Development of an artificial neural network model for rapid determination of NaOH solution concentration based on the transmission spectrum of low-energy gamma rays at 59.54 keV
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Abstract
This work presents a new approach for rapidly determining the concentration of NaOH solutions by measuring the attenuation of a low-energy 59.54 keV γ-ray beam emitted from a 241Am source and using an artificial neural network trained directly on raw spectral features. First, Monte Carlo simulations configured with the exact experimental geometry including a NaI(Tl) detector recording gamma ray transmission spectra through NaOH solutions of varying concentration are used to generate spectral data. Then these spectral data are directly extracted without any intermediate preprocessing to train and optimize a feed forward artificial neural network (ANN). The predictive performance of the optimized network is benchmarked against a conventional calibration curve (CF) that relates the transmission ratio (ln R) between water and NaOH solutions to their concentrations. The obtained results show that the ANN model achieves an average deviation of less than 3.0% from reference concentrations across the tested range, whereas the calibration curve yields deviations above 6,0%. In the low-concentration region (C = 5.0%), the ANN achieves a 2.5% deviation, markedly better than the approximately 12.6% deviation of the CF method. The findings suggest that integrating low-energy γ-ray transmission measurements with neural network spectra presents a promising method for high-precision monitoring of alkaline solutions, which may also be extended to another type of solution.
Keywords
gamma transmission, Monte Carlo simulation, MLP-ANN, NaOH, concentration
Article Details
References
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