Distillation Model Generation for Non-Ideal Blended Fuels
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Solution Overview
Problem
Current distillation processes for multicomponent fluid mixtures are costly and time-consuming, and they struggle to accurately characterize non-ideal behavior in blended fluids, particularly in petroleum-based fuels, due to the complexity of physical experiments and limitations in simulation methods.
Innovation Solution
An automatic distillation model generation system that uses a predetermined distillation profile of a base fluid to generate a thermodynamically accurate distillation profile of a mixture with additives, employing pure components and calibrating parameters like reflux ratio and heat transfer coefficients to create a calibrated distillation model for virtual experiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical distillation experiments are conducted to characterize multicomponent fluid mixtures, then measurement precision is improved, but loss of time and cost increase significantly
Solution Approach 1:
The patent creates a virtual copy of the physical distillation experiment through computer simulation. A distillation model is constructed that replicates the behavior of the physical system, allowing researchers to obtain distillation profiles through simulation rather than physical experimentation. This copying approach maintains measurement precision while eliminating time losses and costs associated with physical experiments.
Solution Approach 2:
The patent transforms the experimental approach by changing from physical parameters (temperature, pressure, composition measurements in real experiments) to computational parameters (simulation inputs, mathematical models, calculated outputs). By parameterizing the distillation process through a computational model, the system achieves accurate distillation profiles without the time and resource constraints of physical experimentation.
2Measurement precision
If physical distillation experiments are conducted to characterize multicomponent fluid mixtures, then measurement precision is improved, but cost increases significantly
Solution Approach 1:
The patent replaces expensive physical distillation experiments with a computational copy. The distillation model simulates the separation process, generating accurate distillation profiles without consuming physical resources such as chemicals, energy for heating/cooling, and laboratory equipment. This copying strategy maintains measurement precision while dramatically reducing costs.
3Productivity
If simulation methods are used to model distillation processes, then productivity is improved, but manufacturing precision deteriorates due to inability to accurately characterize non-ideal behavior
Solution Approach 1:
The patent performs preliminary characterization of the multicomponent mixture by obtaining distillation profiles of individual components and pure substances before conducting the full mixture simulation. This preliminary action allows the model to be calibrated with accurate reference data, ensuring that non-ideal behavior is properly captured. The pre-characterization step enables the simulation to achieve both high productivity and high precision in predicting mixture distillation behavior.
Solution Approach 2:
The patent implements a feedback mechanism where distillation profiles from physical experiments on pure components and base fluids are used to calibrate and validate the simulation model. This feedback loop ensures that the computational model accurately represents real-world non-ideal behavior. By continuously refining the model parameters based on experimental feedback, the system achieves both rapid simulation capability and high prediction accuracy for complex fluid mixtures.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables rapid, cost-effective, and thermodynamically robust characterization of distillation properties, reducing the need for physical experiments and improving accuracy in simulating distillation processes for blended fluids, suitable for industrial-scale process design.
Implementation Method 1
Distillation processes may be used to separate components or substances from a liquid mixture by combination of (e.g., successive) boiling and condensation. Distillation experiments may, for example, take advantage of variations and relative volatility of components of a mixture
Implementation Method 2
Distillation processes may be used to separate components or substances from a liquid mixture by combination of (e.g., successive) boiling and condensation
Data Source
AI summary
Apparatus and associated methods relate to generating, from a predetermined distillation profile (DPBASE-P) of a multicomponent base fluid, a thermodynamically accurate distillation profile (DPMIX) of a mixture of the base fluid and at least one additive. In an illustrative example, at least one calibrating parameter (P) of a distillation model (DM) is determined according to DPBASE-P. The base fluid and the mixture may, for example, be represented by a base composition profile (CPBASE) and mixture composition profile (CPMIX), respectively, of pure chemical components. The DM may, for example, be calibrated to DPBASE-P to generate a distillation profile of the base fluid as a function of CPBASE and P. The calibrated DM may, for example, be configured to generate DPMIX as a function of P and CPMIX. Various embodiments may advantageously enable rapid characterization of a mixture from a known distillation profile of the base fluid.