A system for controlling a boiler in a power plant to ensure combust under optimized conditions is provided. The system for controlling an operation of the boiler may include an optimizer configured to perform a combustion optimization operation for the boiler using a boiler combustion model to calculate an optimum control value for at least one control object of the boiler, and an output controller configured to receive the calculated optimum control value from the optimizer and control the control object according to the optimum control value.

A system for controlling a boiler in a power plant to ensure combust under optimized conditions is provided. The system for controlling an operation of the boiler may include an optimizer configured to perform a combustion optimization operation for the boiler using a boiler combustion model to calculate an optimum control value for at least one control object of the boiler, and an output controller configured to receive the calculated optimum control value from the optimizer and control the control object according to the optimum control value.

Reduction of operation efficiency of a GTCS at a time of changing an operation using bypass stack to an operation using HRSG is suppressed. An HRSG of the GTCS is provided with an air supply piping and a ventilation piping connected to a fuel line of a duct burner at a position upstream of a main shut-off valve and downstream of a fuel shut-off valve, an air supply shut-off valve that opens/closes the air supply piping, and a ventilation shut-off valve that opens/closes the ventilation piping, and is configured such that during an operation using a bypass stack, an inlet of the HRSG is closed to open a bypass stack, a main shut-off valve and the fuel shut-off valve are closed, and the air supply shut-off valve and the ventilation shut-off valve are always opened, and at a time of changing to an operation using HRSG, the inlet of the HRSG is opened to close the bypass stack without shutting down a GT, the main shut-off valve and the fuel shut-off valve are opened, and the air supply shut-off valve and the ventilation shut-off valve are closed.

A method for controlling a coal supply quantity during a transient load-varying process considering exergy storage correction of a boiler system of a coal-fired unit is provided. Temperatures and pressures of working fluid and metal heating surface of the boiler system of the coal-fired unit are measured and recorded in real-time, and converted into the exergy storage amount at different operating load points. During the transient operation process, the real-time exergy storage amount of the boiler system is compared with the exergy storage amount at the corresponding steady-state load point, and the real-time exergy storage variation is obtained; thereafter, the feed-forward control signal of coal supply quantity input is superposed to the existing coal supply quantity command of the boiler system, and the coal supply quantity signal of the boiler system based on the exergy storage correction is finally generated. The method provided by the present invention performs feed-forward correction to the coal supply quantity at the boiler inlet, utilizing the exergy storage difference of the boiler system during the transient process, so as to realize the dynamic accurate control of the inlet coal quantity, ensure the stability of the parameter of each thermodynamic device, and weaken the effects of thermal inertia and time delay, thereby greatly improving the operational flexibility of the boiler system of the coal-fired unit during the transient process with ensuring the economy and safety.

A method for controlling a coal supply quantity during a transient load-varying process considering exergy storage correction of a boiler system of a coal-fired unit is provided. Temperatures and pressures of working fluid and metal heating surface of the boiler system of the coal-fired unit are measured and recorded in real-time, and converted into the exergy storage amount at different operating load points. During the transient operation process, the real-time exergy storage amount of the boiler system is compared with the exergy storage amount at the corresponding steady-state load point, and the real-time exergy storage variation is obtained; thereafter, the feed-forward control signal of coal supply quantity input is superposed to the existing coal supply quantity command of the boiler system, and the coal supply quantity signal of the boiler system based on the exergy storage correction is finally generated. The method provided by the present invention performs feed-forward correction to the coal supply quantity at the boiler inlet, utilizing the exergy storage difference of the boiler system during the transient process, so as to realize the dynamic accurate control of the inlet coal quantity, ensure the stability of the parameter of each thermodynamic device, and weaken the effects of thermal inertia and time delay, thereby greatly improving the operational flexibility of the boiler system of the coal-fired unit during the transient process with ensuring the economy and safety.

A method and apparatus for monitoring operating data of a boiler system based on a Bayesian network are provided. The method includes: S1: establishing a boiler system state model according to association relationships between various components of a boiler system and different positions of the various components; S2: collecting operating states of the various components and the operating states of the various components at the different positions by a sensor to obtain a boiler system observation model; S3: obtaining a boiler system model, combining the boiler system state model and the boiler system observation model; and S4: according to the boiler system model, inferring missing observation data and determining whether the missing observation data is abnormal. The method and apparatus construct a device-operating model based on the Bayesian network, monitor a correctness of data by the boiler system model, and completely supply the missing observation data.

A test planning device builds up a boiler model data by using a plurality of input parameters of the boiler classified into a plurality of parameter groups. The apparatus selects one of the plurality of parameter groups as a parameter group subjected to learning, presents test conditions in which an input parameter thereof is defined as a variable, and an input parameter of a parameter group not subjected to learning is defined as a fixed value. The device modifies the model data on the basis of the result of comparison between an actual process value and a virtual process value using the present test conditions, selects a new parameter groups subjected to learning, and presents new test conditions which use the input parameter of the test conditions in which the input parameter of the previous parameter group subjected to learning is optimal, as the fixed value.

A test planning device builds up a boiler model data by using a plurality of input parameters of the boiler classified into a plurality of parameter groups. The apparatus selects one of the plurality of parameter groups as a parameter group subjected to learning, presents test conditions in which an input parameter thereof is defined as a variable, and an input parameter of a parameter group not subjected to learning is defined as a fixed value. The device modifies the model data on the basis of the result of comparison between an actual process value and a virtual process value using the present test conditions, selects a new parameter groups subjected to learning, and presents new test conditions which use the input parameter of the test conditions in which the input parameter of the previous parameter group subjected to learning is optimal, as the fixed value.

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 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.