Combustion heater control systems and methods that include dynamic safety settings. Current operating parameters of the combustion heater are sensed at a plurality of time intervals and converted into a time-varying signal. The time-varying signal is compared to a burner stability envelope indicating when a burner is likely to enter an unstable state. The unstable state may include huffing, flashback, and/or liftoff. When the burner is likely to enter an unstable state, the combustion heater is controlled to prevent the unstable state.

This disclosure relates to systems and methods of predicting physical parameters for a combustion fuel system. In one embodiment of the disclosure, a method of predicting physical parameters of a combustion fuel system includes causing water injection in at least one combustor. The water injection is associated with at least one time and performed during gaseous fuel operations or after liquid fuel operations. The method includes measuring exhaust spread data associated with the water injection and allows correlating the exhaust spread data to at least one physical parameter associated with a nozzle or a valve of the fuel system. The method further includes storing the exhaust spread data, the at least one physical parameter, and the at least one time to a database. The method further provides stored historical data from the database to an analytical model. The analytical model is operable to predict, based at least partially on the stored historical data, at least one future physical parameter associated with a future time.

Systems and methods for monitoring emissions of a combusted gas are provided. The method includes determining a first net heating value of a flare gas. The method also includes determining a second net heating value of a combustion gas including the flare gas. The second net heating value can be determined based upon the first net heating value and a volumetric flow rate of the flare gas. Based upon the value of the second net heating value, an empirical model or a non-parametric machine learning model can be selected. A combustion efficiency of the combustion gas can be determined using the selected model, the second net heating value, and selected ones of the process conditions and the environmental conditions. Total emissions of the combustion mixture can be further determined from the combustion efficiency and a volumetric flow rate of the combustion gas.

Combustion heater control systems and methods that include dynamic safety settings. Current operating parameters of the combustion heater are sensed at a plurality of time intervals and converted into a time-varying signal. The time-varying signal is compared to a burner stability envelope indicating when a burner is likely to enter an unstable state. The unstable state may include huffing, flashback, and/or liftoff. When the burner is likely to enter an unstable state, the combustion heater is controlled to prevent the unstable state.

A method and an assembly for controlling an internal combustion engine having multiple burners is provided. Combustion measurement data is collected in a burner-specific manner for each burner and assigned to a burner identification identifying the respective burner. Performance measurement data of the internal combustion engine is also collected and used to determine a performance value. A machine learning model is trained by means of the combustion measurement data, the associated burner identifications and the performance measurement data, to generate burner-specific control data which optimizes the performance value when the burners are actuated in a burner-specific manner using the control data. The control data generated by the trained machine learning model is output for the burner-specific actuation of the burners.

A boiler coal saving control method includes a linear relation model creating step, an optimization target determination step, and a machine learning step. The linear relation model creating step includes creating a multi-grade model grading mechanism and creating linear relation models accordingly so as to fill an empty set in a data set. The multi-grade model grading mechanism includes performing primary grading based on boiler load, coal quality, and ambient temperature, and secondary grading based on boiler load. The optimization target determination step includes determining a boiler optimization target that includes boiler combustion efficiency and a nitrate concentration control value for flue gas. The machine learning step performs machine learning according to a data source and includes a model numbering sub-step, an ontology determination sub-step, and a target optimization sub-step. The control method uses machine learning to provide an operation recommendation for improving boiler combustion efficiency and thereby saving coal.

A method operates a furnace unit with a feed chute and a camera for capturing an image of the surface of the chute. The chute includes a slide on which material flows to a grate, and the coverage of the chute and in particular of the slide with material, the burning bed thickness and the burnout zone are determined by an image evaluation.

A machine, method of making, and method of using, along with necessary intermediates, illustratively, by way of a method, there can be a method of generating electrical power, the method including: inputting air, including adjusting flow rate of the air; inputting fuel, including throttling flow rate of the fuel, wherein: the fuel flow rate and the air flow rate are in stoichiometric proportions for combustion, and the fuel is comprised of at least one hydrocarbon, alcohol, or both; combusting a mixture of the fuel and a portion of the air with the remainder of the air to produce heat, wherein: prior to the combusting: combining the portion of the air with the fuel to produce the mixture that, when heated, stoichiometrically forms syngas; heating the mixture with the heat from the combusting; heating the remainder of the air with the heat from the combusting; and during the combusting, matching the remainder of the air with at least one of flow rate, pressure drop, and flow velocity of the mixture; generating electromagnetic emissions from the heat; harvesting the electromagnetic emissions with photovoltaic elements to produce electrical power; processing exhaust gasses produced during the combusting, wherein heat released from the processing is transferred into the mixture and the remainder of the air before the combusting, and the processing removes one or more pollutants from the exhaust gasses; measuring the oxygen content of the exhaust gasses before the processing in ensuring the stoichiometric proportions.

A flame analytics system that may incorporate a burner, one or more sensors at the burner, a historical database connected to the one or more sensors, a model training module connected to the historical database, and a runtime algorithm module connected to the one or more sensors and the model training module. The runtime algorithm may compare realtime data from the one or more sensors and historical data from the model training module in accordance with a machine learning algorithm. The system may further incorporate a fault detection module connected to the runtime algorithm module, a fault diagnostics module connected to the fault detection module, and an enunciator connected to the fault detection module. The one or more sensors may also include having video or acoustic sensitivity of combustion in the burner.

A system that includes: a gas turbine having a combustion system; a control system operably connected to the gas turbine for controlling an operation thereof; and a combustion auto-tuner, which is communicatively linked to the control system, that includes an optimization system having an empirical model of the combustion system and an optimizer; sensors configured to measure the inputs and outputs of the combustion system; a hardware processor; and machine-readable storage medium on which is stored instructions that cause the hardware processor to execute a tuning process for tuning the operation of the combustion system. The tuning process includes the steps of: receiving current measurements from the sensors for the inputs and outputs; given the current measurements received from the sensors, using the optimization system to calculate an optimized control solution for the combustion system; and communicating the optimized control solution to the control system.