Brine Concentration Prediction Using Corrected Activity Coefficients
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Solution Overview
Problem
Existing methods for predicting precipitates and component concentrations during brine concentration processes suffer from inaccuracies due to errors in activity coefficient calculations, leading to significant deviations in predicting the type and amount of precipitates, which results in lithium loss and process inefficiencies.
Innovation Solution
A brine concentration process prediction system that includes data collection, preprocessing, processing, and prediction units to calculate solubility and concentration of precipitates using non-linear regression and machine learning, correcting activity coefficients based on ionic strength and concentration, and accounting for CO2 absorption to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to predict precipitates and component concentrations, then the prediction process is simple, but the accuracy is low due to errors in activity coefficient calculations
Solution Approach 1:
The patent changes the parameters used in calculations by introducing corrected activity coefficients that account for ionic strength and concentration effects. Instead of using traditional fixed activity coefficients, the system dynamically calculates corrected values based on measured brine composition, thereby improving prediction accuracy without requiring overly complex computational models
Solution Approach 2:
The system incorporates feedback by using measured values of brine composition (Na, K, Mg, Ca, Li concentrations) to correct activity coefficients in real-time. This feedback loop allows the prediction model to continuously improve its accuracy by adjusting activity coefficients based on actual brine conditions, resolving the contradiction between simplicity and accuracy
2Manufacturing precision
If activity coefficient calculations are performed with traditional methods, then the calculation process is straightforward, but significant deviations occur in predicting precipitate types and amounts
Solution Approach 1:
The patent applies preliminary anti-action by correcting activity coefficients before performing precipitate calculations. By pre-correcting activity coefficients based on ionic strength and concentration, the system prevents significant deviations in precipitate predictions from occurring in the first place, thereby avoiding lithium loss that would result from inaccurate predictions
Solution Approach 2:
The system provides beforehand cushioning by incorporating correction factors that compensate for calculation errors before they can affect the final predictions. The corrected activity coefficients act as a cushion against the inherent inaccuracies of traditional calculation methods, ensuring reliable precipitate type and amount predictions that prevent lithium loss
3Productivity
If precise prediction of precipitates is achieved through improved activity coefficient corrections, then lithium production efficiency increases, but the calculation complexity increases
Solution Approach 1:
The patent improves lithium production efficiency by changing the calculation parameters to include corrected activity coefficients that reflect actual brine conditions. This parameter change enables more accurate precipitate predictions, allowing better control of the concentration process and reduced lithium loss, while the complexity increase is manageable through systematic correction formulas
4Reliability
If traditional prediction methods are used, then the process is easier to implement, but significant lithium is lost to precipitates and water content
Solution Approach 1:
The system enhances yield calculation reliability by implementing feedback mechanisms that continuously monitor and adjust activity coefficients based on actual brine composition measurements. This feedback ensures that predictions accurately reflect real-time conditions, preventing lithium loss to precipitates and improving the reliability of yield calculations throughout the concentration process
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
The system achieves precise prediction of precipitate types and concentrations with an error range of 5% or less, enhancing lithium production efficiency and process control.
Implementation Method 1
a data processing unit calculating a solubility of each precipitate component using the data converted by the data preprocessing unit
Implementation Method 2
calculate a solubility of each precipitate component through multiple non-linear regression analysis or machine learning
Implementation Method 3
correcting activity coefficients based on ionic strength and concentration
Implementation Method 4
accounting for CO2 absorption to improve accuracy
Data Source
Figure 1
Figure 2(a)~2(b)
Figure 3
AI summary
Exemplary embodiments may provide a brine concentration process prediction system, the brine concentration process prediction system including: a data collection unit collecting data of an initial brine and a high-concentration brine; a data preprocessing unit converting the data collected by the data collection unit to be applied to a data processing unit provided at a later stage; a data processing unit calculating a solubility of each precipitate component using the data converted by the data preprocessing unit; and a data prediction unit predicting a final amount of precipitates and a concentration of each ion component in a final concentrated brine using the solubility of each precipitate component calculated by the data processing unit.