Crystallization Modeling via Discrete Crystal Groups
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Solution Overview
Problem
Current crystallization process modeling approaches, such as population balance equations, are complex and require significant computational effort and user expertise, lacking a simpler and more effective method for predicting crystal size distribution and controlling crystallization processes.
Innovation Solution
A method and system that represent crystals in solution as subsets with attributes like number and characteristic length, tracking their growth and distribution to simulate crystal size progression, including nucleation and breakage, providing a simpler and more intuitive approach for modeling crystallization processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If population balance equations are used to model crystallization processes, then the model provides a comprehensive mathematical framework for tracking particle formation, growth, and breakage, but the model becomes computationally complex requiring significant computational effort and specialized user training
Solution Approach 1:
The patent segments the continuous crystal population into discrete size classes or groups, transforming the continuous population balance equation into a set of discrete balance equations. This segmentation approach maintains the comprehensive tracking of particle formation, growth, and breakage while reducing computational complexity by using discrete rather than continuous mathematical representations.
Solution Approach 2:
The patent employs simplified assumptions and approximations that allow for quicker computational solutions without completely sacrificing model reliability. These simplified models can be used for preliminary design and screening, reserving the more complex population balance approaches only when higher accuracy is absolutely necessary.
2Reliability
If population balance equations are used to model crystallization processes, then the model comprehensively tracks particle behavior, but the model requires substantial implementation effort and extensive user input
Solution Approach 1:
The patent implements automated parameter estimation and model calibration capabilities that reduce the need for extensive manual user input. The system can automatically adjust model parameters based on limited experimental data, performing self-calibration to maintain comprehensive tracking of particle behavior while minimizing the implementation burden on users.
Solution Approach 2:
The patent develops a unified modeling framework that can handle multiple crystallization scenarios (different crystal types, growth mechanisms, and operational modes) using a single set of equations and procedures. This universality reduces implementation complexity by eliminating the need for separate specialized models for different crystallization situations.
3Manufacturing precision
If traditional modeling approaches are used, then the model provides detailed mathematical descriptions, but the model cannot provide quick predictions for conceptual design
Solution Approach 1:
The patent applies a hierarchical modeling strategy where simplified models provide quick predictions for conceptual design and preliminary evaluation, while more detailed models are reserved for final optimization and validation. This partial application of complex modeling only where necessary achieves acceptable prediction accuracy for early-stage design while maintaining high productivity through faster simplified models for routine evaluations.
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 allows for quick and qualitative trend predictions in crystallization processes, improving the design and operation of crystallization units by simplifying the modeling of crystal size distribution and overcoming the limitations of existing methods.
Implementation Method 1
tracking generation of new crystal groups generated by seeding, nucleation, or breakage
Implementation Method 2
Crystallization is one of the most important separation and purification techniques employed industrially to produce a wide variety of materials
Implementation Method 3
tracking increase of the respective characteristic length of each crystal group
Data Source
AI summary
A computer system and method of modeling a crystallization process includes representing a plurality of crystals in a solution by different subsets of the plurality, tracking increase of the respective characteristic length of each crystal group, and determining a crystal size distribution to output a model to a user. Each subset forms a respective crystal group characterized by group attributes of (i) a number of crystals and (ii) a characteristic length. Additionally, the system and method track generation of new crystal groups generated by seeding, nucleation and/or breakage.