AI Boiler Combustion Control for Efficiency and Emissions
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Solution Overview
Problem
Current boiler control methods in thermal power plants prioritize stable combustion over optimal conditions, leading to suboptimal combustion environments and increased emissions, requiring improved automation for real-time data analysis and control adjustments.
Innovation Solution
A system utilizing an artificial intelligence algorithm to generate a boiler combustion model, optimize control values, and control the boiler operation, allowing for cost, emission, or equipment protection prioritization through a weighted objective function, enabling improved combustion efficiency and reduced emissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic real-time data analysis and control adjustment are implemented, then combustion efficiency is improved, but device complexity increases
Solution Approach 1:
The system employs self-learning algorithms that automatically analyze operational data and adjust control parameters without human intervention. The controller continuously learns from real-time data, enabling the boiler system to optimize its own combustion process autonomously, thereby improving combustion efficiency while managing complexity through automation rather than manual control mechanisms
Solution Approach 2:
The patent replaces traditional mechanical control methods with intelligent software-based systems. Instead of complex physical control mechanisms, the system uses algorithms that process operational data and generate control commands, substituting mechanical complexity with computational intelligence to achieve superior combustion efficiency
2Reliability
If skilled expert manual control is used, then stable combustion is achieved, but combustion optimization is limited
Solution Approach 1:
The system continuously monitors operational data and uses this feedback to adjust control parameters in real-time. By analyzing the relationship between control inputs and combustion outcomes, the system learns to maintain stable combustion while continuously optimizing efficiency, surpassing manual expert control that lacks real-time adaptive feedback
Solution Approach 2:
The controller performs self-learning from operational data, automatically improving its control strategy over time. This self-service capability enables the system to maintain combustion stability while continuously seeking optimization opportunities that manual control cannot discover, bridging the gap between stability and efficiency
3Object-generated harmful factors
If emission reduction is prioritized, then environmental performance is improved, but combustion efficiency may decrease
Solution Approach 1:
The system dynamically adjusts combustion parameters based on learned relationships between control inputs and emissions. By continuously optimizing parameters such as air-to-fuel ratio, excess oxygen levels, and combustion temperature, the system achieves emission reduction without sacrificing combustion efficiency, as the intelligent control finds optimal parameter combinations that balance both objectives
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
The system enhances combustion efficiency and minimizes pollutant emissions, reducing operational costs and allowing unskilled operators to maintain optimal combustion environments comparable to those achieved by skilled experts.
Implementation Method 1
A thermal power plant has a boiler therein to heat water by using an exothermic reaction generated when burning fuel such as coal or the like, thereby producing steam
Implementation Method 2
When a combustion occurs in the boiler, emissions such as nitrogen oxides and carbon dioxide are generated
Data Source
AI summary
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 modeler configured to create a boiler combustion model, 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, 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.