Fossil Fuel Power Unit Control System for Flame Stability
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Solution Overview
Problem
Conventional control systems for fossil fuel-fired power generating units face challenges in maintaining flame stability due to variations in fuel heat content and inaccurate oxygen sensor readings, leading to potential flame extinction and increased NOx emissions, which affects efficiency and air pollution.
Innovation Solution
A control system incorporating a combustion optimization system and an oxygen optimization system, utilizing neural network-based models and optimizers to determine optimal setpoints for manipulated variables, minimizing cost functions while adhering to constraints, thereby automatically controlling oxygen levels and air flow to prevent flame instability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional DCS-based control schemes are used to manage fuel and air ratios, then basic combustion control is maintained, but flame stability deteriorates due to variations in fuel heat content and inaccurate oxygen sensor readings
Solution Approach 1:
The system implements a neural network-based feedback mechanism that continuously monitors oxygen levels and fuel characteristics, automatically adjusting air-fuel ratios to compensate for sensor inaccuracies and maintain optimal combustion conditions
Solution Approach 2:
The control system dynamically changes operating parameters including oxygen levels, air-fuel ratios, and combustion temperatures based on real-time sensor data and neural network predictions, adapting to variations in fuel heat content and sensor drift
2Object-generated harmful factors
If conventional control systems operate without advanced optimization, then system complexity is reduced, but NOx emissions increase due to suboptimal air-fuel mixing
Solution Approach 1:
A neural network model serves as an intermediary between sensors and actuators, processing oxygen sensor readings and fuel characteristics to predict optimal combustion parameters and generate control signals that minimize NOx emissions
Solution Approach 2:
The system performs preliminary optimization calculations using the neural network model to determine optimal air-fuel ratios before combustion occurs, pre-adjusting parameters to prevent excessive NOx formation rather than reacting to it afterward
3Productivity
If advanced neural network-based optimization systems are implemented, then combustion efficiency is improved and emissions are reduced, but system complexity and computational requirements increase
Solution Approach 1:
The neural network-based control system is self-adjusting and self-optimizing, automatically learning from operational data and adapting to changing conditions without requiring manual intervention or complex external control mechanisms
Solution Approach 2:
The control system performs multiple functions including real-time optimization of combustion parameters, prediction of fuel characteristics, compensation for sensor inaccuracies, and emission minimization, all within a single integrated neural network framework
Data Source
AI summary
A control system for controlling operation of a fossil fuel fired power generating unit. The control system including both a combustion optimization system and an oxygen optimization system. Both the combustion optimization system and oxygen optimization system each including a model for predicting values of controlled variables and an optimizer for determining optimal setpoint values.