Neural Network Boiler Control for Waste Incineration
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Solution Overview
Problem
Waste incineration plant boilers are challenging to control efficiently due to their complexity, requiring human operators and resulting in costly and error-prone processes, with existing methods struggling to achieve optimal combustion efficiency and residue reduction.
Innovation Solution
A trained neural network is used to control the boiler by processing input data from parameters like boiler drum pressure, combustion chamber temperature, and air duct systems, generating output data for control elements such as air dampers, enabling automated or quasi-automated control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human operators control the boiler, then the system can handle complex decision-making, but the control becomes costly and error-prone
Solution Approach 1:
The neural network enables the boiler control system to serve itself by automatically processing sensor data and generating control commands without human intervention. The system learns optimal control strategies through training data and autonomously adjusts combustion parameters, eliminating dependence on human operators while improving reliability and reducing costs.
2Productivity
If conventional control methods are used, then the system is simple to implement, but combustion efficiency and residue reduction are suboptimal
Solution Approach 1:
The patent replaces conventional mechanical control systems with an intelligent neural network-based control system. The neural network processes multiple sensor inputs and generates optimized control commands, achieving superior combustion efficiency and residue reduction despite the increased computational complexity, which is manageable through modern computing resources.
3Measurement precision
If more parameters are considered for control, then the control precision improves, but the system complexity increases
Solution Approach 1:
The neural network serves as a universal control mechanism that can process multiple different parameter types (temperature, pressure, flow rates, oxygen content) through a single integrated architecture. This multi-functional approach allows the system to consider numerous parameters for precise control without proportionally increasing system complexity, as the neural network handles diverse inputs through standardized processing layers.
Data Source
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AI summary
This disclosure specifies a method for controlling a boiler of a waste incineration plant by a trained neural network, comprising: - inputting input data into the trained neural network, wherein the input data is based on parameters that include at least a boiler drum pressure, a combustion chamber temperature, a parameter of a waste feeding system (1), a parameter of a combustion air duct system (HLA), a parameter of a flue gas discharge duct system (HNA), and a parameter of a live steam duct system (LBA); and - computing and outputting output data by the trained neural network, wherein the output data includes at least control data for at least one control element that contains at least one air damper of the combustion air duct system (HLA).