Automation of data acquisition from a one-way Triaxial Permeameter using Arduino sensors

The determination and interpretation of hydraulic conductivity is an intricate process in porous medium. Accurate measurement of hydraulic conductivity requires laboratory testing, monitoring and prediction. This study focuses on the fabrication of a one-way triaxial permeameter with a flexible wall for continuous permeability monitoring and hydraulic conductivity prediction using machine learning. The fabricated apparatus is equipped with real-time Arduino sensors to record humidity, backpressure, the mass of effluent water, room temperature and effluent water temperature. In the electrical circuit design, the capacitor and inductor were taken from a damaged electronic apparatus and used to buffer noise when the voltage in the circuit fluctuates. An artificial neural network model in R was used to predict the mass of the static load of 50g, the dynamic load of seeped water, and the coefficient of permeability. The standard deviation of the applied static load over 24 hours by regression analysis from day one to day five were 1.08, 1.99, 1.68, 0.99 and 0.75, respectively. The combined 5-day data showed a variance of 4.4, a standard deviation of 2.1, and a mean mass value of 49.93g. The predicted coefficient of hydraulic conductivity of the investigated samples of copper tailing cement slurry cured for 31 days comprised of 25% cement dosage was 1.73e-9 m/s with a high coefficient of determination R=0.97. The use of real-time sensing and machine learning can help accurately determine hydraulic conductivity.

Fisonga M., Deng Y., Wang F., Chikutwe Chanda E. K., Mutambo V., Bunda B., Korir E., Bwalya D., Liyungu J., Chipola P. Automation of data acquisition from a one-way Triaxial Permeameter using Arduino sensors. MIAB. Mining Inf. Anal. Bull. 2022;(10-2):5—23. [In Russ]. DOI: 10.25018/0236_1493_2022_102_0_5.

Fisonga M.^1,2, PhD Student at Southeast University and Lecturer at the University of Zambia, e-mail: fieldmarsheal@gmail.com, ORCID ID: 0000-0002-6261-4126;
Deng Y.¹, Professor, Deputy Director of Underground Engineering, Department, e-mail: noden@seu.edu.cn, ORCID ID: 0000-0002-8223-8711;
Wang F.¹, Professor, e-mail: feiwang@seu.edu.cn, ORCID ID:0000-0001-7844-5008;
Chikutwe Chanda E. K.², Professor, Head of Mining Department, e-mail: emmanuel.chanda@unza.zm, ORCID ID:0000-0002-2102-9342;
Mutambo V.², Senior Lecturer, e-mail: vmutambo@unza.zm, ORCID ID: 0000-0003-4394-7192;
Bunda B.², Senior Lecturer, Dean School of Mines, e-mail: bbesa@unza.zm, ORCID ID: 0000-0001-9133-592X,
Korir E.³, Manager, e-mail: ekorir@belgraviaservices.com;
Bwalya D.⁴, Researcher, e-mail: danny.bwalya@energiasimples.pt;
Liyungu J.⁵, Lecturer, e-mail: jliyungu84@gmail.com;
Chipola P.⁶, Mining Dispatcher, e-mail: chipolapatrick28@gmail.com;
¹ Institute of Geotechnical Engineering, School of Transport Engineering, Southeast University, Nanjing, China;
² School of Mines, University of Zambia, Great East Road Campus, PO Box 32379, Lusaka, Zambia;
³ Belgravia Services Limited;
⁴ R&D Dept Simples Energia Lda Rua Aleixo da mota 86, R/C 4150−044 Porto, Portugal;
⁵ School of Engineering, University of Zambia, Great East Road Campus, P. O. Box 32379, Lusaka, Zambia;
⁶ FQM Trident limited, Kalumbila, Northwestern, Zambia.

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