tl are shown, while Figure 6BAn exemplary output value is shown at time T2.

[0091] As Figure 6A shown, at time Tl, the emissions monitoring platform 260 can detect leaks in more than one of the detection zones of sensor 6 and sensor 9. The emissions monitoring platform 260 can determine output values for sensor 6 and sensor 9 equal to 1 and 2.5, respectively. The output values for the other sensors can be equal to 0. Thus, a total emissions indication value at time Tl can be equal to 3.5. Based on the determination of leaks in the detection zones of sensor 6 and sensor 9, corrective action can be taken. For example, at least one leak in a cell within a detection zone of sensor 9 can be fixed. Based on the higher emissions value reported by sensor 9, the facility can prioritize repairs in the detection zone of sensor 9. For example, the emissions monitoring platform 260 can send an alert to user computing device 285 indicating a potential source of leaks around sensor 9.

[0092] As Figure 6B shown, at a later time T2, the emissions monitoring platform 260 can detect leaks in the detection zones of sensor 3, sensor 6, and sensor 8. Time T2 can be after time Tl and can correspond to a time immediately following the repair of the detection zone of sensor 9. The emissions monitoring platform 260 can determine emissions indication values for sensor 3, sensor 6, and sensor 8 equal to 1, 1.5, and 0.3, respectively. The emissions indication values for the other sensors can be equal to 0. A total emissions indication value at time T2 can thus be equal to 2.8. Based on a comparison between the total emissions indication value at time Tl and the total emissions indication value at time T2, the emissions monitoring platform 260 can accurately determine that the total fugitive gas emissions have decreased in facility 100, even though the number of sensors for which leaks have been detected can have increased. This can provide a better representation of the extent of fugitive gas emissions than a system that simply tracks the number of sensors for which leaks have been detected.

[0093] The emissions monitoring platform 260 can communicate with sensors and / or other emissions monitoring platforms at other facilities (such as via wide area network 275) to perform a comparison between determined emissions indication values / total emissions indication values across multiple facilities. For example, the emissions monitoring 260 can determine total emissions indication values for two different facilities (such as over a time interval), perform a comparison between the two, and provide the results to user computing device 285. User computing device 285 can display (such as in a GUI) the results of the comparison. This can enable a user to determine and compare the performance of various interconnected facilities via a single interface.

[0094] The emissions monitoring platform 260 can be configured to provide a comparison between total emissions indicator values (or average emissions indicator values) determined for two different time intervals. For example, a user can input time intervals for which a comparison is to be performed (such as via the user computing device 285). Based on various calculations performed by the emissions monitoring platform 260 (as described above), the user computing device 285 can provide a GUI displaying a result of the comparison.

[0095] An emissions indicator value can be used in various applications, including but not limited to comparing emissions from different time periods, comparing emissions from different units in a facility, comparing emissions likelihoods based on different unit operating and maintenance procedures, comparing emissions levels using average emissions indicator values for each sensor (which can indicate that a unit or facility is "cleaner" than other units or facilities), and / or combinations thereof. In some examples, government agencies or research organizations can use these data to assess emissions levels in different geographic regions for air quality control and / or climate change research, when emissions from maintenance or emissions permitted are included in the calculations. The present disclosure is not limited to the foregoing examples; one of ordinary skill in the art will recognize after reading the entire content of this document that other use cases stemming from the features disclosed herein can be considered by the present disclosure.

[0096] Figure 7 An example GUI 700 is shown that can be displayed on the user computing device 285 to provide an intuitive interface for comparing and / or assessing emissions. Figure 7 An example of an escape emissions assessment dashboard is shown. On the dashboard GUI, a user can select an assessment method, a facility and / or a unit from a drop down list for comparison and identify a particular time period 710, 720. Results including details are then displayed numerically and / or graphically across the specified time period. Although this analysis can be initiated manually on the dashboard, in one example, it is not uncommon for an analysis of interest to be run by default. In one example, a user can select a display of trends on total emissions on a dashboard that summarizes / overviews the system and keep it updated over time. In some examples, escape emissions assessment data can be combined with other data on the platform or data from enterprise production information (PI) systems for a holistic analysis and trade-off across operational, safety, and emissions controls of industrial facilities to provide continuous improvement across the operational life cycle.

[0097] GUI 700 can be displayed based on various analyses performed by the emissions monitoring platform 260. The GUI 700 shows a comparison between total emissions indicator values determined in different years. Points on line 710 represent total emissions indicator values determined for the current year based on sensor measurements performed over a time interval of 12 months including before the point. Points on line 720 represent total emissions indicator values determined for the previous year based on sensor measurements performed over a time interval, such as 12 months, 3 months, 6 months, or other time period.

[0098] For example, a total emissions indicator value S1 for February of the current year can be determined based on sensor measurements received over a time interval of 12 months including before February of the current year. A total emissions indicator value S2 for February of the previous year can be determined based on sensor measurements received over a time interval of 12 months including before February of the previous year. Likewise, a total emissions indicator value S3 for March of the current year can be determined based on sensor measurements received over a time interval of 12 months including before March of the current year. A total emissions indicator value S4 for March of the previous year can be determined based on sensor measurements received over a time interval of 12 months including before March of the previous year.

[0099] Referring to Figure 7 The plurality of total emissions indicator values can be determined with a granularity size of one month, but can be determined with any other granularity size, such as one day, one week, etc. Based on the plurality of total emissions indicator values, the emissions monitoring platform 260 can determine aggregated total emissions indicator values that can be used for further analysis and monitoring. For example, referring to Figure 7 The emissions monitoring platform 260 can determine an overall change in emissions by determining a first aggregated total emissions indicator value by aggregating total emissions indicator values for each month of the current year, determining a second aggregated total emissions indicator value by aggregating total emissions indicator values for each month of the previous year, and determining a difference between the first aggregated total emissions indicator value and the second aggregated total emissions indicator value. For example, Figure 7 It is shown that emissions determined for the current year decreased by 28.7% compared to emissions determined for the previous year.

[0100] Various parameters for determining total emission indicator values / performing emission comparisons and displaying a GUI (such as GUI 700) can be based on user input at user computing device 285. For example, a user can input a value for a length of a time interval to be employed, an indication of a number of sensors to be used to determine a number of emission indicator values and perform comparisons, a size of an interval for comparisons, a time for a comparison to be performed, and the like. With reference to GUI 700, for example, based on a user input indicating that a comparison is to be performed on total emission indicator values determined for a one-month interval of a two-year adoption and a length of a time interval equal to 12 months, user computing device 285 can display GUI 700.

[0101] More than one unit in a facility (or multiple portions of a unit) can be shut down to perform maintenance. Maintenance can include various activities related to safeguarding, repairing, and / or restoring equipment to maintain functionality and integrity. Typical maintenance activities can include cleaning, inspection, lubrication, testing, replacement, and / or repair of components. For example, a continuously running pump can be at risk of fire and can require periodic inspection / adjustment. For example, pipes and vessels that handle heavy oil can require periodic cleaning to prevent solid residue from accumulating over time. Maintenance activities can last from a few hours to a few weeks.

[0102] There can be significant or insignificant emissions during various maintenance activities. However, in one example, these emissions are permissible emissions under current regulations, meaning that these emissions do not need to be reported and / or included in the final total amount reported to state or federal environmental protection agencies. Sensors in an area that experiences maintenance-related emissions can exhibit substantially higher sensor outputs, which can skew the total emission indicator values in the facility and can result in inaccurate emission calculations and inappropriate comparisons for regulatory compliance purposes. To avoid this, measurements obtained during a maintenance activity can be excluded from the calculation of total emission indicator values. For example, emissions monitoring platform 260 can exclude sensor outputs determined to be impacted during a maintenance activity from all sensors in the facility. Alternatively, the emissions monitoring platform can exclude sensor outputs corresponding to sensors having a detection zone that overlaps with an area that can experience maintenance-related emissions during a maintenance activity. Excluding sensor outputs can include ignoring any detection events (such as peaks) that can be recorded in the sensor outputs during maintenance-related activities.

[0103] Figure 8 An example sensor output 800 observed over a time interval is shown. The time interval can include a time corresponding to a maintenance activity. The sensor outputs correspond to three sensors that can be in an area that experiences a maintenance activity during a maintenance time 804. As Figure 8As shown, three sensors can record a high sensor output during maintenance time 804 and can skew the total emission value that can be calculated for a time interval that can include maintenance time 804. To avoid this, the emission monitoring platform 260 can exclude any sensor output (such as detection events) from these three sensors during maintenance time for calculating the total emission indicative value. Alternatively, the emission monitoring platform 260 can exclude any detection events during maintenance time that can be recorded by any sensor in the facility.

[0104] A software platform in the emission monitoring system 255 can be used to document maintenance activities. Each maintenance activity can be associated with a particular time window, specific units / components within the facility, and / or sensors that can be affected. A user can input (such as via user computing device 285) maintenance activity information with a start time, end time, specific location or area, and nature of the activity to the system prior to / after each maintenance activity. Once the maintenance activity information is added, any plume detection (peaks) recorded by the sensors during that time window can be excluded from the total emission calculation. The exclusion time can be prorated by the software platform to make a valid comparison for the same time period. In one example, a unit has been running for only 9 months (January 1 to September 30) of the past year due to a major construction work, but the unit was fully operational throughout the previous year. To compare the emission levels for the two years, the emission monitoring platform can calculate the average monthly emission indicative value for the past year over the 9 operating months and multiply the monthly average by a factor of 12 to obtain an estimate for the entire year, and then use this number for comparison with the number for the previous year. Alternatively, the emission monitoring platform 260 can use only the emission indicative data for the 9th month calculated from the year for comparison with the 9th month data for the same time period of January 1 to September 30 for the previous year.

[0105] In another example, the emission calculation and / or comparison can be performed using all detections including detections during a maintenance period (such as for research and / or documentation purposes). For example, a plant can want to monitor the total emissions associated with a particular maintenance task. A research organization can compare the total emissions in two or more geographic regions for air quality or climate change research, etc.

[0106] Figure 9An example maintenance dashboard 900 is shown that can be used to document maintenance activities in a facility based on user input. The maintenance dashboard 900 can record future planned maintenance activities, currently executing maintenance activities, and / or completed maintenance activities. Each maintenance activity can be associated with a unit 902 in the facility that experienced the maintenance activity, a maintenance start date / time 904, a maintenance end date / time 906, a work description 908 associated with the maintenance activity, and sensors 910 affected by the maintenance activity. As described above, a user can input at least some of the information at the maintenance dashboard, such as via the user computing device 295.

[0107] The dashboard 900 described herein involves post-processing of archived data. The post-processing is a data operation performed manually or automatically in a server computer on the cloud or network initiated from the software platform. The various embodiments disclosed herein can be used as an added feature on an escape emissions monitoring software platform, although the system itself can collect, stream, and process sensor data from a field, such as an industrial facility, and provide real-time / near real-time feedback including but not limited to severe leaks and potential leak source locations. In some embodiments, the system can also manage sensor health data and detection notifications and investigations conducted under each notification. The post-processing of sensor data in this application can be performed independently of the real-time features, and the post-processing can analyze archived data or data collected over past time periods. The post-processing does not interfere or affect the operation or real-time performance of the sensor network system in detecting gas leaks in the field. The archived data can be historical data from previous years, months, days, hours, or other time periods stored in a data store accessible by the server computer in the software platform.

[0108] Data input via the maintenance dashboard 900 can be stored in the data store 290 and can be used by the emissions monitoring platform 260 to perform the various analyses described herein. For example, for the calculation of the emission indicator value and / or the total detected emissions (e.g., as described with reference to Equations (5) and (6)), the emissions monitoring platform 260 can ignore (e.g., not include) any detection events recorded by any sensor in the facility. Alternatively, for the calculation of the emission indicator value and / or the total detected emissions, the emissions monitoring platform 260 can ignore (e.g., not include) any detection events recorded by a sensor associated with a maintenance activity. For example, with reference to the maintenance activity 912, the affected sensors are S-17, S-20, and S-30. The emissions monitoring platform 260 can ignore any detection events recorded by the sensors S-17, S-20, and S-30 during the maintenance time corresponding to the maintenance activity 912. In another example, the emissions monitoring platform 260 can ignore any detection events recorded by any sensor in the facility during the maintenance time corresponding to the maintenance activity 912.

[0109] Although the above examples illustrate the use of a maintenance dashboard to record maintenance activities and determine the emission indicator value and / or the total detected emissions, maintenance activities can be determined based on the location of employees associated with the facility. For example, each employee can be equipped with a location tracking device (e.g., a global navigation satellite system (GNSS) tracker, a smartphone with built-in tracking capabilities, or any other tracking device) that can be used to determine the location of an employee. The emissions monitoring platform 260 can assume / determine that a unit in the facility experienced a maintenance activity, e.g., if an employee was located in the vicinity of the unit. The emissions monitoring platform 260 can also determine that there is more than one sensor with a detection zone that can include the unit. For the determination of the emission indicator value and / or the total emission indicator value, any detection events recorded by the more than one sensor can be ignored by the emissions monitoring platform 260 (e.g., for the time period during which the employee was located in the vicinity of the unit). For example, the emissions monitoring platform 260 can exclude any detection events that can be detected by a sensor, e.g., if an employee was detected in the vicinity of the sensor or in a unit within a detection zone of the sensor.

[0110] Figure 10An example method 1000 of monitoring fugitive gas emissions is shown. In one arrangement, the example method 1000 can be performed by an emissions monitoring platform, such as the emissions monitoring platform 260. At step 1004, the emissions monitoring platform can receive a plurality of sensor outputs from a plurality of sensors in a facility, such as the gas sensors 265A. The plurality of sensor outputs can correspond to measurements by the plurality of sensors over a specified time interval. At step 1008, the emissions monitoring platform can determine a set of detection events in the plurality of sensor outputs. At step 1012, the emissions monitoring platform can determine whether any subset of detection events (in the set of detection events) corresponds to any maintenance times at one or more units in the facility 100.

[0111] At step 1020, the emissions monitoring platform 260 determines whether a subset of detection events (in the set of detection events) corresponds to a maintenance time. As explained in this disclosure, based on the set of detection events, an updated set of detection events can be determined by (at step 1024) excluding from the set of detection events the subset of detection events that correspond to times when a maintenance activity is being performed in a particular area / region of the facility 100. In one example, at step 1024, the emissions monitoring platform 260 can determine an emissions indicator value for each sensor based on the updated set of detection events (such as using equation (5)). If a subset of detection events (in the set of detection events) does not correspond to a maintenance time, the emissions monitoring platform can determine an emissions indicator value for each sensor based on the complete set of detection events that does not exclude a subset of detection events.

[0112] At step 1028, the emissions monitoring platform can determine a total emissions indicator value based on the emissions indicator values determined for each sensor (such as using equation (6)). As explained previously, the calculated total emissions indicator value is based on more than one detection event that does not correspond to a maintenance event, such as Figure 10The emission monitoring platform can compare the determined total emission indicator value to a second total emission indicator value. For example, the second total emission indicator value can be determined based on sensor outputs in a second specified time interval. As another example, the second total emission indicator value (or average emission value) can be determined based on sensor outputs at a different facility (such as in the specified time interval). At step 1036, the emission monitoring platform can optionally communicate a result of the comparison to a user computing device. The user computing device can display a GUI based on the comparison. For example, the GUI can show the total emission indicator value (or average emission value), the second total emission indicator value (or second average emission value), and / or a difference between the two (such as a relative emission).

[0113] Figure 11 Another example method 1100 of monitoring fugitive gas emissions is shown. In an arrangement, example method 1100 can be performed by an emission monitoring platform (such as emission monitoring platform 260). At step 1104, the emission monitoring platform can receive a plurality of sensor outputs from a plurality of sensors (such as gas sensors 265A) in a facility. The plurality of sensor outputs can correspond to measurements by the plurality of sensors in a specified time interval. At step 1108, the emission monitoring platform can determine a first set of detection events in the plurality of sensor outputs. At step 1116, the emission monitoring platform can determine a total emission value based on emission indicator values determined for each sensor (such as using equation (7)). At step 1132, the emission monitoring platform can compare the determined total emission indicator value to a second total emission indicator value. For example, the second total emission indicator value can be determined based on sensor outputs in a second specified time interval. As another example, the second total emission indicator value can be determined based on sensor outputs at a different facility (such as in the specified time interval). At step 1136, the emission monitoring platform can communicate a result of the comparison to a user computing device. The user computing device can display a GUI based on the comparison. For example, the GUI can show the total emission indicator value, the second total emission indicator value, and / or a difference between the two.

[0114] Various examples herein illustrate monitoring of total emissions in a facility (such as in real-time or near real-time) and / or comparing emissions levels at different time intervals and / or across multiple different facilities. Various examples herein illustrate methods, apparatuses, and systems for determining changes or trends in total emissions for a facility or a particular unit in a facility. As illustrated herein, for example, information provided to an emissions monitoring system can be used to: (a) assess changes in total emissions levels from a facility in a timely manner; (b) assess consistency of LDAR sensor programs over time; and / or (c) assess effectiveness of established LDAR training programs.

[0115] For example, Figure 2 A block diagram of another example of a sensor network-based emissions monitoring system 255 is shown, which is used in whole or in part to perform the methods described herein. The present disclosure is not limited to only Figure 2 The combination of components shown; rather, many variations of the sensor network-based emissions monitoring system are contemplated by the method steps, apparatus components, system interactions, and other aspects disclosed herein. For example, the emissions monitoring platform 260 can be communicatively linked with more than one sensor, such as a gas sensor 265A, a wind sensor 265B, and / or more than one other sensor 265C, such as a GPS location sensor. In one example, one transmitter can host multiple sensors of one or more types. For example, a single sensing assembly can include multiple sensors of one or more types. In other examples, a networked sensor can include multiple sensors of more than one type. The multiple sensors can be operable to collect measurements in near real-time for input to the emissions monitoring platform 260.

[0116] Figure 2 The emissions monitoring system 255 includes a block diagram of many platforms and devices that are further detailed in the present disclosure. Figure 2 The emissions monitoring system 255 is illustrative and has one or more processing devices to implement the methods and functions of certain aspects of the present disclosure. The processing devices can include general purpose microprocessors and / or application specific processors designed to perform certain calculations or functions described herein. For example, the processing devices can execute computer executable instructions stored in memory of the platform or device in software and / or firmware form. Examples of well known computing systems, environments, and / or configurations that can be suitable for use with the disclosed embodiments include, but are not limited to, personal computers (PCs), server computers, handheld or laptop computers, smart phones, multiprocessor systems, microprocessor-based systems, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

[0117] Additionally,Figure 2 The platforms and / or devices in the system can include one or more memories, such as any of various computer-readable media. Examples of computer-readable media can include a tangible computer memory accessible by the emissions monitoring platform 260. The memory can be non-transitory, volatile, or non-volatile and / or removable and non-removable storage implemented in any method or technology for storage of information such as computer-readable instructions, object code, data structures, database records, program modules, or other data. Examples of computer-readable media can include Random Access Memory (RAM), Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory or other memory technology, Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disc (DVD), or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the emissions monitoring platform 260. The memory can also include the data store 290 in the platform and further can be a storage module that can include compiled software code that causes the platform, device, and / or overall system to operate in an improved manner as disclosed herein. For example, the data store 290 can store software used by the computing platform, such as an operating system, application programs, and / or associated database.

[0118] Further, Figure 2 The devices in the system can include one or more communication interfaces, including but not limited to a microphone, a keyboard, a touchscreen, and / or a stylus through which a user of a computer, such as the computing device 285, can provide input, and can also include a speaker for providing audio output and a video display device for providing textual, audiovisual, and / or graphical output. The communication interfaces can include a network controller for electronic communication (such as wireless or wired) with one or more other components on the network, such as the public network 275 or the private network 270. The network controller can include electronic hardware for communication over network protocols, including TCP / IP, UDP, Ethernet, and / or other protocols. In some examples, the emissions monitoring platform 260 can be a cloud-based device that is operated remotely from the facility over a computer network.

[0119] The controller 280 can interact with and / or execute commands received from an emissions monitoring platform 260. The controller 280 can be communicatively linked to the emissions monitoring platform 260 and configured to actuate one or more tangible components in the facility. For example, the facility can include a valve component 281 assembled between a first component and a second component that transport gas material throughout the facility. The controller 280 can actuate the valve component 281 from an open position to a closed position, and vice versa. For example, the components can be transporting gas material a distance in the facility, and when a source of a leak is detected to originate from the second component that transports gas material throughout the facility, the controller can issue a command to actuate the valve component 281 into a closed position, thereby shutting off the gas flow to the component with the source of the leak. In another example, the controller 280 can be communicatively linked to a regulator 282 component.

[0120] Referring to Figure 2 In one example, the user computing device 285 can include a processor, a memory, and / or a communication interface. The processor can process and analyze data stored in the memory. In some embodiments, the memory can store computer-executable instructions that, when executed by the processor, cause a user computing device 285 to perform one or more of the steps disclosed herein. In some embodiments, the system 255 can determine total detected emissions and generate an emissions report based on signals received through the communication interface. As explained herein, in one example, the user computing device 285 can receive data from the emissions monitoring platform 260 and display a graphical user interface (GUI) on the user computing device 285 to enable a user to navigate the emissions report.

[0121] In some embodiments, the system 255 can generate an alert based on values received through the communication interface. The values can represent that a dangerous gas leak has been detected in the facility due to an abnormal sensor reading. The detected event can cause an adjustment to one or more operational parameters of the facility. As a result of the operational parameter adjustment, the facility can make one or more operational adjustments or stop / start. In an alternative embodiment, the commands can be communicated wirelessly or in a wired manner directly to physical components at the facility, such that the physical components include an interface for receiving and executing the commands.

[0122] While Figure 2Without limitation, in some embodiments, user computing device 285 can include a desktop computer, a smartphone, a wireless device, a tablet, a laptop, etc. User computing device 285 can be physically located on-premise or remotely and can be connected to one or more other devices in system 255 through one or more communication links.

[0123] While Figure 2 components are illustrated as logical blocks, the present disclosure is not limited to this Figure 2 One or more of the representative blocks of the components can be combined into a single block or the functionality of one or more of the representative blocks can be divided among more blocks in accordance with some examples. Moreover, some blocks described as internal to one or more of the components can be moved externally to such components. For example, in some examples, Figure 2 It is contemplated that data stores 290 can be stored within a firewall (e.g., inside LAN 270) or on a publicly accessible network 275 to facilitate sharing among multiple facilities.

[0124] Further, data stores 290 from multiple plant locations can be shared and analyzed en masse to identify one or more trends and / or patterns in the operation and behavior of the facility and / or components. In such a crowdsourcing-type example, a distributed database arrangement can be provided in which a database (such as a data store) can only serve as an interface through which multiple independent data stores can be accessed. As such, a system 255 can access the database to analyze data collected by various sensors. In another example, data values from a database from various facilities can be combined and / or collated into a single database that an emissions monitoring platform can utilize to perform various calculations.

[0125] While specific embodiments are shown and described in connection with the figures, it will be understood that various modifications can be thought of by those skilled in the art, without departing from the spirit and scope of the appended claims. For example, the term "gas stream" includes examples involving a single gas, multiple gases, and a mixture of gases, and the disclosure contemplates their use as interchangeable. Further, the term "emissions indicator value" includes examples involving a single emissions value, and the disclosure contemplates their use as interchangeable in real-world applications. Further, it will be recognized that the scope of the disclosure and the appended claims is not limited to the specific embodiments shown and discussed in the figures, and that modifications and other embodiments are intended to be included within the scope of the disclosure and the appended figures. Further, while the foregoing description and associated figures describe example embodiments in the context of certain exemplary combinations of elements and / or functions, it will be recognized that different combinations of elements and / or functions can be provided by alternative embodiments without departing from the scope of the disclosure and the appended claims.

Claims

1. A computer system comprising: at least one processor; a data store configured to store archived data corresponding to plant operations of an industrial facility; and a non-transitory computer-readable memory storing computer-readable instructions that, when executed by the at least one processor, cause a platform to: post-process the archived data corresponding to a combination of plant operations, algorithms, and intuitive graphical user interfaces that allow for an assessment of emissions of the industrial facility, wherein the plant operations include a gas stream within a time interval associated with a gas plume detection; determine a response factor from the data store as: where Xi-Xn are molar ratios of any gas in the gas stream, Fi-Fn are individual response factors of a species of the any gas in the gas stream; receive the archived data associated with the time interval from the data store; determine an emission indicator value based on a plurality of detection events in the archived data as: where S i is a cumulative emission detection value from sensor i for the time interval, and F i is an overall response factor at sensor i for the gas stream, wherein the determination excludes those detection events corresponding to a maintenance activity; determine a total emission indicator value for the industrial facility within the time interval based on corresponding emission indicator values and corresponding response factors in the archived data for the time interval employing the algorithms; determine a difference between the total emission indicator value for the industrial facility within the time interval and a second total emission indicator value for the industrial facility within a second time interval; and send an indication of the difference; and a user computing device configured to display the indication of the difference on the intuitive graphical user interface on a display device coupled with the user computing device, wherein the intuitive graphical user interface outputs a holistic analysis and trade-off to allow for an assessment of emissions of the industrial facility.

2. The computer system of claim 1, wherein, The non-transitory computer-readable memory stores computer-readable instructions that, when executed by the at least one processor, cause the platform to determine the detection event for the time interval based on the archived data associated with the time interval and a modeled baseline value corresponding to the time interval.

3. The computer system of claim 2, wherein, The non-transitory computer-readable memory stores computer-readable instructions that, when executed by the at least one processor, cause the platform to determine a detection event in the detection events at a first time (Tl) based on a difference between a modeled baseline value at the first time and a value of a sensor detection peak at the first time based on the archived data associated with the time interval exceeding a threshold value.

4. The computer system of claim 1, wherein, The non-transitory computer-readable memory stores computer-readable instructions that, when executed by the at least one processor, cause the platform to: determine the emission indicator value based on aggregating peak areas associated with the detection event in the archived data associated with the time interval.

5. The computer system of claim 1, wherein, The non-transitory computer-readable memory stores computer-readable instructions that, when executed by the at least one processor, cause the platform to: determine the emission indicator value based on aggregating peak heights associated with the detected events in the archived data associated with the time interval.

6. The computer system of claim 1, wherein, when the computer-readable instructions are executed by the at least one processor, the computer-readable instructions cause the platform to: receive, from the user computing device, an indication of a maintenance time interval and a location associated with the maintenance activity; determine the archived data associated with the maintenance time interval based on the location; determine one or more detected events in the archived data associated with the maintenance time interval at the location based on the time interval.

7. The computer system of claim 1, wherein, when the computer-readable instructions are executed by the at least one processor, the computer-readable instructions cause the platform to: determine a third total emission indicator value over a third time interval and a fourth total emission indicator value over a fourth time interval; determine a first aggregated total emission indicator value based on a sum of the total emission indicator value and the second total emission indicator value; determine a second aggregated total emission indicator value based on a sum of the third total emission indicator value and the fourth total emission indicator value; determine an aggregated change in emissions based on a difference between the first aggregated emission indicator value and the second aggregated emission indicator value; and and communicate the total emission indicator value, the second total emission indicator value, the third total emission indicator value, the fourth total emission indicator value, and the aggregated change in emissions to the user computing device.

8. The computer system of claim 7, wherein, the user computing device is configured to display one or more of the total emission indicator value, the second total emission indicator value, the third total emission indicator value, the fourth total emission indicator value, and the aggregated change in emissions on the display device.

9. The computer system of claim 1, wherein when the computer-readable instructions are executed by the at least one processor, the computer-readable instructions cause the platform to: determine a third total emission indicator value over the time interval for a second facility; determine a second difference between the total emission indicator value and the third total emission indicator value; and communicate an indication of the second difference to the user computing device; and wherein the user computing device is configured to display one or more of the total emission indicator value, the third total emission indicator value, and the second difference on the display device.

10. The computer system of claim 1, wherein, the determining a total emission indicator value for the industrial facility includes determining a sum of products of corresponding emission indicator values and corresponding response factors for the archived data.

11. A method comprising: receiving archived data from a data store, the archived data corresponding to plant operations associated with gas plume detection over a time interval; for the archived data: determining one or more emission indicator values based on a plurality of detection events in the archived data; and determining a response factor for a gas stream associated with the archived data based on a composition of the gas stream and response factors for species of any gases in the gas stream as: where X1-Xn are molar ratios of any gases in the gas stream and F1-Fn are individual response factors for species of the any gases in the gas stream; determining a total emission indicator value for a facility over the time interval based on the one or more emission indicator values and corresponding response factors; determining a difference between the total emission indicator value for the facility over the time interval and a second total emission indicator value for the facility over a second time interval; and sending an indication of the difference to a user computing device on an intuitive graphical user interface.

12. The method of claim 11, wherein, determining the response factor for the gas stream includes determining at least one gas species in the gas stream and its response factor.

13. The method of claim 11, further comprising: for the archived data, determining the detection events based on modeled baseline values corresponding to the archived data.

14. The method of claim 13, further comprising: determining a detection event in the plurality of detection events at a first time (T1) based on a difference between the modeled baseline value at the first time and a value of a sensor detection peak at the first time based on the archived data exceeding a threshold value.

15. The method of claim 11, wherein, determining the one or more emission indicator values for the archived data includes determining the emission indicator values based on aggregating peak amplitude values associated with the detection events in the archived data.

16. The method of claim 11, wherein, the plurality of detection events in the archived data exclude one or more detection events corresponding to a maintenance activity, the method further comprising: receiving an indication of a maintenance time interval and a location associated with the maintenance activity from a user computing device; determining the archived data associated with the maintenance time interval based on the location; and determining the one or more detection events at the location in the archived data associated with the maintenance time interval based on the time interval.

17. The method of claim 11, further comprising: determining a third total emission indicator value for a second facility over the time interval; determining a second difference between the total emission indicator value and the third total emission indicator value; and sending an indication of the second difference to the user computing device.

18. A non-transitory computer-readable medium storing instructions that, when executed, cause: ​ receiving a plurality of sensor outputs from a plurality of sensors in a facility associated with gas concentration measurements over a time interval; for a sensor of the plurality of sensors: determining an emission indicator value based on a plurality of detection events in a sensor output associated with the sensor, wherein, in the sensor output, the plurality of detection events excludes a plurality of detection events corresponding to a maintenance activity; and determining a response factor for a gas stream based on a composition of the gas stream and response factors for species of any gases in the gas stream is: where X1-Xn are molar ratios of any gases in the gas stream and F1-Fn are individual response factors for species of the any gases in the gas stream; and determining a total emission indicator value for the facility over the time interval based on corresponding emission indicator values for the plurality of sensors and corresponding response factors.

19. The non-transitory computer readable medium of claim 18, further storing instructions that when executed cause: determining a difference between the total emission indicator value for the facility over the time interval and a second total emission indicator value for the facility over a second time interval; and sending an indication of the difference to a user computing device.

20. A computer system comprising: at least one processor; a data store configured to store archived data corresponding to plant operations of an industrial facility; and a non-transitory computer readable memory storing computer readable instructions that when executed by the at least one processor cause a platform to: post-process the archived data corresponding to a combination of plant operations that allow for an assessment of emissions of the industrial facility, algorithms, and an intuitive graphical user interface, wherein the plant operations include a gas stream associated with gas plume detection over a time interval; select a response factor from the data store to be: where X1-Xn are molar ratios of any gases in the gas stream and F1-Fn are individual response factors for species of the any gases in the gas stream; receive the archived data associated with the time interval from the data store; determine an emission indicator value based on a plurality of detection events in the archived data associated with the time interval; determine a total emission indicator value for the industrial facility over the time interval using the algorithms based on corresponding emission indicator values in the archived data for the time interval and corresponding response factors; and send the total emission indicator value; and a user computing device configured to output the total emission indicator value on the intuitive graphical user interface on a display device coupled to the user computing device.

21. The computer system of claim 20, wherein: the memory stores computer readable instructions that when executed by the at least one processor cause the platform to: determine a difference between the total emission indicator value for the industrial facility over the time interval and a second total emission indicator value for the facility over a second time interval; and sending an indication of the difference to the display device of the user computing device for display.

22. The computer system of claim 20, wherein, the non-transitory computer-readable memory storing computer-readable instructions that, when executed by the at least one processor, cause the platform to: determine the emission-indication value based on aggregating peak areas associated with the detected events in the archived data associated with the time interval.

23. The computer system of claim 20, wherein, the non-transitory computer-readable memory storing computer-readable instructions that, when executed by the at least one processor, cause the platform to: determine the emission-indication value based on aggregating peak areas associated with the detected events in the archived data associated with the time interval.

24. The computer system of claim 20, wherein the computer-readable instructions, when executed by the at least one processor, cause the platform to: determine a third total emission-indication value for a second facility over the time interval; determine a second difference between the total emission-indication value and the third total emission-indication value; and send an indication of the second difference to the user computing device; and wherein the user computing device is configured to display one or more of the total emission-indication value, the third total emission-indication value, and the second difference on the display device.

25. The computer system of claim 20, wherein, the determining a total emission-indication value for the industrial facility includes determining a sum of corresponding emission-indication values for the archived data and corresponding response factors.

26. A method comprising: receiving archived data from a data store, the archived data corresponding to plant operations associated with a detection of a plume of gas emissions at an industrial facility over a time interval; for the archived data: determining one or more emission-indication values based on a plurality of detected events in the archived data; and determining a response factor for a gas stream associated with the archived data based on a composition of the gas stream and response factors for species of any gases in the gas stream as: where X1-Xn are molar ratios of any gases in the gas stream and F1-Fn are individual response factors for species of the any gases in the gas stream; determining a maintenance activity that occurred at the industrial facility during the time interval; excluding those detected events corresponding to the maintenance activity from the one or more emission-indication values; determining a total emission-indication value for the industrial facility over the time interval based on the one or more emission-indication values and corresponding response factors after the excluding step; and determining a difference between the total emission-indication value for the industrial facility over the time interval and a second total emission-indication value for the industrial facility over a second time interval.

27. The method of claim 26, further comprising: communicating an indication of the difference to a user computing device.

28. The method of claim 26, wherein, determining the one or more emission indicative values comprises determining the emission indicative values based on aggregating peak area values associated with the detected events in the archived data.

29. The method of claim 26, wherein, determining the one or more emission indicative values comprises determining the emission indicative values based on aggregating peak amplitude values associated with the detected events in the archived data.

30. The method of claim 26, further comprising: receiving, from a user computing device, an indication of a maintenance time interval and a location associated with the maintenance activity; determining, based on the location, a first sensor in the industrial facility; and determining, based on the time interval, one or more detected events of the first sensor in the archived data for the exclusion step.

31. A method comprising: for a first facility: post-processing archived data stored in a data store corresponding to a combination of plant operations, algorithms, and intuitive graphical user interfaces that enable assessment of emissions of the first facility, wherein the plant operations include a gas stream associated with gas plume detection over a time interval; selecting, from the data store, a response factor for: where X1-Xn are molar ratios of any gas in the gas stream and F1-Fn are individual response factors for species of the any gas in the gas stream; receiving, from the data store, the archived data associated with the time interval; determining an emission indicative value based on a plurality of detected events in the archived data associated with the time interval; and determining, using the algorithms, a total emission indicative value for the first facility over the time interval based on corresponding emission indicative values in the archived data and corresponding response factors for the time interval; for a second facility: post-processing archived data stored in a data store corresponding to a combination of plant operations, algorithms, and intuitive graphical user interfaces that enable assessment of emissions of the second facility, wherein the plant operations include a gas stream associated with gas plume detection over a time interval; selecting, from the data store, a response factor for: receiving, from the data store, the archived data associated with the time interval; determining an emission indicative value based on a plurality of detected events in the archived data associated with the time interval; determining, using the algorithms, a total emission indicative value for the second facility over the time interval based on corresponding emission indicative values in the archived data and corresponding response factors for the time interval; determining a difference between the total emission indicative value of the first facility over the time interval and the total emission indicative value of the second facility over the time interval; and communicating an indication of the difference to a user computing device for display on an intuitive graphical user interface by a display device.

32. A method comprising: for a first unit: post-processing archived data stored in a data store corresponding to a combination of plant operations, algorithms, and intuitive graphical user interfaces that enable assessment of emissions of the first unit, wherein the plant operations include a gas stream associated with gas plume detection over a time interval; selecting a response factor from the data store; wherein X1-Xn are molar ratios of any gas in the gas stream, F1-Fn are individual response factors for species of the any gas in the gas stream; receiving the archived data associated with the time interval from the data store; determining an emission indicator value based on a plurality of detection events in the archived data associated with the time interval; and determining an average emission indicator value for the first unit over the time interval using the algorithms based on corresponding emission indicator values and corresponding response factors in the archived data; for a second unit: post-processing archived data stored in a data store corresponding to a combination of plant operations, algorithms, and intuitive graphical user interfaces that enable assessment of emissions of the second unit, wherein the plant operations include a gas stream associated with gas plume detection over a time interval; selecting a response factor from the data store; receiving the archived data associated with the time interval from the data store; determining an emission indicator value based on a plurality of detection events in the archived data associated with the time interval; determining an average emission indicator value for the second unit over the time interval using the algorithms based on corresponding emission indicator values and corresponding response factors in the archived data; determining a difference between the average emission indicator value for the first unit over the time interval and the average emission indicator value for the second unit over the time interval; and sending an indication of the difference to a user computing device for display on an intuitive graphical user interface by a display device.

33. The method of claim 32, wherein, The first unit and the second unit are both at a first facility.

34. The method of claim 32, wherein, The first unit is at a first facility, and wherein the second unit is at a second facility.

35. The method of claim 32, wherein, for the first unit, the plurality of detection events excludes one or more detection events corresponding to a maintenance activity in the archived data, the method further comprising: for the first unit: receiving an indication of a maintenance time interval at the first unit and a location associated with the maintenance activity from a user computing device; determining the archived data associated with the maintenance time interval at the first unit based on the location; and determining the one or more detection events at the location in the archived data associated with the maintenance time interval based on the time interval; and wherein for the second unit, the plurality of detection events excludes one or more detection events corresponding to a maintenance activity in the archived data, the method further comprising: for the second unit: receiving, at the second unit, an indication of a maintenance time interval and a location associated with the maintenance activity; based on the location, determining the archived data associated with the maintenance time interval at the second unit; and based on the time interval, determining the one or more detected events at the location in the archived data associated with the maintenance time interval.

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