Dynamic Model-Based Control System Configuration for Process Plants
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Solution Overview
Problem
Process plants face challenges in efficient operation due to limited measured process states, slow load changes, and uncertainties in design parameters, which affect control strategies and equipment performance monitoring.
Innovation Solution
A dynamic model based on thermo fluidic and phenomenological correlations is used to represent the process plant, allowing for state estimation, prediction, and adaptation, and enabling improved control strategies and equipment performance monitoring by receiving process parameters and using them to determine updated controller set points and simulate profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conservative controller tuning is used to ensure stability, then reliability is improved, but productivity decreases due to slow load changes
Solution Approach 1:
The dynamic model is used during the design phase to simulate and optimize control strategies before actual plant operation. This preliminary action allows testing aggressive control tuning that would be too risky to implement directly on the running plant, thereby enabling faster load changes without compromising stability during actual operation
Solution Approach 2:
A virtual copy of the plant is created through the dynamic model that replicates plant behavior. This copy is used for controller configuration and testing, allowing conservative tuning to be optimized in the virtual environment while enabling more aggressive, productive control strategies to be safely deployed in the actual plant
2Measurement precision
If detailed measurement of all process states is implemented, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The dynamic model acts as an intermediary that fills gaps in process state information. Instead of installing sensors throughout the plant, the model uses available measurements combined with process knowledge to estimate and provide complete process state information, thereby achieving detailed knowledge without proportional increase in measurement hardware
Solution Approach 2:
Physical measurement systems (sensors, instruments) are replaced or supplemented by a virtual measurement system based on the dynamic model. The model computationally derives process states that would otherwise require physical sensors, reducing hardware complexity while maintaining or improving measurement precision
3Reliability
If extensive testing and commissioning is performed to optimize control strategies, then reliability is improved, but loss of time increases
Solution Approach 1:
Control strategy optimization is performed in advance during the design phase using the dynamic model. Extensive simulation and testing are conducted on the virtual plant before actual commissioning, so that when the plant is commissioned, the control strategies are already optimized and require minimal field testing, dramatically reducing commissioning time
Solution Approach 2:
Potential control issues and optimization opportunities are identified and addressed beforehand through dynamic model simulation. This prior cushioning prevents problems during commissioning and allows the control system to be deployed with confidence, reducing the time needed for field testing and adjustment
Data Source
AI summary
A method for configuring a control system for a process plant using a dynamic model of the process plant, the dynamic model being based on at least one of thermo fluidic correlations, thermo dynamic correlations, phenomenological correlations, and equations, and being based on geometry and/or topology of components of the process plant, the dynamic model receiving process parameters as input values, the dynamic model being adapted to represent a transition from one to another state of the process plant and the dynamic model covering the entire operating range of the process plant wherein the dynamic model is used in an offline mode, in which the dynamic model is used in stand-alone fashion, wherein, based on input and output values of the dynamic model, a behaviour of the process plant is predicted, and wherein, based on the predicted behaviour of the process plant, the control system is configured.