Boiler Combustion Modeling for Real-Time Control Without Skilled Tuning

The system employs self-learning algorithms that automatically acquire and analyze boiler operating data, enabling the system to optimize combustion parameters autonomously without relying on skilled operators. The algorithm continuously learns from operational data and automatically adjusts control variables to achieve optimal combustion states.

The patent replaces manual mechanical adjustment by skilled operators with an automated computational system. The self-learning algorithm processes operating data and calculates optimal control variables, substituting human expertise with an automated intelligence-based control mechanism.

The self-learning algorithm serves multiple functions: it acquires operating data, analyzes combustion states, determines optimal control parameters, and adjusts control variables. This multi-functional approach consolidates what would otherwise require separate systems into a single integrated intelligent controller.

The patent introduces a self-learning algorithm as an intermediary between raw operating data and control decisions. This algorithm acts as a mediator that processes complex data streams and translates them into optimized control actions, simplifying the overall system architecture.

The patent divides multiple control targets into distinct groups or categories. The self-learning algorithm processes and optimizes different control variables in an organized sequence, breaking down the complex multi-parameter optimization problem into manageable segments that can be handled efficiently.

The system dynamically adjusts the optimization process based on the interrelationships between control targets. The self-learning algorithm identifies and prioritizes critical control variables, adapting the calculation approach to minimize computation time while maintaining overall combustion efficiency.