Process Simulation Port Equivalency for Faster Model Solving
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Solution Overview
Problem
Complex industrial processes generate overwhelming volumes of data from various sensors, making it challenging to ensure accurate and responsive simulation models, which are essential for optimizing refinery and petrochemical plant operations.
Innovation Solution
A system that creates simulation models with connection variables to represent information transfer between ports, reducing the underlying solution matrix size and improving simulation performance by treating simple equivalency equations as references rather than equations to be solved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional simulation models are used to accurately represent complex industrial processes, then model accuracy is improved, but simulation computation time increases significantly
Solution Approach 1:
The simulation model is divided into multiple sub-models, each representing a specific portion of the industrial process. Each sub-model contains its own set of variables and equations, allowing the overall system to be solved in a modular fashion. This segmentation reduces the computational burden on any single solving operation while maintaining overall model accuracy.
Solution Approach 2:
The system dynamically determines which variables are independent and which are dependent based on the process flow configuration. Independent variables are solved for first, then used to calculate dependent variables. This dynamic approach allows the simulation to adapt its solving strategy to the specific process being modeled, improving computational efficiency without sacrificing accuracy.
2Measurement precision
If detailed process modeling is performed to capture all process variables, then simulation accuracy is improved, but system complexity increases
Solution Approach 1:
The complex process model is broken down into smaller sub-models that can be independently configured and validated. Each sub-model handles a specific function or process unit, making the overall system more manageable and easier to understand while maintaining comprehensive process representation.
Solution Approach 2:
Different levels of modeling detail are applied to different parts of the process based on their importance and complexity requirements. Critical process units receive more detailed modeling attention, while less critical units use simplified representations. This allows accurate simulation where needed without unnecessarily complicating the entire model.
3Reliability
If all process variables are treated as independent equations to be solved, then model completeness is improved, but solution matrix size increases
Solution Approach 1:
The system dynamically identifies and classifies variables as either independent or dependent based on process flow analysis. Independent variables are those that can be determined from process specifications and measurements, while dependent variables are calculated from the independent ones. This classification reduces the number of equations that need to be solved simultaneously, shrinking the solution matrix size while maintaining model completeness.
Solution Approach 2:
Dependent variables are extracted from the system of equations to be solved and instead calculated directly from the independent variables using algebraic relationships. This removal of redundant equations from the solution matrix reduces computational complexity while preserving the complete process model.
Data Source
AI summary
A system for developing a simulation of a process. In one aspect, a system creates a first model within the simulation. The first model represents a part of the process and comprises a first port to which other models may be connected. The system also creates a second model within the simulation. The second model represents another part of the process and comprises a second port to which other models may be connected. The system then connects the first port and the second port together. Upon connection, the system allocates a memory location as a connection variable that represents a type of information transfer between the first and second ports. A first port variable, which represents a value transferrable through the first port, is set to reference the value at the allocated memory location. Similarly, a second port variable, which represents a value transferrable through the second port, is also set to reference the value at the allocated memory location.