Response surface method and artificial neural network-based lead Stefan chemical combination process optimization method

By combining response surface methodology and artificial neural networks, the lead stemonate compounding process was optimized, solving the problems of purity and particle size distribution of lead stemonate during the compounding process. This improved the performance and safety of pyrotechnics and ensured the reliability of weapon systems.

CN122157827APending Publication Date: 2026-06-05NANJING UNIV OF SCI & TECH
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Patent Information

Application Number
CN202610278364.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-09
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Lead stemonate has problems such as large fluctuations in product purity, wide particle size distribution and poor particle flowability during the chemical combination process. This leads to uneven charge density in pyrotechnics, reduced detonation consistency and safety hazards, affecting the operational safety and tactical reliability of weapon systems.

Method used

By combining response surface methodology and artificial neural networks, key chemical process parameters were screened through single-factor experiments, a mathematical model was established, the interaction of parameters was analyzed, an artificial neural network model was constructed, and the combination of chemical process parameters was optimized to predict and optimize the performance of lead stemonate.

Benefits of technology

It improved the production efficiency and product performance of lead stemonate, reduced quality defects such as purity and flowability, and ensured the stability of product quality and the reliability of the production process.

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Abstract

The invention discloses a lead stefan chemical combination process optimization method based on a response surface method and an artificial neural network, belongs to the field of primary explosives and initiating explosive devices, and solves the problems that in an existing lead stefan production line, chemical combination process parameters are adjusted depending on artificial experience; the product purity fluctuation is large; the particle size distribution is wide; the free-running property is poor; and the batch consistency is insufficient. The method comprises the following steps: determining key chemical combination process parameters influencing the performance of the lead stefan based on collected related data of the chemical combination process of the lead stefan; and establishing a mathematical model for describing the key chemical combination process parameters and the relationship between the interaction of the key chemical combination process parameters and the lead Stefan performance indexes. According to the method, a stable and reliable performance influence rule is formed by optimizing the Stefan lead chemical combination process parameters, and the production efficiency is improved, so that the problem that the conventional performance control mainly depends on manual experience setting is solved.
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