Boiler Combustion Model Automation via Event-Driven Learning
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Solution Overview
Problem
Boilers in coal-fired power plants face inefficiencies due to increased costs from polluted exhaust gas and reduced combustion efficiency, necessitating a solution to optimize boiler combustion.
Innovation Solution
An apparatus and method for automatically generating a boiler combustion model through automated learning based on real-time data, which includes determining specific events, adjusting training conditions, and generating periodic or aperiodic models to optimize combustion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated learning is performed based on real-time data to generate combustion models, then combustion efficiency is improved and operational costs are reduced, but device complexity and data processing requirements increase
Solution Approach 1:
The system performs automated learning and model generation using real-time data from the boiler itself, without requiring external intervention. The combustion model automatically learns from operational data, detects specific events, and updates itself, enabling the system to optimize its own performance while reducing manual complexity
Solution Approach 2:
The system continuously monitors real-time operational data from sensors and uses this feedback to train and update combustion models. The generated models then provide optimization recommendations that are fed back to control systems, creating a closed-loop system that improves combustion efficiency while managing complexity through automated feedback mechanisms
2Measurement precision
If periodic model generation is performed at regular intervals, then model precision is maintained, but loss of time and computational resources increase
Solution Approach 1:
The system dynamically adjusts model generation timing based on detected specific events rather than following a fixed periodic schedule. When events such as operational changes or anomalies are detected, the system generates models aperiodically to maintain precision only when necessary, thereby reducing unnecessary computational time and resource loss
Solution Approach 2:
The system changes the temporal parameter of model generation from a fixed periodic interval to an event-driven variable schedule. This allows the model generation frequency to adapt based on operational conditions, maintaining precision when events occur while minimizing time loss during stable operational periods
3Measurement precision
If abnormal operation data is deleted or converted before training, then model precision is improved, but loss of information increases
Solution Approach 1:
The system applies different data processing treatments to different portions of the operational data based on local characteristics. Abnormal data points are selectively converted or deleted only when they would negatively impact model training, while preserving other informative data. This localized approach maintains model precision without unnecessary information loss
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces the generation of polluted exhaust gas, enhances combustion efficiency, and lowers operational costs by continuously learning from real-time data to optimize boiler performance.
Implementation Method 1
automatically generating a boiler combustion model periodically or aperiodically through automated learning which is performed based on data of real-time measurements
Data Source
AI summary
A method and apparatus for automatically generating periodic boiler combustion models and aperiodic boiler combustion models through automatic learning are provided. The method of automatically generating a boiler combustion model may include determining whether a specific event has occurred in association with a boiler, changing a training condition according to a result of the determining, generating a boiler combustion model trained on operation data measured in the boiler and stored in a database according to the training condition, and determining a precision of the generated boiler combustion model.