Biomass heating system with a control device optimized by means of machine learning and corresponding method
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Solution Overview
Problem
Conventional biomass heating systems face challenges with high gaseous and solid emissions, low efficiency, and varying fuel quality, particularly when handling different types of biological fuels, leading to inefficient combustion and increased pollutant emissions.
Innovation Solution
A biomass heating system utilizing an AI model-based control device optimized through machine learning, which learns from sensor data to optimize combustion processes, air supply, and fuel handling, enabling efficient operation with both wood chips and pellets, and achieving low emissions and high efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional control devices are used in biomass heating systems, then the system structure remains simple, but combustion efficiency is low and emissions are high
Solution Approach 1:
The patent replaces conventional mechanical control systems with an AI-based control device that uses machine learning algorithms to optimize combustion parameters. The control device processes sensor data and adjusts combustion air supply, fuel feed rate, and grate speed dynamically, substituting traditional mechanical regulation with intelligent automated control to achieve higher combustion efficiency.
Solution Approach 2:
The control device implements continuous feedback loops by monitoring combustion parameters through sensors and automatically adjusting control variables. The system measures actual combustion conditions, compares them with optimal values learned through machine learning, and makes real-time adjustments to maintain high combustion efficiency and low emissions.
2Adaptability or versatility
If the heating system is designed for specific fuel types, then combustion efficiency is optimized, but adaptability to different fuels is reduced
Solution Approach 1:
The control device dynamically adapts to different fuel types by continuously learning from sensor data and adjusting combustion parameters in real-time. When a new fuel type is introduced, the system observes combustion characteristics and modifies air supply, fuel feed rate, and grate speed automatically, enabling the heating system to maintain high combustion efficiency across various biomass fuels without manual reconfiguration.
Solution Approach 2:
The AI control device performs self-learning and self-adjustment when handling different fuel types. It autonomously analyzes combustion patterns, identifies optimal parameters for each fuel type, and adjusts operating conditions without external intervention, enabling the system to serve multiple fuel types efficiently while maintaining high productivity.
3Object-generated harmful factors
If manual control and adjustment are used, then the system structure remains simple, but emissions are high and maintenance is complex
Solution Approach 1:
The patent replaces manual control operations with an AI-based automated control system that processes sensor data and adjusts combustion parameters dynamically. The control device monitors emissions-related parameters and automatically optimizes combustion air supply and fuel feed rate to minimize pollutant emissions, substituting manual regulation with intelligent automated control.
Solution Approach 2:
The control device performs self-adjustment and self-optimization of combustion parameters to reduce emissions. It autonomously analyzes sensor data, identifies optimal operating conditions for low emissions, and adjusts control variables without manual intervention, enabling the system to maintain low pollutant emissions while simplifying operation.
4Productivity
If combustion parameters are not optimized, then the system operation is simple, but combustion efficiency is low and ash removal is difficult
Solution Approach 1:
The control device implements continuous feedback loops by monitoring combustion parameters through sensors and automatically adjusting control variables. The system measures actual combustion conditions, compares them with optimal values learned through machine learning, and makes real-time adjustments to maintain high combustion efficiency and facilitate easy ash removal.
Solution Approach 2:
The patent replaces manual optimization processes with an AI-based control system that automatically adjusts combustion parameters. The control device processes sensor data and dynamically optimizes air supply, fuel feed rate, and grate speed, substituting manual optimization with intelligent automated control to achieve high combustion efficiency and simplified ash removal.
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
The system achieves significant reductions in emissions, improves combustion efficiency, and simplifies ash removal and maintenance, ensuring high system availability and flexibility with varying fuel types.
Implementation Method 1
A biomass heating system utilizing an AI model-based control device optimized through machine learning, which learns from sensor data to optimize combustion processes
Implementation Method 2
Biomass can be considered a cheap, domestic, crisis-proof and environmentally friendly fuel. There are, for example, wood chips or pellets as combustible biomass or biogenic solid fuels.
Implementation Method 3
In the combustion chamber of a fixed-bed furnace, a furnace grate is also usually provided, on which the fuel is essentially supplied and burned continuously
Implementation Method 4
Biomass heating systems for fuels in the form of pellets and wood chips essentially have a boiler with a combustion chamber (the combustion chamber) and with an adjoining heat exchange device
Implementation Method 5
When the primary air flows through the grate, the grate is also cooled, which protects the material. In addition, insufficient air supply can lead to slag formation on the grate
Implementation Method 6
In the first phase, the fuel is at least partially pyrolytically decomposed and converted into gas by high temperatures and air that can be blown into the combustion chamber
Implementation Method 7
The combustion process can be advantageously regulated by means of a lambda probe provided at the exhaust gas outlet of the boiler
Data Source
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AI summary
Biomass heating system (1) for burning biogenic fuel, comprising: a boiler (11) with a combustion unit (2) and with a heat exchanger (3); a control unit (100) with a storage unit (105, 105a); at least one sensor (86, 111-117, 582, 592) for providing sensor data, which can detect at least one chemical and/or physical parameter of the biomass heating system (1) and which is communicatively connected to the control unit (100); at least one actuator (4, 5, 52, 6, 61, 66, 7, 72, 91, 201, 231) of the biomass heating system (1), which is communicatively connected to the control unit (100) and can be controlled by it; wherein the control device (100) includes at least one AI model (104) for controlling the biomass heating system (1) based on the provided sensor data, wherein the AI model (104) is parameterized by machine learning.