Fluid heating system with combustion trim learning
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Solution Overview
Problem
Current combustion control methods for boilers and furnaces do not account for various factors such as venting draft and fuel quality, leading to sub-optimal combustion at specific modulation percentages/firing rates, and fail to adapt when sensors like O2 sensors fail.
Innovation Solution
Implementing learned feedback control loops that adapt based on multiple variables, including air-fuel ratio, NOx concentration, and flame characteristics, using a controller with an electronic processor and memory to adjust combustion settings dynamically, even when sensors like O2 sensors are faulty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If continuous combustion control is used to regulate combustion at all modulation percentages, then combustion control coverage is improved, but control accuracy deteriorates because it cannot account for specific factors at each modulation percentage
Solution Approach 1:
The patent segments the continuous combustion control into discrete modulation percentage points (e.g., 10%, 20%, 30%, etc.). At each segment, specific feedback control is applied using sensors to measure actual combustion parameters and adjust control accordingly. This segmentation allows the system to maintain both broad coverage across all modulation percentages and high accuracy at each specific point.
Solution Approach 2:
The patent implements feedback control loops at each modulation percentage that use sensor data (O2 concentration, CO concentration, flame characteristics) to continuously monitor and adjust combustion parameters. This feedback mechanism ensures that control accuracy is maintained at each discrete modulation point while the overall system covers the full range of modulation percentages.
2Productivity
If learned feedback control loops are implemented to adapt to various factors, then combustion optimization is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary learning during initial operation at each modulation percentage, storing optimal trim values in memory. This preliminary action allows the system to automatically adapt to specific installation conditions, venting characteristics, and fuel properties without requiring complex real-time calculations during normal operation. The learned values are then applied during subsequent operations, simplifying the control logic while maintaining optimization.
Solution Approach 2:
The patent uses sensor measurements to create a model or copy of the actual combustion conditions and uses this information to adjust control parameters. Instead of implementing complex physical adjustments for each variable, the system creates a digital representation of the combustion state and uses this copy to drive control decisions, reducing mechanical complexity while maintaining adaptability.
3Measurement precision
If multiple feedback loops are used to control various combustion parameters, then combustion control precision is improved, but the system becomes more vulnerable to sensor failures
Solution Approach 1:
The system implements redundancy and fallback mechanisms beforehand. When a sensor fails or provides invalid data, the control system switches to using previously learned trim values for that modulation percentage, ensuring continuous operation. This prior cushioning approach maintains reliability by preparing alternative control paths before failures occur, allowing the system to degrade gracefully rather than fail completely.
Solution Approach 2:
The system monitors its own sensor health and automatically adjusts operation when sensors fail. Through self-diagnosis and automatic switching to learned values or alternative control strategies, the system serves itself by detecting and compensating for sensor failures without requiring external intervention, thereby maintaining both precision and reliability.
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
Ensures optimal and efficient combustion control by accounting for various factors and continuing to function effectively even when sensors fail, maintaining proper combustion performance and safety.
Implementation Method 1
controlling combustion of boilers, water heaters, and furnaces
Data Source
AI summary
A fluid heating system including a burner unit is operated based on feedback control loops. The fluid heating system comprises a burner unit configured to heat a fluid, a sensor configured to sense a characteristic of the appliance, and a controller coupled to the burner unit and the sensor. The controller includes an electronic processor and a memory. The controller is configured to receive a first signal corresponding to the characteristic from the sensor, determine, based on the first signal, a first feedback loop control, control combustion of the burner unit based on the first feedback loop control, determine, based on the first feedback loop control, a second feedback loop control, and control combustion of the burner unit based on the second feedback loop control.