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.

An electronic control device includes: a flow rate calculation unit that calculates a flow rate of intake air based on an output signal of a flow rate measurement device assembled to an intake pipe; a flow rate correction value calculation unit that calculates an average value, a maximum value, and a minimum value of the flow rate of the intake air, calculated by the flow rate calculation unit, during a predetermined period, and an amplitude of a signal with one or more frequencies equal to or higher than a fundamental frequency of the output signal of the flow rate measurement device and included in the output signal of the flow rate measurement device, and calculates a correction value for the flow rate of the intake air based on calculation results; and a flow rate correction unit that corrects the flow rate of the intake air based on the correction value.

Systems and methods for monitoring and controlling burning operations are provided. A method of one embodiment includes igniting oil or gas with a burner (282) during a burning operation and monitoring the burning operation with a camera (290). This monitoring of the burning operation can include acquiring image data for a flame (290) of the burner via the camera and analyzing the acquired image data to detect image features indicative of combustion of the oil or gas via the burner. Additional systems, methods, and devices are also disclosed.

To provide a furnace control system which can predict production of flammable gases produced inside a furnace during melting, incineration, and fusion batch processes and effectively carry out furnace combustion control on the basis of said prediction results in order to reduce the conventional problem of time lag. A furnace control system has a flammable gas quantity of state calculation unit 12 which calculates a flammable gas quantity of state corresponding to prediction factor data using a quantity of state estimation model for flammable gas originating in volatile organic compounds produced using intelligent information processing technology using as learning data past data relating to furnaces, data relating to materials, and data relating to exhaust gases, and a combustion control unit 11 which controls furnace combustion on the basis of the flammable gas quantity of state calculated by the flammable gas quantity of state calculation unit 12.

A control device is a control device for a gas turbine including a plurality of combustors and is configured to select combustors to ignite in accordance with a target load on the basis of a predictor which defines a relationship between a load and the number and arrangement of combustors to ignite and a combustion temperature.

An apparatus for combustion optimization is provided. The apparatus for combustion optimization includes a management layer configured to collect currently measured real-time data for boiler combustion, and to determine whether to perform combustion optimization and whether to tune a combustion model and a combustion controller by analyzing the collected real-time data, a data layer configured to derive learning data necessary for designing the combustion model and the combustion controller from the real-time data and previously measured past data for the boiler combustion, a model layer configured to generate the combustion model and the combustion controller through the learning data, and an optimal layer configured to calculate a target value for the combustion optimization by using the combustion model and the combustion controller, and to output a control signal according to the calculated target value.

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.