ANFIS Modeling for Prediction of Particle Size in Nozzle Assisted Solvent-Antisolvent Process for Making Ultrafine CL-20 Explosiv

ANFIS Modeling for Prediction of Particle Size in Nozzle Assisted Solvent-Antisolvent Process for Making Ultrafine CL-20 Explosiv

Dinesh K. Pal, Shallu Gupta, Deepika Jindal, Anil Kumar, Arun Aggarwal, Prem Lata

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Abstract. Physical properties such as particle size, surface area and shape of explosive control the rapidity and reliability of initiation, and detonation and thus determine the performance of an explosive device such as slapper detonators. In this paper, Nozzle assisted solvent/antisolvent (NASAS) process for recrystallisation of CL-20 explosive is established. Many process parameters are involved which affect the particle size of the explosive. Therefore an accurate prediction of particle size is required to tailor the particle size. In the present work, an intelligent algorithm is applied to build a simplified relationship between recrystallization process parameters and particle size. This can be used to predict explosive particle size with a wide range of process parameters through an adaptive neuro-fuzzy inference system (ANFIS). The model is trained using experimental data obtained from design of experiment techniques utilizing a MATLAB software. Six process parameters such as Solution pressure, Antisolvent pressure, Antisolvent temperature, Stirrer speed, Solution concentration and Nozzle diameter are used as input variables of the model and the particle size is used as the output variable. The predicted results are in close agreement with experimental values and the accuracy of the model has been tested by comparing the simulated data with actual data from the explosive recrystallization experiments and found to be inacceptable range with maximum absolute percentage error of 11.52 %. The ultrafine CL-20 prepared by NASAS process is used in Slapper detonator application. The threshold initiation voltages for CL-20 based slapper detonator is found to be in the range of 0.9 kV with standard deviation of ±0.1 kV.

Keywords
Ultrafine CL-20, Artificial Neural Network, ANFIS, Neuro-Fuzzy

Published online 7/30/2019, 7 pages
Copyright © 2019 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Dinesh K. Pal, Shallu Gupta, Deepika Jindal, Anil Kumar, Arun Aggarwal, Prem Lata, ANFIS Modeling for Prediction of Particle Size in Nozzle Assisted Solvent-Antisolvent Process for Making Ultrafine CL-20 Explosiv, Materials Research Proceedings, Vol. 13, pp 121-127, 2019

DOI: https://doi.org/10.21741/9781644900338-21

The article was published as article 21 of the book Explosion Shock Waves and High Strain Rate Phenomena

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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