Multi-Burner Engine Control via Machine Learning Segmentation
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Solution Overview
Problem
Modern combustion engines with multiple burners face challenges in optimizing operation to reduce pressure fluctuations, temperature peaks, and pollutant emissions while increasing power and efficiency, as these objectives are often mutually opposed and difficult to achieve simultaneously.
Innovation Solution
A method and arrangement that record burner-specific combustion measurement data and performance data to train a machine learning model for generating burner-specific control data, optimizing the combustion process by adjusting fuel flow, mixture ratio, and injection pressure, using multi-way valves for fuel stage-specific control and potentially employing reinforcement learning methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate annular lines are provided for each fuel stage to control multiple burners, then the combustion process can be optimized through adjusted fuel stages, but the device complexity and piping outlay increase
Solution Approach 1:
The system segments the control function by providing separate annular lines for different fuel stages (first fuel stage annular line and second fuel stage annular line), allowing independent control of fuel distribution to multiple burners. This segmentation enables optimized combustion control for each fuel stage while maintaining a manageable piping structure through shared infrastructure where possible.
Solution Approach 2:
The annular lines are designed to serve multiple burners simultaneously, with each annular line functioning as a universal distribution system for its respective fuel stage. The first and second annular lines can supply fuel to multiple burners in the first and second groups respectively, reducing the need for individual piping to each burner while maintaining control flexibility.
2Stability of the object's composition
If fuel stages are adjusted to reduce pressure fluctuations and temperature peaks, then combustion stability improves, but the power output and efficiency may be reduced
Solution Approach 1:
The system employs periodic fuel stage adjustment through the controlled operation of first and second fuel stage valves, which modulate fuel flow in a periodic manner to maintain stable combustion. The valves open and close in a coordinated sequence, creating periodic fuel supply patterns that stabilize combustion while allowing for power optimization through adjusted duty cycles and timing.
Solution Approach 2:
The system changes operational parameters by adjusting the fuel/air mixture ratios and fuel flow rates in different fuel stages. The first and second fuel stages use different mixture ratios and flow rates to optimize combustion stability, while the control system dynamically adjusts these parameters to maintain power output. The annular lines distribute fuel at different pressures and flow rates to achieve stable combustion without sacrificing power.
3Adaptability or versatility
If burner-specific control data are generated using machine learning models, then adaptive optimization of combustion performance is achieved, but the complexity of the control system increases
Solution Approach 1:
The machine learning model implements a feedback mechanism by continuously receiving combustion measurement data from sensors monitoring pressure, temperature, and fuel flow in real-time. The model processes this feedback information and dynamically adjusts the burner-specific control data, which in turn modifies fuel stage valve operations. This closed-loop feedback system enables adaptive optimization while managing complexity through algorithmic automation rather than manual control system expansion.
Solution Approach 2:
The machine learning model operates autonomously to generate and update burner-specific control data without requiring constant external intervention. The system self-adjusts by processing combustion measurement data and automatically optimizing fuel distribution patterns for each burner group. This self-service capability reduces the operational complexity burden on operators while maintaining high adaptability through continuous autonomous optimization.
Data Source
AI summary
A method and an assembly for controlling an internal combustion engine having multiple burners is provided. Combustion measurement data is collected in a burner-specific manner for each burner and assigned to a burner identification identifying the respective burner. Performance measurement data of the internal combustion engine is also collected and used to determine a performance value. A machine learning model is trained by means of the combustion measurement data, the associated burner identifications and the performance measurement data, to generate burner-specific control data which optimizes the performance value when the burners are actuated in a burner-specific manner using the control data. The control data generated by the trained machine learning model is output for the burner-specific actuation of the burners.