Method for operating a process plant
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Solution Overview
Problem
Existing process plants face challenges in efficient operation due to limited measurement capabilities, nonlinear dynamic responses, and the need for extensive commissioning and tuning of control systems, which can lead to production losses and inefficiencies.
Innovation Solution
A dynamic model based on thermo-fluidic and thermo-dynamic correlations is used to simulate process plants, allowing for improved control strategies by estimating unmeasured parameters and pre-configuring control systems, reducing model mismatch, and enabling faster, more accurate operation and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dynamic model based on thermo-fluidic and thermo-dynamic correlations is used to simulate process plants, then measurement precision and control accuracy are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent introduces a dynamic model as an intermediary system that receives process data from sensors and generates estimates of unmeasured parameters. This mediator bridges the gap between limited measurements and complete process knowledge, allowing accurate control without directly measuring all parameters.
Solution Approach 2:
The patent creates a virtual copy of the process plant through the dynamic model. This digital twin replicates the plant's behavior using thermo-fluidic and thermo-dynamic correlations, allowing operators to study, predict, and control the actual plant through its virtual representation without physical modification.
2Reliability
If control systems are extensively commissioned and tuned, then reliability and performance are improved, but loss of time and production losses increase
Solution Approach 1:
The patent applies preliminary action by pre-configuring and pre-tuning control strategies using the dynamic model before actual plant commissioning. The virtual model allows extensive testing and optimization of control parameters without affecting the physical plant, so that when deployment occurs, minimal on-site tuning is needed.
Solution Approach 2:
The dynamic model enables self-service commissioning by automatically generating control recommendations and predictions based on process data. The system serves itself by using its own virtual representation to determine optimal control settings, reducing dependence on external experts for lengthy commissioning processes.
3Productivity
If aggressive controller tuning is implemented, then productivity and response speed are improved, but stability and reliability may worsen
Solution Approach 1:
The patent implements feedback by continuously comparing actual plant performance with predictions from the dynamic model. The model receives real-time process data, predicts future states, and provides feedback on optimal control adjustments. This closed-loop approach allows aggressive tuning while maintaining stability through continuous validation against actual plant behavior.
Data Source
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AI summary
The invention relates to a method for operating a process plant (100) using a dynamic model (200) of the process plant, the dynamic model (200) 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 (100), the dynamic model (200) receiving process parameters as input values, the dynamic model (200) being adapted to represent a transition from one to another state of the process plant (100), wherein the dynamic model (200) is used in an online mode, in which the dynamic model (200) is used in parallel with the operation of the process plant (100), wherein signals from a control system (300) of the process plant, the signals representing values of at least one first process parameter (310), are received and fed into the dynamic model (200), and wherein values of at least one second process parameter (410) are determined based on the dynamic model (200) and used for operating the process plant (100).