Doosan Heavy Industries & Construction Co., Ltd

Doosan Enerbility Co., Ltd.

Other

Doosan Enerbnity Co., Ltd.

Jee Hun Park

Hyun Sik Kim

Sang Gun Na

Jwa Young Maeng

Jun Woo Yoo

Young Min Kim

Jae Hyeon Park

Sang Jin Lee

Seung Gyun Cheong

In Suk Cho

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.

Apparatus for combustion optimization and method therefor

Disclosed is a fault signal recovery system including a data processor configured to generate a signal subset U* by removing, from a signal set U for a plurality of tags, some tags including a fault signal, and a first learning signal subset X* by removing tags disposed at positions corresponding to the some tags from a learning signal set X containing only tags of normal signals, a modeling unit configured to generate feature information F extractable from the first learning signal subset X* and recovery information P on a plurality of recovery models usable for restoring the fault signal, and a recovery unit configured to estimate and recover normal signals for the some tags based on the signal subset U*, the first learning signal subset X*, the feature information F, the recovery information P on the plurality of recovery models, and similarity Z.

Fault signal recovery system and method

An apparatus for managing combustion optimization is provided. The apparatus for managing combustion optimization includes a data collector configured to collect real-time data that is measured in real time from a boiler system including a boiler and a combustion controller configured to control combustion of the boiler, a management configured to determine whether to perform combustion optimization of the boiler based on the real-time data, and an executor configured to generate a control command and transmit the control command to the combustion controller to perform the combustion optimization of the boiler in response to determining that the combustion optimization of the boiler is possible.

Apparatus for managing combustion optimization and method therefor

An apparatus for diagnosing failure of a plant is provided. The apparatus for diagnosing failure of a plant includes: a data analyzer configured to provide data analysis information, which is information requiring analysis to diagnose failure of the plant, and a comprehensive diagnostic device configured to diagnose the failure using each of an algorithm-based diagnosing technique and a domain knowledge-based diagnosing technique based on the data analysis information, and to derive comprehensive diagnosis information for the failure by summarizing results of the algorithm-based diagnosing technique and the domain knowledge-based diagnosing technique.

Apparatus and method for diagnosing failure of plant

An apparatus and method for generating learning data for combustion optimization is provided. The apparatus includes a data pre-processor to collect raw data including currently measured real-time data for boiler combustion and previously measured past data for the boiler combustion, and to perform pre-processing for the collected raw data, and a data analyzer to derive learning data from the raw data by analyzing the raw data. An apparatus for combustion optimization includes a management layer to collect currently measured real-time data for boiler combustion, to determine whether to perform combustion optimization, and to determine whether to tune a combustion model and a combustion controller; a data layer to derive learning data from raw data; a model layer to generate the combustion model/controller through the learning data; and an optimal layer to calculate a target value for combustion optimization and to output a control signal according to the calculated target value.

Apparatus for generating learning data for combustion optimization and method therefor

A system and method predict whether a plant is abnormal by modeling a relationship equation between tags based on a correlation between the tags, applicable even if modeling is executed without understanding a target to abnormality determination, and implements internal early alarm logic based on a difference between measured data and predicted data over time. The plant abnormality prediction system includes a modeling information output unit including a pre-processing part for pre-processing past data received for a plurality of tags, a correlation analysis part for receiving the pre-processed data for each tag to determine an independent tag among the plurality of tags based on correlation coefficients for any two tags, and a modeling part for generating a relationship equation between the tags by using outputs of the pre-processing part and the correlation analysis part; and a prediction unit for calculating estimated data for the tag based on the relationship equation.

Plant abnormality prediction system and method

A system and method for configuring a boiler combustion model are provided. The system for configuring the boiler combustion model may include a model generator configured to generate the boiler combustion model using, as input/output data, data obtained based on measured data, analysis data, and controller information, a model simulator configured to simulate the generated boiler combustion model and output simulated results, and a model modifier configured to evaluate the boiler combustion model based on the simulated results and generate modification information for modifying the boiler combustion model based on the generated boiler combustion model and corresponding evaluated results.

System and method for configuring boiler combustion model

A failure prediction model generating apparatus and method thereof are provided. The failure prediction model generating apparatus includes a memory configured to store a plurality of failure prediction models derived previously; and a processor configured to predict a failure of the plant, wherein the processor is configured to collect data measured from the plant, select at least one failure prediction model from among the plurality of failure prediction models using the collected data, and predict a failure of the plant using the selected failure prediction model.

Failure prediction model generating apparatus and method thereof

Disclosed is a fault signal recovery system including a data processor configured to generate a signal subset U* by removing, from a signal set U for a plurality of tags, some tags including a fault signal, and a first learning signal subset X* by removing tags disposed at positions corresponding to the some tags from a learning signal set X containing only tags of normal signals, a modeling unit configured to generate feature information F extractable from the first learning signal subset X* and recovery information P on a plurality of recovery models usable for restoring the fault signal, and a recovery unit configured to select an optimum recovery model by matching the feature of the learning signal set X with the recovery models generated through the recovery in formation P to estimate and recover normal signals for the some tags.

A system for generating learning data is provided. The system for generating the learning data includes a data incorporating part configured to generate new data by filtering plant data based on a warning condition to incorporate the plant data for one configuration of a plant into existing learning data and a learning data generating part configured to differentiate a weight applied to the new data and the existing learning data, respectively, by comparing the number of the new data and the number of the existing learning data, and generate new learning data by combining the new data with the existing learning data to which the weight is applied.

System for generating learning data

A system for controlling a boiler apparatus in a power plant to combust under optimized conditions, and a method for optimizing combustion of the boiler apparatus using the same are provided. The boiler control system may include a task manager configured to collect information on a current operating state of a boiler and determine whether to perform a combustion optimization operation for the boiler, a pre-processor configured to preprocess data collected from the boiler and supply the pre-processed data, a modeler configured to create a boiler combustion model on the basis of the pre-processed data received from the pre-processor, an optimizer configured to receive the boiler combustion model from the modeler and perform the combustion optimization operation for the boiler using the boiler combustion model to calculate an optimum control value, wherein the pre-processed data is supplied to the modeler and the optimizer by the pre-processor, and an output controller configured to receive the optimum control value from the optimizer and control an operation of the boiler by reflecting the optimum control value to a boiler control logic.

System and method for optimizing combustion of boiler

An apparatus and method for diagnosing analysis is provided. The apparatus includes an analytic layer to divide a peripheral space of a target component into a plurality of cells and to derive analytic data by performing a numerical analysis iteration according to computational fluid dynamics for the plurality of cells; a model layer to derive an analytic model that simulates the numerical analysis iteration; a predictive layer to derive predictive data by predicting a result of the numerical analysis iteration by using the analytic model; and a diagnostic layer to diagnose an abnormality condition of numerical analysis by comparing the analytic data and predictive data during the numerical analysis iteration performed by the analytic layer. The diagnostic layer includes an early alarm to generate early alarm information by sorting a cell satisfying an early alarm condition; and an abnormality diagnostic device to determine whether the numerical analysis iteration is abnormal.

Apparatus for diagnosing analysis and method therefor

The present disclosure relates to an exhaust gas cooling device and method, and more particularly, to a device and method for installing an exhaust gas cooling device on the upper end of a duct of a heat recovery steam generator to cheaply cool the exhaust gas without occupying an additional dedicated area. An object of the present disclosure is to reduce the costs using a cheap cooling device in the cooling path for cooling the exhaust gas. In one aspect, the exhaust gas cooling device includes an exhaust gas cooling unit located on the upper end of a duct of a heat recovery steam generator connected with a gas turbine and for cooling the exhaust gas discharged from the gas turbine; and a control unit for controlling the exhaust gas cooling unit to lower the increase rate of the energy of the exhaust gas flowed into the heat recovery steam generator through the duct.

Exhaust gas cooling device and method

A fault signal recovery apparatus and method for collecting signals obtained in a plant and recovering normal signals from fault signals contained in the measured signals through a machine learning method includes receiving an input X including only normal signals for a plurality of tags, an input U including fault signals for a first group of tags among the plurality of tags and normal signals for a second group of tags, and an input S having information on the first group of tags including fault signals, and estimating and recovering normal signals for the first group of tags including fault signals based on feature information F, recovery model information P, and ensemble learning.

Fault signal recovery apparatus and method

A system and method predict whether or not a plant is abnormal and perform an accurate prediction even if a modeling is executed in a state where the understanding for a target to abnormality determination is low, or when a person unfamiliar with system designs a prediction model. The system includes a correlation coefficient calculation unit for calculating a correlation coefficient for each of two tags among a plurality of tags; a relevant tag determination unit for determining a relevant tag for each tag of the plurality of tags by comparing the correlation coefficient with a reference value; and an independent tag determination unit for determining one or more among the plurality of tags as an independent tag based on the relevant tag. The relevant tag determination unit includes primary and second tag extraction sections for extracting primary and second tags for each tag of the plurality of tags.

Growing community of inventors

Gwangmyeong-si, South Korea

Jee Hun Park