Multi-source Fuel Blending Combustion Control for Power Plants
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge lies in establishing a stable fuel-load mapping relationship for multi-source fuel blending combustion units, which is hindered by the unstable calorific value and varying physical and chemical properties of the fuels, leading to difficulties in optimizing the operation of thermal power units and reducing pollution and carbon emissions effectively.
Innovation Solution
An intelligent pollution and carbon reduction method based on combustion control and load distribution is introduced, which includes a data processing layer, a multi-unit load distribution and operation optimization layer, and a single-unit boiler multi-objective combustion optimization layer. This method employs mechanism analysis, optimization model construction, closed-loop simulation verification, and parameter adjustment to optimize air-coal ratios and air distribution in boilers, thereby improving energy efficiency and reducing pollutant emissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If multi-source fuel blending combustion is used to reduce fuel costs and carbon emissions, then economic efficiency and environmental performance are improved, but the unstable calorific value and varying physical-chemical properties of fuels make it difficult to establish a stable fuel-load mapping relationship
Solution Approach 1:
The patent implements dynamic adaptation mechanisms that continuously adjust combustion parameters based on real-time fuel quality variations. The system uses online monitoring of calorific value and compositional changes to dynamically modify air-fuel ratios, combustion timing, and mixing proportions, enabling the system to maintain stable operation despite varying fuel properties across different fuel sources
Solution Approach 2:
The patent systematically varies key combustion parameters including air-fuel ratio, combustion temperature, pressure, and fuel blending proportions based on detected fuel quality parameters. By establishing parameter adjustment rules that correlate fuel characteristics with optimal combustion settings, the system compensates for fuel variability and maintains consistent combustion performance and stable fuel-load mapping
2Ease of operation
If traditional operation mode determines electricity by heat in cogeneration units, then operation simplicity is maintained, but energy waste occurs under low-load conditions and insufficient energy supply occurs under high-load conditions
Solution Approach 1:
The patent implements dynamic load distribution strategies that automatically adjust the operational mode and load allocation among multiple units based on real-time demand conditions. The system transitions between heat-led and power-led operation modes dynamically, optimizing the mix of base-load and peak-load units to match varying demand patterns, thereby eliminating the energy waste of traditional fixed modes while maintaining operational simplicity through automated control
Solution Approach 2:
The patent creates a multi-functional operation system where cogeneration units can flexibly switch between different operational roles (base-load power, peak-load power, heat supply, or combined heat and power) based on system needs. This universal operational capability allows the same equipment to adapt to various load conditions and functional requirements, resolving the contradiction between simple operation and flexible energy supply
3Adaptability or versatility
If flexible operation strategy is implemented to improve unit operation flexibility, then adaptability to variable load conditions is improved, but higher requirements are placed on deep peak regulation, load ramp, and rapid start-stop capabilities
Solution Approach 1:
The patent divides the flexible operation control into modular functional components including load prediction modules, optimization calculation modules, and execution control modules. Each module handles specific aspects of flexible operation (e.g., short-term load forecasting, combinatorial optimization, real-time parameter adjustment), reducing overall system complexity by breaking down the complex control task into manageable, independently developable segments
Solution Approach 2:
The patent introduces an intelligent optimization algorithm as an intermediary layer between demand signals and physical control actions. This algorithmic mediator performs complex calculations and decision-making, translating high-level operational goals into specific control parameters for combustion systems, thereby shielding the physical equipment from the full complexity of flexible operation requirements while enabling advanced adaptability
4Productivity
If existing multi-objective combustion optimization based on data mining and intelligent optimization algorithms is used, then optimization capability is improved, but the open-loop optimization method cannot achieve multi-objective optimization under variable load conditions
Solution Approach 1:
The patent implements closed-loop optimization where real-time measurements of combustion parameters, fuel quality, and operational performance continuously feed back to the optimization algorithm. The system compares actual outcomes with optimization targets, detects deviations, and automatically adjusts control parameters in real-time, enabling continuous adaptation to variable load conditions while maintaining optimization performance through iterative refinement
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 proposed method achieves improved unit energy efficiency, reduced pollutant emissions, and optimized fuel consumption, with the potential to save up to 16% in fuel consumption and reduce carbon emissions by 16% or more at the source of a coal-fired power plant.
Implementation Method 1
an intelligent pollution and carbon reduction method based on combustion control and load distribution for a multi-source fuel blending combustion unit
Data Source
AI summary
In an intelligent pollution and carbon reduction method based on combustion control and load distribution, a data processing layer, a multi-unit load distribution and operation optimization layer and a single-unit boiler multi-objective combustion optimization layer are used. The data processing layer, the multi-unit load distribution and operation optimization layer and the single-unit boiler multi-objective combustion optimization layer are embedded in a power plant information system in the form of modules. A load distribution and operation optimization method for a multi-source fuel blending combustion unit with economy as an objective is provided to overcome the operation optimization difficulty of the key production process of a multi-source fuel blending combustion cogeneration unit, such as sludge drying-steam distribution.