AI-Controlled Burner System for Adaptive Low-NOx Combustion
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Solution Overview
Problem
Existing fuel burners face inefficiencies and increased pollutant emissions due to variations in operating conditions, hardware positions, and non-optimized fuel and air staging, particularly under stringent NOx emission requirements, leading to issues like increased NOx and CO emissions.
Innovation Solution
A burner system incorporating an artificial intelligence (AI) to control a burner control system, which includes an oxidizer and fuel subsystem, with AI-adjustable trim control curves within defined control envelopes, enabling efficient operation and reduced pollutant emissions by optimizing fuel and oxidizer supply.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional burner control methods are used, then the system structure is simple, but pollutant emissions (NOx and CO) increase under varying operating conditions
Solution Approach 1:
The patent implements dynamic control of fuel and oxidizer flow rates based on real-time operating conditions. The control system continuously adjusts the trim control curve according to actual burner performance, load demands, and environmental parameters, transforming static control into dynamic adaptation to minimize emissions across varying operating zones.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor burner output, emissions levels, and operating parameters. This feedback is used to continuously refine the trim control curve and adjust control decisions, enabling the system to learn from actual performance and improve emission control over time while adapting to changing conditions.
2Object-affected harmful factors
If low NOx technologies are implemented, then NOx emissions are reduced, but the operating zone becomes narrow and efficiency decreases under non-ideal conditions
Solution Approach 1:
The control system dynamically adjusts the trim control curve to expand the viable operating zone. By continuously adapting control parameters based on actual conditions including load swings, pressure variations, and oxygen levels, the system maintains low NOx emissions across a broader range of operating conditions than static low NOx designs.
Solution Approach 2:
The system changes operating parameters dynamically by adjusting the trim control curve in response to varying conditions. This includes modifying fuel-to-oxidizer ratios, staging configurations, and mixing parameters to maintain optimal combustion efficiency and low emissions across different operating zones and load levels.
3Speed
If rapid changes in burner firing rate are made, then responsiveness to load demands improves, but combustion stability deteriorates and emissions increase
Solution Approach 1:
The system implements dynamic control that responds to load changes at optimized rates. The trim control curve adjustment incorporates rate-of-change limitations and predictive algorithms that enable rapid yet stable response to load demands, preventing combustion instability while maintaining responsiveness to varying energy requirements.
4Use of energy by moving object
If operating near stable combustion limits is done, then fuel efficiency improves, but emissions control becomes difficult and operation becomes unstable
Solution Approach 1:
The system uses feedback from emissions sensors and combustion monitors to detect when operation approaches unstable limits. The trim control curve is automatically adjusted to maintain optimal efficiency while preventing operation in zones that generate excessive emissions or combustion instability, balancing fuel efficiency with emissions 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 AI-controlled burner system achieves efficient operation with low NOx emissions (less than 10 ppm) and adaptive performance under varying conditions, maintaining emissions and efficiency levels through continuous learning and adjustment.
Implementation Method 1
fuel is combusted to provide heat for building conditioning, for process application, or for electrical generation
Data Source
AI summary
A burner system includes an artificial intelligence executed on a processing element, a burner control system including: a burner, an oxidizer subsystem, and a fuel subsystem. The artificial intelligence is operative to control the burner control system.