Patent Application
20240191663
This disclosure relates to technologies for monitoring emissions.
Industrial engines are used for power generation in a variety of facilities, such as power plants, oil and gas refineries, and factories. Such industrial engines are often the subject of emission regulations, meaning that the exhaust gasses emitted from these engines need be monitored and tracked. In some instances, emissions caps are imposed by regulatory agencies. In such instances, the operators of industrial engines take steps to ensure and prove that their industrial engines are producing emissions well under the imposed emissions caps. Common emissions that are regulated include carbon monoxide (CO), carbon dioxide (CO2), unburned hydrocarbons, various nitrous oxides (NOx), and various sulphur oxides (SOx).
This disclosure relates to prediction emission monitoring.
An example implementation of the subject matter described within this disclosure is a method with the following features. Sensor values indicative of operating parameters of a combustion system are received. An emission is determined, by at least a predictive model, based on at least the sensor values. The predictive model has been trained using at least a first set of data acquired from a measured emission and a second set of data determined using at least a physics model. Combustion system operating parameters are adjusted based on at least the determined emission.
Aspects of the example method, which can be combined with the example method alone or in combination with other aspects, include the following. The emission includes any of the following: carbon monoxide (CO), carbon dioxide (CO2), unburned hydrocarbons, nitrous oxide (NOx), or sulphur oxides (SOx).
Aspects of the example method, which can be combined with the example method alone or in combination with other aspects, include the following. The combustion system is a gas turbine engine. The sensor values include values indicative of the following: a compressor exit temperature, a compressor exit pressure, a fuel mass-flow rate, an ambient temperature, an ambient relative humidity, a power turbine inlet temperature, and an average power turbine exhaust temperature.
Aspects of the example method, which can be combined with the example method alone or in combination with other aspects, include the following. The predictive model is forward queried. Forward querying includes predicting emission outputs based on at least inputting, to the predictive model, values indicative of corresponding sensor values.
Aspects of the example method, which can be combined with the example method alone or in combination with other aspects, include the following. The predictive model is reverse queried. Reverse querying includes receiving a target emission value, determining a range of values indicative of operating parameters of the combustion system that result in the target emission, and controlling the combustion system based upon the target emissions.
Aspects of the example method, which can be combined with the example method alone or in combination with other aspects, include the following. The predictive model is retrained based on at least updated sensor values.
Aspects of the example method, which can be combined with the example method alone or in combination with other aspects, include the following. Retraining the predictive model includes the following steps. The combustion system is run in a first operating mode and a second operating mode. Data is received from multiple sensors during the first operating mode. Data is received from the multiple sensors during the second operating mode. The multiple sensors include an emissions sensor. The predictive model is retrained based on at least the received data.
Aspects of the example method, which can be combined with the example method alone or in combination with other aspects, include the following. Receiving data from the multiple sensors within the second operating mode includes receiving nine data points.
Aspects of the example method, which can be combined with the example method alone or in combination with other aspects, include the following. The emission is a first emission. The method further includes the following features. A second emission is determined by at least the retrained predictive model based on at least the sensor values. Combustion system operating parameters based on at least the second emission.
An example implementation of the subject matter described within this disclosure is a system with the following features. Memory stores instructions, which, when executed by at least one data processor causes the at least one data processor to perform the following operations. Sensor values indicative of operating parameters of a combustion system are received. An emission is determined by at least a predictive model based on at least the sensor values. The predictive model has been trained using at least a first set of data acquired from a measured emission and a second set of data determined using at least a physics model. Combustion system operating parameters are adjusted based on at least the determined emission.
Aspects of the example system, which can be combined with the example system alone or in combination with other aspects, include the following. The system further includes a combustion system, which includes a gas-turbine engine. The sensor values include values indicative of the following: a compressor exit temperature, a compressor exit pressure, a fuel mass-flow rate, an ambient temperature, an ambient relative humidity, a power turbine inlet temperature, and an average power turbine exhaust temperature.
Aspects of the example system, which can be combined with the example system alone or in combination with other aspects, include the following. The predictive model is configured to be recalibrated periodically based on at least updated sensor values.
Aspects of the example system, which can be combined with the example system alone or in combination with other aspects, include the following. Recalibrating the predictive model includes the following. The combustion system is run in a first operating mode and a second operating mode. The sensor values are received during the first operating mode. The sensor values are received during the second operating mode. The sensors include an emissions sensor. The predictive model is retrained based on at least the received sensor values.
Aspects of the example system, which can be combined with the example system alone or in combination with other aspects, include the following. Receiving sensor values within the first operating mode includes receiving nine data points.
Aspects of the example system, which can be combined with the example system alone or in combination with other aspects, include the following. The operations further include the following. The emission is determined by at least the retrained predictive model based on at least the sensor values. Combustion system operating parameters are adjusted based on at least the determined emission.
An example implementation of the subject matter described within this disclosure is a non-transitory computer readable memory storing instructions which, when executed by at least one data processor forming part of at least one computing system, causes the at least one data processor to perform the following operations. Sensor values indicative of operating parameters of a combustion system engine are received. An emission is determined by at least a predictive model based on at least the sensor values. The predictive model has been trained using at least a first set of data acquired from a measured emission and a second set of data determined using at least a physics model. Combustion system operating parameters are adjusted based on at least the determined emission.
Aspects of the example non-transitory computer readable memory, which can be combined with the example non-transitory computer readable memory alone or in combination with other aspects, include the following. The combustion system includes a gas-turbine engine. The data from sensors includes sensor values indicative of the following: a compressor exit temperature, a compressor exit pressure, a fuel mass-flow rate, an ambient temperature, an ambient relative humidity, a power turbine inlet temperature, and an average power turbine exhaust temperature.
Aspects of the example non-transitory computer readable memory, which can be combined with the example non-transitory computer readable memory alone or in combination with other aspects, include the following. The predictive model is configured to be recalibrated periodically based on at least updated sensor values.
Aspects of the example non-transitory computer readable memory, which can be combined with the example non-transitory computer readable memory alone or in combination with other aspects, include the following. Recalibrating the predictive model includes the following. The combustion system is run in a first operating mode and a second operating mode. Data is received from sensors during the first operating mode. Data is received from the sensors during the second operating mode. The sensors include an emissions sensor. The predictive model is retrained based on at least the received data.
Aspects of the example non-transitory computer readable memory, which can be combined with the example non-transitory computer readable memory alone or in combination with other aspects, include the following. The emission is a first emission. The non-transitory computer readable memory further includes instructions to perform the following. a second emission is determined by at least the retrained predictive model based on at least the received data from the sensors. Combustion system operating parameters are adjusted based on at least the determined second emission.
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, show certain aspects of the subject matter disclosed herein and, together with the description, help explain some of the principles associated with the disclosed implementations.
Continuous, direct measurement of emissions from an industrial engine over an extended period is difficult due to temperatures, contamination, and sensor reliability. However, such monitoring, or alternative emissions tracking is required by some regulatory agencies. Every industrial engine is unique in its power and emission profile due to manufacturing tolerances, so generic emission models are often insufficient, especially as engine parameters change over time due to wear.
Some implementations of the subject matter relate to predicting emissions from an industrial machine using a predictive model, for example, a machine learning model, trained both on actual sensor data as well as a physics-based model. In some implementations, sensor values indicative of operating parameters of an industrial engine are received. Emissions are then determined by a machine learning model based on the sensor values. The machine learning model can have been trained using both empirical emissions data from sensors configured to sense parameters of an engine and calculated emissions data of an engine from a physics model. Once the emission values are determined, engine operating parameters can be adjusted to ensure the industrial engine is operating such that emissions are below a set threshold, such as a regulatory threshold. While the predictive model is primarily described in the context of a machine learning model, other predictive models can be used without departing from this disclosure. Combining the physics based modeling with the operational data from empirical emissions to train the machine learning model reduces the amount of time, power, and computing cycles to train the model for predicting emission levels.
The gas-turbine system 100 includes a variety of sensors that produce signals that include sensor values indicative of performance of a gas-turbine system 100. For example, the P2 sensor 112 and the T2 sensor 114 detect pressure and temperature, respectively, at an inlet 108 of a compressor 102. The P3 116 sensor measures a pressure of the compressor outlet prior to the combustors, and the T3 118 sensor measures a temperature at the same location. The T48 sensor 120 measures a temperature at an inlet of the low-pressure turbine 106. Additional sensor can be used to further measure parameters of the gas-turbine's operation.
The sensor are all coupled to a controller 110 used to monitor and control the gas-turbine system 100. For example, the various sensors produce signals indicative of the parameters the sensors are monitoring. These signals are received and processed by the controller 110. More details on the controller 110 are described throughout this disclosure.
In training, the machine learning model 201 takes in various operating parameters, for example, pressures, flows, and ambient conditions of a combustions system. In some implementations, at 202, the machine learning model takes in operational data including data from the T2 sensor 114 (
The controller 110 can be implemented with various levels of autonomy. In some implementations, the controller 110 alerts an operator that a parameter is out of disclosure, for example, emissions are above a target threshold, and the operator then adjusts engine parameter to move the emissions below the desired emissions threshold. In some implementations, the controller 110 alerts the operator that a parameter is out of disclosure, and provides recommendations to the operator to move the parameter within disclosure. The operator then selects an option and the controller adjusts operations accordingly. In some instances, the controller 110 determines that a parameter is out of disclosure, and changes or otherwise adjusts operations to move the parameter within disclosure with no input from the operator.
In operation, the controller is configured to receiving sensor values indicative of operating parameters of the combustion system, for example, the gas-turbine system 100. The controller can then determine, determine, at least in part with the predictive model, an emission based on at least the sensor values. The predictive model can have been trained using at least a first set of data acquired from a measured emission and a second set of data determined using at least a physics model. Engine operating parameters can then be adjusted based on at least the determined emission. In some implementations, the predictive model is stored within the non-transitory memory 552.
In some implementations, the engine is a gas-turbine engine. In such instances, sensor values can include values indicative of a compressor exit temperature, a compressor exit pressure, a fuel mass-flow rate, an ambient temperature, an ambient relative humidity, a power turbine inlet temperature, and/or an average power turbine exhaust temperature.
In some instances, the machine learning model is retrained, or recalibrated, based on updated empirical data. In such instances, the engine coupled to the controller is ran in various operating modes. Data is received from various sensors (including an emissions sensor) within various operating modes. The machine learning model stored within the non-transitory memory 552 is then retraining based on the received data, such as received sensor values. After the model is retrained, the controller determines (using the model) emissions based on the sensor values. More specifically, emission values are determined without the use of an emission sensor. The controller then adjusts operation parameters of the engine based on the determined emissions, for example, to maintain emissions below a regulatory threshold.
Periodically, the machine learning model can be recalibrated based on updated empirical data. Such recalibration can be performed on a periodic basis per applicable regulations and standards. In such instances, the gas-turbine system 100 can be run in various operating modes. For example, start-up, shut-down, low load, medium load, and high load operating modes. In some implementations, only three operating modes are used. While operating in the various mode, data is received from various sensors. An emissions sensor is included during this phase. The model is retrained (or further trained) based on the received data from the recalibration runs. As the model has previously been trained on both empirical and physics model data, a low number of data points is needed in each mode, For example, less than ten data points. In some implementations, only nine data points are needed during each operating mode. That is, only nine data points in three operating modes are necessary to retrain the model. For example, if three operating modes are retrained, twenty-seven data points are taken for retraining. Once the model is retrained, emissions can be determined based solely on sensor values, for example, values indicative of a compressor exit temperature, a compressor exit pressure, a fuel mass-flow rate, an ambient temperature, an ambient relative humidity, a power turbine inlet temperature, and/or an average power turbine exhaust temperature. An emissions sensor or a value indicative of an emission level from an emission sensor is not needed. Engine operating parameters are then adjusted based on the determined emissions, for example, the operations are adjusted such that the emission levels are below a specified threshold.
While this disclosure contains many specific implementation details, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features specific to particular implementations of particular inventions. Certain features that are described in this disclosure in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, and it should be understood that the described components and systems can generally be integrated together in a single product or packaged into multiple products.
Thus, particular implementations of the subject matter have been described. Other implementations are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results.
Other implementations can be within the scope of the following claims.
