Boiler Combustion Control Using Real-Time Model Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Coal-fired power plant boilers face inefficiencies in combustion, leading to increased nitrogen oxide emissions and higher operational costs due to incomplete combustion and excessive exhaust gases, necessitating a method to enhance combustion efficiency while reducing exhaust gas production.
Innovation Solution
An apparatus and method for combustion optimization in boilers, utilizing a management layer to collect real-time data, a data layer to derive learning data, a model layer to generate combustion models and controllers, and an optimal layer to calculate target values for optimizing combustion, thereby reducing exhaust gases while maintaining efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If coal combustion is intensified to increase power generation efficiency, then energy output increases, but nitrogen oxide emissions and exhaust gas volume increase
Solution Approach 1:
The system changes combustion parameters dynamically by adjusting air-fuel ratio, combustion temperature, and oxygen concentration based on real-time monitoring data. The control unit modifies these parameters to optimize combustion efficiency while maintaining nitrogen oxide emissions within acceptable limits, resolving the contradiction between power generation efficiency and harmful emissions.
Solution Approach 2:
The system implements closed-loop feedback control by continuously monitoring combustion parameters and exhaust gas composition, then using this information to adjust combustion conditions. The control unit receives feedback from sensors measuring oxygen concentration, temperature, and emissions, and automatically adjusts air supply and fuel injection to maintain optimal combustion while minimizing nitrogen oxide generation.
2Productivity
If combustion intensity is increased to reduce operational costs, then energy output increases, but incomplete combustion occurs reducing efficiency
Solution Approach 1:
The system enables self-optimizing combustion control where the control unit automatically adjusts combustion parameters based on real-time sensor data without external intervention. The system monitors its own performance through embedded sensors and autonomously modifies air-fuel ratio, injection timing, and combustion chamber conditions to maintain complete combustion at high output levels, eliminating the trade-off between productivity and reliability.
3Ease of operation
If traditional combustion control methods are used to maintain stable operation, then operational simplicity is maintained, but exhaust gas treatment costs increase
Solution Approach 1:
The system replaces traditional mechanical combustion control with an intelligent control unit that uses sensors, processors, and algorithms to optimize combustion. This substitution of mechanical systems with electronic intelligence enables precise control of air-fuel ratio and combustion parameters, reducing nitrogen oxide emissions and exhaust gas treatment costs while maintaining operational simplicity through automated control.
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 solution effectively reduces exhaust gas production while maximizing combustion efficiency, thereby lowering operational costs and improving power generation efficiency in coal-fired power plants.
Implementation Method 1
A boiler of a coal-fired power plant heats water by using the exothermic reaction that occurs during coal combustion and produces steam required for power generation
Data Source
AI summary
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.