Crystal Particle Caking Prediction Using CHS Bridge Growth Models
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Solution Overview
Problem
Current methods for predicting the critical agglomeration cycle of crystal particles are time-consuming, require large experimental quantities, and are not accurate, especially for different temperature and humidity conditions, lacking a quantitative mathematical relationship.
Innovation Solution
Establish a CHS crystal bridge growth model database with data on equivalent particle radius, moisture absorption capacity, and critical agglomeration cycles under various conditions, using calculation equations to predict the critical agglomeration cycle based on equivalent particle radius and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If accelerated agglomeration experimental process under high temperature and high humidity is used, then prediction of critical agglomeration cycle can be realized, but experimental period becomes long (months or even a year) and experimental quantity becomes large
Solution Approach 1:
The patent changes the experimental parameters by conducting tests at multiple temperature levels (25℃, 35℃, 45℃) and humidity levels (60%, 70%, 80%) simultaneously, rather than using a single accelerated condition. This multi-parameter approach allows for building a comprehensive prediction model that can accurately predict critical agglomeration cycles across different storage conditions without requiring extremely long experimental periods at single high-stress conditions
Solution Approach 2:
The patent develops a universal prediction model based on moisture absorption capacity that can predict critical agglomeration cycles under various temperature and humidity conditions. This single model serves multiple functions: predicting agglomeration risk at different storage conditions, determining safe storage times, and guiding transportation requirements, thereby eliminating the need for separate long-term experiments for each condition
2Measurement precision
If accelerated agglomeration experimental process is used, then prediction can be realized, but experimental quantity becomes large and cost increases
Solution Approach 1:
The patent reduces experimental quantity by changing from extensive long-term monitoring to targeted short-term measurements of moisture absorption capacity at different humidity levels. By measuring moisture absorption at 60%, 70%, and 80% humidity for 24 hours each, the system predicts long-term agglomeration behavior without requiring months of actual storage experiments
Solution Approach 2:
The patent introduces moisture absorption capacity as an intermediary parameter that correlates with critical agglomeration cycle. Instead of directly measuring agglomeration over months, the system measures this intermediary property (moisture absorption) which can be determined quickly and used to predict the ultimate agglomeration outcome, thereby reducing experimental quantity significantly
3Reliability
If conventional prediction methods are used, then critical agglomeration cycle can be determined, but it can only predict under specific humidity cycle condition and cannot achieve fast prediction for particle groups under different temperature and humidity conditions
Solution Approach 1:
The patent creates a universal prediction model that works across multiple temperature and humidity conditions. The model uses moisture absorption capacity measured at different humidity levels (60%, 70%, 80%) to predict critical agglomeration cycles for various storage scenarios. This single model structure can be applied to different particle types and environmental conditions, providing both reliability and versatility
Solution Approach 2:
The patent adds dimensional complexity by considering multiple temperature levels (25℃, 35℃, 45℃) and multiple humidity levels (60%, 70%, 80%) simultaneously in the prediction model. This multi-dimensional approach allows the system to predict agglomeration behavior in three-dimensional environmental space (temperature-humidity-time) rather than being limited to single-condition predictions
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
Enables fast and accurate prediction of the critical agglomeration cycle with reduced experimental effort and cost, suitable for industrial guidance on crystal particle storage and transportation.
Implementation Method 1
data of a moisture absorption capacity of crystal particles with different equivalent particle radiuses under multiple ambient temperatures and multiple ambient humidity conditions
Data Source
Figure 1
Figure 2(a)~2(b)
Figure 2(c)
AI summary
The present disclosure provides a technology field of agglomeration of crystal particles, and in particular relates to a method for predicting a critical agglomeration cycle of a crystal particle. The method includes: establishing a CHS crystal bridge growth model database of a crystal particle with a same type of a crystal particle to be predicted firstly, selecting existed data in the corresponding CHS crystal bridge growth model database based on an equivalent particle radius, a stored ambient temperature, and an environmental high and low humidity cycle condition of the crystal particle to be predicted, respectively, and calculating the critical agglomeration cycle according to experience calculation equations, wherein a result obtained by calculation is a predicted critical agglomeration cycle of the crystal particle to be predicted. The present disclosure has characteristics of time-saving, convenience, good universality, and high prediction accuracy, which can quickly predict the critical agglomeration cycle of multi-particle crystal particle products under different humidity storage conditions within a week, resulting in greatly reducing time costs and providing guidance for storage of industrial crystal particle products.