A neural network-based method for identifying semi-batch reaction thermal behavior

By defining thermal behavior labels in a semi-batch reactor, extracting core features, constructing a dimensionless mathematical model, and optimizing a two-layer BP neural network, the problems of insufficient feature representativeness and applicability in thermal behavior recognition in existing technologies are solved, achieving high-precision and robust thermal behavior recognition.

CN116631523BActive Publication Date: 2026-06-09HEBEI UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HEBEI UNIV OF TECH
Filing Date
2023-02-27
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing technologies for identifying thermal behavior in semi-batch reactors suffer from problems such as insufficient feature representativeness, risk of overfitting, insufficient applicability, and underutilization of neural network algorithms.

Method used

By defining hot behavior labels, extracting the core numerical features of traditional criteria, establishing a dimensionless mathematical model, generating a dataset and preprocessing it, selecting a two-layer BP neural network as the optimal algorithm, and combining genetic algorithm and Bayesian regularization to optimize network weights, a hot behavior recognition method is constructed.

Benefits of technology

It achieves more comprehensive thermal behavior characterization, improves recognition accuracy and robustness, has stronger generalization ability and versatility, avoids overfitting, and is applicable to a variety of reaction systems.

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Abstract

The application discloses a kind of based on neural network's semi-batch reaction thermal behavior identification method, first define thermal behavior label, solve dimensionless mathematical model;Again, extract core numerical features from traditional criterion, and match with NI, TR and QFS thermal behavior to constitute data set;Again, data set is preprocessed, and is divided;Again, the recognition accuracy and generalization ability of different machine learning algorithms to reaction thermal behavior are analyzed and compared, and double-layer BP neural network is selected;Then determine the structure of double-layer BP neural network;Again, the weight and threshold of double-layer BP neural network are optimized using genetic algorithm and bayesian regularization algorithm;Again, confusion matrix is drawn to evaluate the classification performance of double-layer BP neural network;Finally, dimensionless mathematical model and optimized double-layer BP neural network are deployed, simulate and thermal behavior prediction for semi-batch reaction, effectively solve the problem that reaction system thermal behavior identification angle is not comprehensive, with stronger generalization ability and universality.
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