NRTL-SAC Model Segmentation for Chemical Mixture Solubility
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Solution Overview
Problem
Current methods for modeling physical properties of chemical mixtures, particularly in pharmaceutical applications, are inadequate due to limitations in existing solubility estimation techniques such as the Hansen and UNIFAC models, which struggle with large, complex molecules and lack predictive capability, resulting in inaccurate solvent selection and process design.
Innovation Solution
The Non-Random Two-Liquid Segment Activity Coefficient (NRTL-SAC) model assigns conceptual segments to molecular species, determining equivalent numbers for each segment to compute physical properties, allowing for accurate modeling of mixtures with little experimental data, including those with significant hydrophobic, polar, or hydrogen-bonding interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Hansen model or UNIFAC model is used to estimate solubility of pharmaceutical components, then the modeling can be performed with available data, but the accuracy is insufficient (±200% for Hansen, ±500% for UNIFAC) and predictive capability is limited
Solution Approach 1:
The molecular structure of each chemical species is divided into conceptual segments (e.g., hydrophobic, polar, hydrogen-bonding segments). Each segment is assigned an equivalent number that represents its contribution to the overall molecular behavior. This segmentation allows the model to capture molecular characteristics more effectively, improving both accuracy and predictive capability for pharmaceutical components with molecular weights of 200-600 daltons.
Solution Approach 2:
The invention introduces a new parameter set consisting of conceptual segment equivalents that differ from traditional solubility parameters. These parameters are determined through regression of available solubility data and provide a more accurate basis for calculating activity coefficients and solubilities across different solvents, achieving ±45% accuracy compared to the poor performance of existing models.
2Quantity of substance
If solubility experiments are conducted as part of trial and error process, then some phase equilibrium data can be obtained, but the process is time-consuming and does not provide comprehensive guidance for solvent selection
Solution Approach 1:
The model uses limited available solubility data to determine conceptual segment parameters in advance through regression analysis. Once these parameters are established, the model can predict solubility and phase equilibrium behavior for any solvent-solute combination without requiring additional experiments, enabling rapid solvent screening and selection.
Solution Approach 2:
The model enables researchers to self-determine the most suitable solvents by computing solubility predictions for hundreds of candidate solvents using the established conceptual segment parameters. This eliminates the need for extensive trial-and-error experimentation and allows the research team to identify optimal solvents through computational analysis alone.
3Ease of operation
If existing solubility parameter models are used, then no binary parameters are required and the models are simple to apply, but they follow only empirical guidance and cannot accurately model polar or hydrogen-bonding interactions
Solution Approach 1:
The invention segments molecules into conceptual units (hydrophobic, polar, hydrogen-bonding segments) with assigned equivalent numbers. This segmentation enables the model to explicitly account for different types of molecular interactions, going beyond the empirical 'like dissolves like' principle while maintaining computational simplicity through the use of predetermined segment parameters.
Data Source
AI summary
Included are methods for modeling at least one physical property of a mixture of at least two chemical species. One or more chemical species of the mixture are approximated or represented by at least one conceptual segment. The conceptual segments are then used to compute at least one physical property of the mixture. An analysis of the computed physical properties forms a model of at least one physical property of the mixture. Also included are computer program products and computer systems for implementing the modeling methods.