Model Predictive Control for IGCC Plant Coordination
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Solution Overview
Problem
Current IGCC power plant control methods rely on simplistic and rigid procedures, leading to suboptimal operation due to limited online monitoring and reliance on secondary metrics, resulting in inefficiencies and variability in performance across different operational modes and fuel conditions.
Innovation Solution
A model predictive control system that uses a sensor suite, estimator, and dynamic model to generate predictions of plant performance and determine a control strategy, prioritizing tracking and optimization objectives through coordinate transformations, allowing for real-time adaptation of control inputs to meet specific operational modes and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If model predictive control with multiple control knobs is implemented, then control flexibility and optimization capability improve, but system complexity increases
Solution Approach 1:
The control system is segmented into distinct functional modules: a sensor suite for measurement, an estimator for state reconstruction, a dynamic model for prediction, and a controller for decision-making. This modular architecture manages complexity by dividing the overall control function into manageable, independent components that can be developed and implemented separately.
Solution Approach 2:
The system performs preliminary actions by using the dynamic model to predict future plant performance over a prediction time horizon before actual control actions are applied. The estimator proactively reconstructs current plant state from available measurements, and the controller pre-calculates optimal control strategies based on predicted outcomes, enabling proactive rather than reactive control.
2Productivity
If real-time dynamic prediction and control optimization are performed, then plant efficiency and reliability improve, but computational requirements and processing time increase
Solution Approach 1:
The system applies partial action by focusing computational resources on predicting and controlling only the most critical plant parameters and performance metrics rather than attempting to optimize all possible variables simultaneously. The dynamic model and controller concentrate on essential state variables that have the greatest impact on plant efficiency and reliability.
3Reliability
If multivariable control inputs are used to meet multiple objectives, then optimization performance improves, but control difficulty and coordination complexity increase
Solution Approach 1:
The control system implements continuous feedback through the sensor suite that monitors plant parameters and feeds this information back to the estimator and controller. The estimator reconstructs the current plant state from sensor measurements, and this reconstructed state feeds back into the dynamic model and controller, creating a closed-loop system that automatically adjusts control strategies based on actual plant performance, reducing the need for manual intervention.
Data Source
AI summary
Control system and method for controlling an integrated gasification combined cycle (IGCC) plant are provided. The system may include a controller coupled to a dynamic model of the plant to process a prediction of plant performance and determine a control strategy for the IGCC plant over a time horizon subject to plant constraints. The control strategy may include control functionality to meet a tracking objective and control functionality to meet an optimization objective. The control strategy may be configured to prioritize the tracking objective over the optimization objective based on a coordinate transformation, such as an orthogonal or quasi-orthogonal projection. A plurality of plant control knobs may be set in accordance with the control strategy to generate a sequence of coordinated multivariable control inputs to meet the tracking objective and the optimization objective subject to the prioritization resulting from the coordinate transformation.