Online analysis of iron ore slurry using PGNAA technology with artificial neural network

Haolong Huang; Pingkun Cai; Xuwen Liang; Wenbao Jia

Nuclear Engineering and Technology (Jul 2024)

Online analysis of iron ore slurry using PGNAA technology with artificial neural network

Haolong Huang,
Pingkun Cai,
Xuwen Liang,
Wenbao Jia

Affiliations

Haolong Huang: Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
Pingkun Cai: Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
Xuwen Liang: Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
Wenbao Jia: Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China; Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Suzhou, 215000, China; Corresponding author. Department of Nuclear Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.

Journal volume & issue: Vol. 56, no. 7
pp. 2835 – 2841

Abstract

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Real-time analysis of metallic mineral grade and slurry concentration is significant for improving flotation efficiency and product quality. This study proposes an online detection method of ore slurry combining the Prompt Gamma Neutron Activation Analysis (PGNAA) technology and artificial neural network (ANN), which can provide mineral information rapidly and accurately. Firstly, a PGNAA analyzer based on a D-T neutron generator and a BGO detector was used to obtain a gamma-ray spectrum dataset of ore slurry samples, which was used to construct and optimize the ANN model for adaptive analysis. The evaluation metrics calculated by leave-one-out cross-validation indicated that, compared with the weighted library least squares (WLLS) approach, ANN obtained more precise and stable results, with mean absolute percentage errors of 4.66% and 2.80% for Fe grade and slurry concentration, respectively, and the highest average standard deviation of only 0.0119. Meanwhile, the analytical errors of the samples most affected by matrix effects was reduced to 0.61 times and 0.56 times of the WLLS method, respectively.

Published in Nuclear Engineering and Technology

ISSN: 1738-5733 (Print)
Publisher: Elsevier
Country of publisher: Korea, Republic of
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering: Nuclear engineering. Atomic power
Website: http://www.journals.elsevier.com/nuclear-engineering-and-technology

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