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Prediction method of nanoparticle migration and influence factor analysis method and system thereof

A nanoparticle and prediction method technology, which is applied in the field of nanoparticle parameter prediction and analysis, and can solve problems such as inaccurate nanoparticle prediction models and insufficient interpretation of migration parameters

Pending Publication Date: 2021-01-08
HUAZHONG UNIV OF SCI & TECH
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Problems solved by technology

[0005] In view of the defects of the prior art, the purpose of the present invention is to provide a method for predicting the migration of nanoparticles, its influencing factor analysis method and system, aiming at solving the inaccuracy of the existing nanoparticle prediction model and insufficient explanation of the migration parameters The problem

Method used

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  • Prediction method of nanoparticle migration and influence factor analysis method and system thereof
  • Prediction method of nanoparticle migration and influence factor analysis method and system thereof
  • Prediction method of nanoparticle migration and influence factor analysis method and system thereof

Examples

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Embodiment 1

[0108] Example 1: Validation verification of the prediction method.

[0109] In this example, the validity of the prediction method proposed in the present invention is verified by using the experimental data of nanoparticle migration columns in porous media disclosed in published literature. 19 training features and 2 target features were extracted from the database to build a prediction model. The target features are retention rate (used to establish regression model) and retention curve category (used to establish classification model), which are used to represent the total amount and distribution of nanoparticles retained in the porous medium during the migration process, respectively. The database contains a total of 411 effective samples, among which the target feature of 403 samples is the retention rate, which is used for regression prediction; the target feature of 325 samples is the retention curve category, which is used for regression prediction. Among all 19 trai...

Embodiment 2

[0137] Example 2: Effectiveness of Feature Analysis

[0138] Using the database in Example 1 and the optimal prediction model after training, the characteristics affecting the retention rate and retention curve type were analyzed by the SHAP method. In each analysis, the 10 features that have the greatest impact on the model output are selected as the key features, and combined with the existing theory, the validity of the analysis method used is specifically verified, as explained below:

[0139] (1) Analysis on the characteristics of the retention rate

[0140] The interpretability analysis results of retention rate based on SHAP are as follows: Figure 8 shown. The associated potential information (particle IEP, particle potential, and collector potential), pore flow velocity, solution concentration, influent volume, and particle size have a greater impact on model predictions than other features.

[0141] From the Shapley value results, it can be seen that higher partic...

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Abstract

The invention provides a prediction method of nanoparticle migration and an influence factor analysis method and system thereof, and the method comprises the steps: extracting parameters and result data from a nanoparticle migration experiment in a porous medium, and obtaining training features and target features; preprocessing the data by using a one-hot coding method and a random forest method,and filling missing values while coding category type features; and carrying out data balance by using an SMOTE technology, establishing and training a model in combination with a support classification characteristic gradient elevator, and carrying out regression or classification prediction on indexes representing nanoparticle migration; finally, analyzing the direction and size of the influence of different characteristics on the migration of the nano particles through a salpril accumulation interpretation method. Generalization of prediction is improved while the cost of a nanoparticle migration experiment is saved; the unbalanced data is subjected to data processing, so that the sample data quality and the prediction precision are improved; A model interpretation method is used for characteristic analysis, so that the nanoparticle migration behavior has interpretability.

Description

technical field [0001] The invention belongs to the field of prediction and analysis of nanoparticle parameters, and more specifically relates to a prediction method for nanoparticle migration, an analysis method and system for its influencing factors. Background technique [0002] As one of the application tools of nanotechnology, engineered nanoparticles are widely used in biomedicine, catalysis, electronics, energy, environment, medicine and other fields. However, as nanoparticles are widely used in consumer products, it is inevitable that they will enter porous media such as soil through various transmission routes, thereby causing pollution to the environment. In addition, nanoparticles also show great potential in petroleum industry applications, such as drilling and completion improvement, reservoir sensing imaging, and many other scenarios. Therefore, to predict the migration behavior of nanoparticles in porous media, to reduce the impact of nanoparticles on the env...

Claims

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Application Information

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IPC IPC(8): G06F30/20G06N99/00G06N3/00
CPCG06F30/20G06N99/007G06N3/006
Inventor 刘颉李尚元周凯波周翔张昌河张凯锋曹贯男
Owner HUAZHONG UNIV OF SCI & TECH
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