AI Air Damper Control for Industrial Boiler Energy Efficiency
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Solution Overview
Problem
Industrial boilers face inefficiencies due to fixed air volume settings derived from experimental conditions, which do not account for varying site environments, leading to energy inefficiencies and increased energy consumption.
Innovation Solution
An AI-based optimal air damper control system that collects operational data, calculates energy efficiency, trains a prediction model to derive optimal air volume conditions, and automatically adjusts the air damper to achieve peak energy efficiency under given load conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed air volume setting derived from experimental conditions is applied uniformly to all boilers of the same model, then the control system is simple and easy to operate, but energy efficiency deteriorates because the fixed setting cannot adapt to varying site environments
Solution Approach 1:
The patent implements dynamic air volume control by replacing fixed settings with a control system that continuously adjusts air damper positions based on real-time operational parameters (fuel consumption, steam production, ambient temperature, pressure). The system transitions from static experimental data to dynamic adaptive control, allowing the air volume to optimize automatically according to changing site conditions while maintaining ease of operation through automated decision-making.
Solution Approach 2:
The patent employs feedback mechanisms by monitoring operational parameters such as fuel consumption rates, steam generation, exhaust gas composition, and ambient conditions. This feedback loop enables the control system to compare actual performance against optimal values and adjust air damper positions accordingly, thereby improving energy efficiency while maintaining simple operation through automated closed-loop control.
2Use of energy by moving object
If an AI-based dynamic control system is implemented to optimize air volume settings for each site condition, then energy efficiency is improved, but device complexity increases due to data collection, processing, and model training requirements
Solution Approach 1:
The patent implements self-service by enabling the control system to automatically collect operational data, process information, train AI models, and adjust air damper positions without requiring external intervention. The system performs self-optimization by learning from its own operational history and environmental conditions, thereby improving energy efficiency while managing complexity through autonomous operation rather than external management.
Solution Approach 2:
The patent replaces traditional mechanical control systems with AI-based intelligent control. Instead of using complex mechanical adjustment mechanisms or manual tuning, the system uses machine learning models and algorithms to automatically determine optimal air damper positions. This substitution reduces operational complexity while improving energy efficiency through more sophisticated decision-making capabilities.
3Reliability
If experimental air volume data is used as the basis for control settings, then the control system is straightforward and reliable, but adaptability to different site conditions deteriorates because experimental data cannot account for ever-changing environmental factors
Solution Approach 1:
The patent applies preliminary action by pre-processing and storing operational data in databases before they are needed for control decisions. The system collects and organizes historical operational information, environmental data, and performance metrics in advance, enabling the AI models to make reliable predictions about optimal air volume settings for various site conditions without requiring real-time complex calculations during operation.
Solution Approach 2:
The patent implements parameter changes by continuously monitoring and adjusting operational parameters such as air damper position, fuel consumption rate, steam production, and ambient conditions. The system dynamically modifies these parameters based on real-time data and AI model predictions, thereby maintaining reliability through data-driven decisions while achieving adaptability to changing site environments through continuous parameter optimization.
Data Source
AI summary
There is provided an AI-based air damper control system and method for industrial boilers. An AI-based optimal air damper control method according to an embodiment calculates energy efficiency under a given control condition and an environment by extracting energy efficiency-related data from industrial boiler operational data and analyzing a correlation between corresponding data, trains an AI-based optimal air volume-for-load prediction model by using the extracted data and the calculated energy efficiency as training data, and derives an air volume condition that results in peak energy efficiency under a given load, based on the trained optimal air volume-for-load prediction model, and automatically controls the air damper according to the corresponding air volume condition.