Sootblower Control via Fuzzy Logic and Rules
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Solution Overview
Problem
Current sootblowing systems in fossil fueled power plants face inefficiencies due to reliance on sophisticated models and expert systems, which can lead to inaccuracies and complexity, and do not effectively adapt to changing conditions, resulting in performance penalties and equipment issues.
Innovation Solution
A graphical programming environment combined with a set of rules is used to activate sootblowers, allowing for real-time evaluation based on current status of key control variables, eliminating the need for sequences or queues, and incorporating time constraints to prevent over- or under-blowing, thus adapting to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predetermined time-based sequences are used to activate sootblowers, then the control system is simple and easy to operate, but the system cannot adapt to changing soot buildup conditions leading to sub-optimal cleaning performance
Solution Approach 1:
The patent implements dynamic sootblower activation by continuously monitoring boiler operating conditions (steam temperatures, pressures, fuel types, load levels) and adjusting the timing and selection of sootblower activation in real-time. This replaces static predetermined sequences with a dynamic control system that adapts to changing conditions while maintaining operational simplicity through automated decision-making algorithms.
2Productivity
If sophisticated models and expert systems are used to optimize sootblowing, then cleaning performance improves, but system complexity increases and accuracy may be compromised
Solution Approach 1:
The patent employs feedback mechanisms by continuously monitoring boiler operating conditions and using this information to adjust sootblower activation timing. The system incorporates feedback from temperature sensors, pressure sensors, and operational parameters to dynamically optimize cleaning cycles, achieving high cleaning performance without requiring overly complex models through continuous real-time adjustment based on actual system state.
Solution Approach 2:
The control system performs self-optimization by automatically analyzing operating conditions and determining optimal sootblower activation without requiring external expert intervention. The system serves itself by using its own operational data to make real-time decisions, reducing the need for complex external expert systems while maintaining high cleaning performance.
3Reliability
If frequent sootblowing is performed to prevent excessive soot buildup, then heat transfer efficiency is maintained, but heat rate penalty increases due to added material in combustion process
Solution Approach 1:
The patent applies partial action by activating sootblowers only when and where needed based on real-time monitoring of soot buildup indicators (temperature differentials, pressure drops, operational conditions). Instead of uniform frequent blowing across all sections, the system applies cleaning action selectively to sections that require it, maintaining heat transfer efficiency while minimizing the energy penalty associated with excessive sootblowing.
4Ease of operation
If sootblowers are activated based on fixed schedules, then operational procedures are simple, but under-blowing occurs leading to excessive temperatures and heat loss
Solution Approach 1:
The patent replaces fixed mechanical timing schedules with an intelligent control system that uses sensor data and algorithms to determine optimal sootblower activation. This substitution of mechanical timing with intelligent control maintains operational simplicity through automated decision-making while preventing under-blowing conditions that lead to excessive temperatures and heat loss.
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
This approach allows for flexible and efficient sootblowing operations, maintaining temperatures and pressures closer to desired values, reducing the risk of tube leaks and heat balance changes, and improving overall plant efficiency by avoiding alarm conditions and optimizing blower activation.
Implementation Method 1
Sootblowing involves the removal of slag and soot with high-velocity jets of air, steam, or water
Implementation Method 2
The air, steam and water all impart a heat rate penalty (lower efficiency) to the power plant as they add material to the combustion process
Implementation Method 3
this is from the combustion of fossil fuel (for example, coal or oil) in a power plant for generating electricity or process steam
Implementation Method 4
The air, steam and water all impart a heat rate penalty (lower efficiency) to the power plant as they add material to the combustion process that will be heated and rejected as waste heat
Implementation Method 5
by sudden temperature excursion caused by a newly cleaned surface taking in heat more quickly than the reaction time of the control system
Data Source
AI summary
A system and method to control of sootblowers in a fossil fueled power plant, in particular to plant applications systems using a graphical programming environment in combination with a set of rules to activate sootblowers. The system can be constrained by time limits and/or rule based time limits. Actual blower activation is typically based on the current status of key control variables in the process which alter the actual activation time within a constraints system. The system does not typically require knowledge or models of specific cleanliness relationships. The result is a system without sequences or queues that readily adapts to changing system conditions.