Ammonium perchlorate moisture quantitative detection using terahertz spectroscopy combined with chemometrics

Ammonium perchlorate (AP) occupies an important position in solid propellants. However, the increase of moisture content in AP will seriously affect its combustion performance and mechanical properties, resulting in the abnormal use of solid propellants, which may bring serious security risks. There are very few studies on the accurate measurement of AP moisture content, which makes this study of great value and significance. In this paper, the optical parameters of aqueous AP samples were measured by terahertz time domain spectroscopy. Combined with chemometrics, five quantitative prediction models were constructed. The results show that the absorption coefficient based genetic algorithm optimized extreme learning machine model has the best prediction results. This method can achieve accurate prediction for moisture content below 0.1%, and its absolute error is less than 0.001%, and the prediction error of the external verification model of different groups of samples is less than 0.06%. This study provides a reliable basis for rapid, accurate and non-destructive detection of the moisture content of ammonium perchlorate and similar substances.

Automated summary

This study utilizes terahertz time domain spectroscopy to measure the optical parameters of aqueous AP samples and constructs five quantitative prediction models. The optimized extreme learning machine model based on the absorption coefficient with the help of chemometrics shows the best prediction results for moisture content. The research provides a reliable basis for accurate prediction of the moisture content of AP and similar substances, paving the way for rapid and non-destructive detection methods.