AI Boiler Operation Number Control for Variable Load Efficiency
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Solution Overview
Problem
Existing industrial boiler operation control methods struggle to adapt to variability in loads and changes in boiler performance, leading to inefficiencies in energy consumption.
Innovation Solution
A method that utilizes AI to analyze operation data from industrial boilers, deriving optimal operating combinations and control values based on field-specific characteristics, rather than relying on test data from manufacturer factories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If operation number control is performed based on factory test data and predefined sequences, then the control system is simple to implement, but it cannot adapt to variability in field loads and changes in boiler performance
Solution Approach 1:
The patent implements dynamic operation number control that automatically adjusts the number of operating boilers based on real-time load conditions and boiler performance data. The system transitions from static factory-defined sequences to dynamic, condition-based control that adapts to field variability, resolving the contradiction between adaptability and complexity by using automated decision logic rather than manual reconfiguration
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor load conditions and boiler performance, then use this information to adjust operation numbers. The control system receives feedback from field operations and automatically modifies its control strategy, enabling adaptability to load variability while maintaining systematic control rather than requiring complex manual intervention
2Measurement precision
If factory test efficiency data is used for operation control, then the control method is simple, but it does not reflect actual field operating conditions and performance changes
Solution Approach 1:
The system performs preliminary data collection and analysis by continuously gathering operation data from field boilers and pre-processing it for efficiency calculations. This preliminary action ensures that accurate field-based efficiency data is ready when needed for control decisions, resolving the contradiction between measurement precision and time loss by preparing data in advance rather than calculating it on-demand
Solution Approach 2:
The control system serves itself by automatically collecting, processing, and analyzing its own operation data to determine efficiency metrics. Rather than requiring external manual measurement and data entry, the system self-generates the precise field-based efficiency data needed for control decisions, eliminating time losses associated with external data collection while maintaining high measurement accuracy
3Adaptability or versatility
If the number of boilers is increased to handle variable loads, then load coverage is improved, but energy consumption increases due to suboptimal operation numbers
Solution Approach 1:
The system optimizes energy consumption by dynamically changing the operation number parameter based on load conditions and boiler-specific efficiency data. Rather than running a fixed number of boilers regardless of conditions, the system adjusts this critical parameter to match actual demands and performance characteristics, resolving the contradiction between load coverage and energy loss by using data-driven parameter optimization
Solution Approach 2:
The control system applies local quality optimization by selecting specific boilers to operate based on their individual performance characteristics and current conditions. Rather than simply turning on any available boiler, the system chooses the optimal combination of units that best matches the load requirements and efficiency profile, enabling effective load coverage while minimizing energy consumption through localized optimization
Data Source
AI summary
Provided are AI-based optimal operation number control system and method for increasing operation efficiency of industrial boilers. A boiler control method according to an embodiment includes: collecting operation data of boilers; deriving operating boiler combinations by inputting the collected operation data to an AI model that is trained to receive operation data and to derive operating boiler combinations; and controlling operation of the boilers according to the derived operating boiler combinations. Accordingly, by analyzing operating conditions changeable according to a schedule of the field and using the operating conditions for deriving a control value, operation efficiency of industrial boilers are increased.