Particle Size Distribution Estimation Using Chord Length Descriptors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for predicting particle size distribution (PSD) and morphology based on chord length distribution (CLD) are inaccurate and imprecise due to variability in chord length measurements and require multiple models for different types of CLDs and morphologies, failing to handle morphological changes during in-line monitoring.
Innovation Solution
A system and method that estimates PSD and morphology using a computer system receiving multiple CLDs, employing a morphology estimation model, descriptor identifier, statistical model, and parameterized PSD model, which can handle various CLD types and morphologies, and adjusts parameters based on training datasets to generate accurate PSD and morphology estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single model is used to predict PSD from CLD, then device complexity is reduced, but measurement precision deteriorates because existing models are specific to particular CLD types and particle morphologies
Solution Approach 1:
The patent develops a universal prediction model that can handle multiple CLD types (e.g., Type A, Type B, Type C) and various particle morphologies (spherical, fibrous, plate-like, irregular) within a single framework. This multi-functional model eliminates the need for separate models for each CLD type and morphology combination, reducing device complexity while maintaining prediction accuracy through morphology identification and adaptive parameter adjustment
Solution Approach 2:
The patent introduces morphology-dependent parameters and weighting factors that are dynamically adjusted based on the identified particle morphology. By changing parameters such as the relationship between chord length and particle diameter, and the weighting of different CLD moments, the model adapts to different morphology types, maintaining high prediction accuracy across diverse particle forms without requiring multiple separate models
2Measurement precision
If multiple models are used for different CLD types and morphologies, then PSD prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple morphology-specific prediction approaches into a single unified model that incorporates morphology identification as a preliminary step. The system combines CLD data processing, morphology classification, and PSD prediction into an integrated framework where the model automatically selects appropriate parameters based on the identified morphology, effectively combining the advantages of multiple specialized models while avoiding their complexity
3Measurement precision
If existing models are used for morphological changes, then they fail to identify morphology, but adapting models to handle morphological changes improves prediction accuracy
Solution Approach 1:
The patent performs morphology identification as a preliminary step before PSD prediction. By first classifying the particle morphology based on CLD characteristics, the system prepares the appropriate parameters and weighting factors in advance, enabling accurate PSD prediction for the specific morphology type. This preliminary classification action allows the model to adapt to morphological changes without requiring complete model retraining
Data Source
AI summary
This disclosure relates to a method for estimating a particle size distribution (PSD) and a morphology for a set of particles. A computer system receives a plurality of chord length distributions (CLDs) of different types for a set of particles. The computer system then estimates a morphology for the set of particles based on the plurality of received CLDs. The computer system also identifies a plurality of descriptors of the plurality of CLDs for the set of particles based on the plurality of received CLDs. The computer system then estimates metrics for the PSD for the set of particles based on the plurality of identified CLD descriptors. Based on the estimated PSD metrics for the set of particles, the computer system generates an estimate of the PSD for the set of particles. Finally, the computer system outputs the estimated morphology and the estimated PSD for the set of particles.