Thermodynamic Phase Equilibrium Analysis Using Reduced Variables
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Solution Overview
Problem
Existing thermodynamic modeling systems face challenges in efficiently calculating phase equilibrium and predicting phase splits in complex mixtures, leading to high computational demands and potential failure in real-time plant control due to the need for constant recalculations and handling of complex thermodynamic equations.
Innovation Solution
A method and system that utilize a tangent hyperplane distance function to evaluate phase stability and pseudo-properties, reducing the number of variables in calculations and employing a thermodynamic process simulation application to estimate the probability of phase splits, thereby improving computational efficiency and enabling real-time control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative calculations of complex thermodynamic equations are performed to accurately describe static views of dynamic systems, then measurement precision of phase equilibrium is improved, but processing time increases significantly
Solution Approach 1:
The patent performs preliminary calculations to establish thermodynamic models and equilibrium constants before real-time control is needed. These pre-computed models are then used for rapid phase stability assessments during actual plant operation, eliminating the need for time-consuming iterative calculations in real-time control scenarios.
Solution Approach 2:
The patent creates simplified mathematical models that replicate the essential behavior of complex thermodynamic systems without requiring full iterative thermodynamic calculations. These reduced-order models capture phase equilibrium relationships through algebraic equations derived from pre-computed thermodynamic data, enabling fast real-time predictions.
2Reliability
If constant recalculations are performed to keep the model updated for rapidly changing systems, then reliability of real-time prediction is improved, but computational load increases
Solution Approach 1:
The patent implements a dynamic model that adapts to changing system conditions by updating equilibrium constants and thermodynamic parameters based on current temperature, pressure, and composition measurements. This allows the model to maintain accuracy for rapidly changing systems without requiring complete recalculations from scratch, reducing computational load while preserving reliability.
Solution Approach 2:
The patent changes the mathematical parameters and equations used in calculations based on operating conditions. By selecting appropriate algebraic equations from pre-derived sets corresponding to different temperature, pressure, and composition ranges, the system maintains prediction reliability across varying conditions while avoiding the computational burden of universal iterative thermodynamic calculations.
3Measurement precision
If complex thermodynamic equations are solved to determine phase stability, then measurement precision of phase split detection is improved, but device complexity increases
Solution Approach 1:
The patent extracts key thermodynamic relationships and equilibrium constants from complex thermodynamic equations, separating the essential phase stability determination logic from the full thermodynamic calculation framework. This extraction results in simplified algebraic equations that maintain phase split detection accuracy while dramatically reducing computational model complexity and making the system more implementable in real-time control environments.
Data Source
AI summary
A method of modeling phase characteristics of thermodynamic systems utilizing pseudo-properties strategy and a reduced number of variables is disclosed herein. The method describes a means of determining the probability of phase splitting of mixtures of materials at a given temperature, pressure, and composition by characterizing the functions that describe the system via pseudo-properties, and also by describing the system in n−1 or fewer variables, where n represents the number of components in the system of interest. In an embodiment, a multi-component system is characterized in one variable, thereby providing simplified thermodynamic models in a time-efficient manner. In addition, the information generated by this reduced-variable calculation can further be used as a starting point for calculations of equations of state.