Integrated CFB-FDA Control Optimizing Limestone Usage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current fluidized bed combustion (FBC) power plants face challenges in optimizing the integrated operation of circulating fluidized bed boilers and flash dryer absorber systems, particularly during transient operations and load changes, due to complex variable relationships and the lack of relative humidity control in flash dryer absorber systems, leading to suboptimal economic performance.
Innovation Solution
An integrated modeling and optimization control system is developed, incorporating a controller and optimizer connected to both the fluidized bed combustion and air pollution control systems, utilizing design of experiments, neural network modeling, and dynamic test design to optimize setpoints and inputs based on economic parameters and system outputs, including relative humidity measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an integrated modeling and optimization control system is implemented, then economic efficiency and process optimization are improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The control system is divided into separate functional modules: a neural network-based optimization module that processes economic parameters and system outputs to generate optimized setpoints, and a control module that implements these setpoints. This segmentation allows each module to specialize in specific tasks, improving overall economic efficiency while managing system complexity through modular design.
Solution Approach 2:
An integrated modeling and optimization control system acts as an intermediary between the fluidized bed combustion system and the flash dryer absorber system. This intermediary processes information from both systems, optimizes their integrated operation, and coordinates their interactions, thereby improving overall economic efficiency without requiring direct complex integration between the combustion and pollution control systems.
2Manufacturing precision
If relative humidity control is added to flash dryer absorber systems, then sulfur capture control is improved, but measurement and control difficulty increase
Solution Approach 1:
The system incorporates relative humidity sensors in the flash dryer absorber that provide continuous feedback to the optimization control module. This feedback loop allows the system to monitor humidity levels and adjust operating parameters accordingly, improving sulfur capture control. The neural network processes this humidity data along with other system parameters to optimize the desulfurization process.
Solution Approach 2:
The integrated optimization control system serves multiple functions simultaneously: it optimizes economic parameters, controls sulfur capture, monitors relative humidity, and coordinates operations of both the combustion and pollution control systems. This multi-functionality allows relative humidity control to be integrated into the existing control infrastructure without requiring a separate dedicated control system.
3Measurement precision
If neural network modeling and dynamic test design are used, then optimization accuracy is improved, but computational requirements and system complexity increase
Solution Approach 1:
The neural network is trained offline using dynamic test design data collected from the system operations. This preliminary training phase allows the neural network to learn optimal control strategies and relationships between variables before being deployed for real-time optimization. By performing the computationally intensive training beforehand, the system achieves high optimization accuracy during operation without requiring excessive real-time computational resources.
Solution Approach 2:
The neural network creates a virtual model or copy of the complex nonlinear relationships in the integrated combustion and pollution control system. Instead of directly computing complex optimization problems in real-time, the system uses the trained neural network model to approximate these relationships, providing accurate optimization results with reduced computational burden during actual operation.
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 system enables optimized operation of the CFB-FDA system, reducing limestone and lime usage, enhancing economic efficiency, and effectively controlling sulfur capture, even during transient conditions, by integrating steady-state and dynamic models to manage multiple variables and improve overall process efficiency.
Implementation Method 1
additional sulfur is captured by the FDA in a backend process utilizing residual limestone in flying ash exiting the CFB boiler
Implementation Method 2
a suitable sorbent, such as limestone containing CaCO3, for example, is used to absorb SO2 from flue gas during the combustion process
Implementation Method 3
fluidized beds suspend solid fuels on upward-blowing jets of air during the combustion process, causing a tumbling action which results in turbulent mixing of gas and solids
Implementation Method 4
sulfur is oxidized to form primarily gaseous SO2
Data Source
AI summary
A system for optimizing and controlling a circulating fluidized bed combustion (FBC) system (7) and an air pollution control (APC) system (9) includes a controller (205, 305, 406) and an optimizer (210, 310). The controller (205, 305, 406) is connected to the FBC system (7) and/or the APC system (9). The optimizer (210, 310) is connected to the controller (205, 305, 406). The optimizer (210, 310) provides an optimized setpoint (220, 320, 420) to the controller (205, 305, 406) based on an economic parameter (235, 335, 435) and system outputs (230, 330) from the FBC system (7) and the APC system (9). The controller (205, 305, 406) provides an optimized input (215, 315) to the FBC system (7) and/or the APC system (9) based on the optimized setpoint (220, 320, 420) from the optimizer (210, 310) to optimize operation of the FBC system (7) and/or the APC system (9).