Modified UNIFAC Model for Biodiesel Cloud Point Prediction

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Solution Overview

Problem

Current methods struggle to accurately predict the cloud point of biodiesel mixtures containing fatty acid methyl esters from diverse sources, as they fail to consider molecular interactions and are challenged by the addition of new components or additives, leading to inaccuracies in predicting the onset of liquid to solid phase transition.

Innovation Solution

A method and system that identify the chemical and molecular structure of each component in a biodiesel mixture, calculate activity coefficients and chemical potentials in both liquid and solid phases, and determine the cloud point by using a modified UNIFAC model to account for molecular interactions and non-ideal behavior.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional statistical regression models are used to predict cloud point, then the prediction process is simple, but the accuracy is low due to inability to capture molecular interactions

Engineering Contradiction:

Improvecloud point prediction accuracyVSAvoidmodel complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces activity coefficients as an intermediary parameter to bridge the gap between composition data and cloud point prediction. By calculating activity coefficients using the UNIFAC model, the system captures molecular interaction effects without requiring complex experimental data, thus improving accuracy while maintaining computational feasibility

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces simple statistical regression with a thermodynamic modeling approach based on chemical potential equality. This substitution allows the system to predict cloud points by calculating chemical potentials and activity coefficients, capturing molecular-level interactions that statistical models miss

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If UNIQUAC model is used to predict cloud point, then prediction accuracy improves, but the model requires various parameters when new components are added

Engineering Contradiction:

Improvecloud point prediction accuracyVSAvoidmodel adaptability to new components

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent adopts the UNIFAC model which provides universal group contribution parameters that can be applied to any FAME component regardless of its specific chemical structure. By using functional group contributions rather than component-specific parameters, the model maintains high accuracy while being easily adaptable to new components and additives

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent changes the approach from requiring component-specific interaction parameters (as in UNIQUAC) to using universal group contribution parameters (as in UNIFAC). This parameter change allows the model to handle diverse FAME components from different sources without needing to re-calibrate or provide new parameters

Inventive Principle:

Principle #35Parameter changes

3Ease of manufacture

If simple linear regression is used, then the model is easy to implement, but it fails to account for non-ideal behavior and molecular interactions

Engineering Contradiction:

Improvemodel implementation easeVSAvoidprediction reliability

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The patent introduces activity coefficients as an intermediary that captures non-ideal behavior. By calculating activity coefficients through the UNIFAC model, the system maintains relative simplicity while significantly improving reliability by accounting for molecular interactions and non-ideal mixture behavior

Inventive Principle:

Principle #24Intermediary (Mediator)

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 provides a more accurate prediction of cloud points in biodiesel mixtures, improving the reliability of cloud point predictions by considering molecular interactions and composition, thereby enhancing the handling of diverse fatty acid methyl ester sources and additives.

Implementation Method 1

cloud point is a phenomenon of solid-liquid equilibrium. The cloud point of FAME depends on the composition because the main FAME components have different melting points

Methodology Applied

Scientific EffectPhase transition: Phase Change

Implementation Method 2

calculating activity coefficients for each component in a liquid phase and a solid phase according to a modified UNIFAC model

Methodology Applied

Scientific EffectActivity coefficient:

Implementation Method 3

calculating chemical potential for each component in the liquid phase and in the solid phase at a predetermined temperature and a predetermined pressure

Methodology Applied

Scientific EffectChemical potential:

Data Source

PatentUS9026421B2Method of modeling cloud point of a mixture of fatty acid methyl esters using a modified UNIFAC model and a system therefor

Publication Date: 2015.05.05 PURDUE RES FOUND

AI summary

A method for predicting onset of liquid phase to solid phase transition of a mixture including a plurality of fatty acid methyl esters components. The method includes identifying chemical and molecular structure of each component of the mixture, calculating activity coefficients for each component in a liquid phase and a solid phase, calculating chemical potential for each component in the liquid phase and in the solid phase at a predetermined temperature and a predetermined pressure, and calculating the cloud point of the mixture. A system for carrying out the method is also disclosed.