Title: Optimise a ventilation system in underground mines using artificial neural networks

Authors: Marco Cotrina; José Mamani; Solio Arango; Jairo Marquina; Eduardo Noriega; Dominga Cano; Teofilo Donaires; Joe Gonzalez; Tomas Anticona

Addresses: Department of Mining Engineering, Universidad Nacional de Trujillo, Trujillo, 13001, Perú ' Faculty of Chemical Engineering, National University of the Altiplano, Puno, 21001, Peru ' Department of Mining Engineering, Universidad Nacional de Trujillo, Trujillo, 13001, Perú ' Department of Mining Engineering, Universidad Nacional de Trujillo, Trujillo, 13001, Perú ' Department of Mining Engineering, Universidad Nacional de Trujillo, Trujillo, 13001, Perú ' Faculty of Industrial Process Engineering, National University of Juliaca, Juliaca, 21101, Perú ' Faculty of Chemical Engineering, National University of the Altiplano, Puno, 21001, Peru ' Department of Industrial Engineering, National University of Trujillo, Trujillo, 13001, Perú ' Department of Mining Engineering, National University of Trujillo, Trujillo, 13001, Perú

Abstract: This study aimed to optimise ventilation systems in underground mines using artificial neural networks (ANNs) to reduce temperature and relative humidity while improving airflow. A dataset of 66 samples was used to train the ANN model, which included 4 input parameters, 128 and 64 hidden neurons, and 3 output parameters. The model was tested with 70% of data in training and 30% in validation. The results demonstrated that the ANN showed strong predictive performance for temperature and humidity, achieving R² values of 0.88 and 0.96, respectively, in the validation data. Additionally, the model achieved an R² of 0.81 for airflow data, indicating reliable predictions. The ANN also successfully optimised the ventilation parameters, resulting in a temperature reduction of 6.13°C, a 5.61% decrease in humidity, and an increase of 110.80 CFM in airflow. In conclusion, the study confirms that artificial neural networks can effectively optimise a ventilation system in mines.

Keywords: ventilation; underground mines; artificial neural networks; ANNs.

DOI: 10.1504/IJMME.2025.146855

International Journal of Mining and Mineral Engineering, 2025 Vol.16 No.2, pp.131 - 147

Received: 21 Dec 2023
Accepted: 07 Nov 2024
Published online: 23 Jun 2025 *

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