Hanoi University of Mining and Geology

Nguyen H., Bao T.D., Bui X., Pham V., Nguyen D., Do N., Hoa L.T., Le Q., Le T.

Predicting flyrock is key to safety and efficiency in open pit mining. In this study, we developed and tested four hybrid models utilizing an adaptive neuro–fuzzy inference system (ANFIS) integrated with metaheuristic optimization techniques: Lévy-enhanced Jaya (ANFIS–LJ), bat algorithm (ANFIS–BA), firefly algorithm (ANFIS–FA) and social spider optimization (ANFIS–SSO). Remarkably, the Lévy technique was applied to enhance the JA algorithm and improve the performance of the ANFIS model for predicting flyrock distance. The models were trained and tested using a dataset from Ta Phoi copper mine with 204 blast events and flyrock distance as the target variable. A drone was used to measure flyrock distance in this study with high resolution to capture the entire flyrock phenomenon of each blast. The k-fold cross-validation technique (with 5 folds) was applied to ensure that AI-based models are not only accurate but also generalize well to new data. It helps in evaluating model performance, tuning hyperparameters, reducing overfitting, and providing a more reliable estimate of how the model will perform in predicting blast-induced flyrock. The models were evaluated using MAE (mean absolute error), RMSE (root mean-squared error) and R2. The result showed that ANFIS–LJ outperformed the other models with MAE of 1.423, RMSE of 1.895 and R2 of 0.981 on the testing dataset. It was also validated through 13 blasts in practice and achieved a high R2 of 0.988, indicating excellent agreement between predicted and observed flyrock distances. Besides, the low MAE (1.322) and RMSE (1.825) values confirmed the model's precision and reliability in predicting flyrock distances. These results confirmed its potential as a valuable tool for optimizing blast designs, enhancing safety, and reducing environmental impacts in real-world engineering applications. This study showed that combining ANFIS with metaheuristic algorithms, especially Lévy-enhanced Jaya algorithm, can produce accurate flyrock prediction. The result can be used to improve the predictive model in open pit mining and decision making. Future study can focus on refining the models and applying them in different mining environments to improve the accuracy.

Dao K.A., Chung N.X., Nguyen T.T.

Presently, the applications of metallic nanoparticles, nanomaterials, and plasmonic structures have been widely using into the different plasmonic solar cells aiming for enhancing the power conversation efficiency. When nanoparticles (Au, Ag...) added into anatase TiO2 structure being in solar cell, then different plasmonic effects of plasmon excitation surface plasmonic resonance will be stimulated by incident light, the electron oscillation, scattering, and guiding of light into active materials, and plasmon near-field will be dominated and can increase the collection performance efficiency. Based on our obtained experiment results of the enhanced optical absorption spectra for single plasmonic structures (AuNPs@TiO2, /a-Si/AuNPs) and dual combined /a-Si/AuNPs/AuNPs@TiO2/ plasmonic structure being in different samples groups, this paper, using a rather new approach, outlines the theoretical aspects and calculation of carriers generation rates in different forms under AM 1.5 irradiation for each mentioned plasmonic structure to reveal the contribution effect of carrier generation rates causing the optical absorption spectra enhancements. The calculated results show the intrinsic relationship between the measured and theoretically calculated results concerning the optical absorption spectra and carrier generation rate enhancements. The carrier generation caused by single AuNPs@TiO2 plasmonic structure is poor, while that caused by the single /a-Si/AuNPs/ plasmonic structure is considerably large, especially the carrier generation process caused by dual combined /a-Si/AuNPs/AuNPs@TiO2/ plasmonic structure is the largest. The obtained results are analyzed and proposed for the potential application in plasmonic solar cells for increasing power conversation efficiency.

Nguyen H.N., Nguyen T.P., Le P.T., Tran Q.M., Do T.H., Nguyen T.D., Tran-Nguyen P.L., Tsubota T., Dinh T.M.

Cost-effective CO2 adsorbents are gaining increasing attention as viable solutions for mitigating climate change. In this study, composites were synthesized by electrochemically combining the post-gasification residue of Macadamia nut shell with copper benzene-1,3,5-tricarboxylate (CuBTC). Among the different composites synthesized, the ratio of 1:1 between biochar and CuBTC (B 1:1) demonstrated the highest CO2 adsorption capacity. Under controlled laboratory conditions (0°C, 1 bar, without the influence of ambient moisture or CO2 diffusion limitations), B 1:1 achieved a CO2 adsorption capacity of 9.8 mmol/g, while under industrial-like conditions (25°C, 1 bar, taking into account the impact of ambient moisture and CO2 diffusion limitations within a bed of adsorbent), it reached 6.2 mmol/g. These values surpassed those reported for various advanced CO2 adsorbents investigated in previous studies. The superior performance of the B 1:1 composite can be attributed to the optimization of the number of active sites, porosity, and the preservation of the full physical and chemical surface properties of both parent materials. Furthermore, the composite exhibited a notable CO2/N2 selectivity and improved stability under moisture conditions. These favorable characteristics make B 1:1 a promising candidate for industrial applications.

Van Thiet D., Chung N.X., Tuan N.Q., Tung N.T., Hai H.T., Tu D.N., Cho S.

FeMn2O4-δ epitaxial films were grown on a MgO (100) substrate by using molecular beam epitaxy (MBE) at low growth temperature from 100 to 300 °C in oxygen-cracked environment. The results shows that the structural properties of films are single crystalline with flat surface at growth temperatures of 200 °C. The tensile strain of films decrease as growth temperature increase which is confirmed by X-ray diffraction characterization. The optical band gaps of films are 3.44, 3.50, and 3.20 eV with growth temperatures of 100 °C, 200 °C, and 300 °C, respectively. In addition, the films possess magnetic properties in ferrimagnetic ordering at room temperature without any magnetic transition below this temperature; magnetization of films decreases as growth temperature increase. The results average magnetic moment per unit are estimated to be 3.72, 2.84, and 2.13 µB/f.u corresponding to 100 °C, 200 °C, and 300 °C of growth temperature. Our work provides a study on structural and magnetic properties of FeMn2O4-δ in thin film form, which has potential application in fabrication of spin-filter devices.

Vo Thi C.T., Manh Do D., Nguyen Duc K., Xuan Le H., Minh Phan X.

Nguyen H.T., Ganat T.A., Truong T.V.

Phuong N., Giang H., Hai D., Osial M., Nam N., Thanh D.

ABSTRACTRising global warming concerns drive the demand for cost‐effective CO2 capture technologies for industrial reuse. In this study, a novel composite material for CO2 adsorption based on superparamagnetic iron oxide nanoparticles (SPIONs) and copper benzene‐1,3,5‐tricarboxylate (CuBTC) was proposed by a chronoamperometry electrochemical method. XRD, FT‐IR, and SEM–EDX techniques confirmed the presence of SPION and CuBTC in the synthesized composites. SPIONs were small, uniform, and spherical, facilitating their effective combination with CuBTC. Among the synthesized composites, the 1:1 ratio of SPION to CuBTC exhibited the highest CO2 adsorption capacity. Under controlled laboratory conditions (0°C, 1 bar, without the influence of ambient moisture or CO2 diffusion limitations), the SPION/CuBTC 1:1 composite demonstrated a CO2 adsorption capacity of 3.5 mmol/g. Under more realistic conditions (25°C, 1 bar, with the influence of ambient moisture) the SPION/CuBTC 1:1 composite exhibited the highest CO2 adsorption efficiency of 12% (expressed as weigh percentage of dry sorbent). The CO2 adsorption capacity of the composite decreased by more than half when the CO2 concentration dropped from 100% to 15%. The adsorption mechanism is primarily driven by chemical adsorption via surface functional groups and physical adsorption through microcapillary formation and van der Waals interactions, mainly due to CuBTC. The incorporation of SPION in the composite accelerated the CO2 desorption process. The combined magnetic behavior and heat generation results suggest that SPION/CuBTC composites possess enhanced magnetic properties and thermal responses, highlighting their potential for efficient heat‐mediated desorption in industrial applications.

Qin X., Su H., Yu L., Wang H., Jiang Y., Pham T.

ABSTRACTThis work examines the effect of loading rate ( ) on the mode III fracture behavior of sandstone. Edge‐notched diametrically compressed (ENDC) disc sandstone specimens were tested under different static and dynamic mode III fracture loadings, revealing a clear loading rate effect on both mode III and mode I fractures. Specifically, the peak load and fracture toughness (KIIIC, KIC) increase as the increases across both static and dynamic scales. At the static scale, the KIIIC is about 1.28–1.38 times of the KIC, whereas at the dynamic scale, the KIIIC is less than the KIC. The relationship between KIIIC and KIC is affected by the loading scale and the shape of the specimen, but the data collected thus far indicate that the origin and type of rock have minimal effect on this relationship. In addition, the fracture surface morphology characteristics were quantitatively analyzed.

Tran N.T., Tran Q.T., Le H.V., Nguyen D., Tran V.T., Nguyen D.H.

Nguyen T.H., Tuan N.T., Le H.A.

The mining industry plays a crucial role in the global economy by providing essential resources such as coal, oil, gas, and metal ores. However, the complexity and diversity of the mining process present many challenges in supply chain management (SCM), including transparency, traceability, efficiency, and growing demands to adhere to environmental regulations and uphold sustainability practices. The emergence of Industry 5.0 has led to a transformation in supply chain dynamics, ushering in the era of sustainable supply chains. This paper proposes a Blockchain and Smart Contract framework to monitor sustainable mining SCM (SMSCM). Blockchain technology ensures a secure decentralized supply chain through a peer-to-peer network. By incorporating innovative tools like Unmanned Aerial Vehicles (UAVs), LiDAR, and remote sensing tools, the framework addresses key issues including provenance, trust, consensus, inventory management, volume verification, and SCM monitoring. A methodology based on the Ethereum blockchain is outlined, demonstrating functions through a case study on coal mining in Vietnam, with detailed Smart Contracts (SC) implementation analysis.

Nguyen H., Van Thieu N.

Flyrock from blasting in open-pit mining is one of the most dangerous occurrences that can cause accidents to workers, damage to machinery and equipment and even fatalities. Therefore, quick and reliable prediction of blast-induced flyrock distance (BIFRD) in open-pit mines is very crucial to ensure the safety of the surrounding environment. In this study, unmanned aerial vehicle (UAV) technology combined with advanced artificial intelligence techniques was used to predict BIFRD in open-pit mines and improve safety. UAV was used to record blasting operations and the resulting flyrock. The distance of the flyrock was then measured from the recorded video footage and was analyzed using the ProAnalyst software. Then, various metaheuristics-optimized ANFIS (adaptive neuro-fuzzy inference system) was developed to predict BIFRD. These networks were optimized using adaptive differential evolution with optional external archive (JADE), genetic algorithm (GA), fireworks algorithm (FWA), and artificial bee colony (ABC) algorithms and resulted to JADE–ANFIS, GA–ANFIS, FWA–ANFIS, and ABC–ANFIS models. A dataset with 204 blasting events was gathered and analyzed, and finally, only four input variables were used for developing these models, including spacing, weight charge, stemming, and powder factor. The results showed that JADE–ANFIS is the best with high accuracy (97.8%), good generalizability (MAPE of 1.1%), and reasonable training time for predicting BIFRD in this study. The other models performed poorly with accuracy ranging from 88.7 to 96.5% and MAPE ranging from 1.4 to 3.0%. Sensitivity analysis also showed that the length of stemming is the most affecting factor to flyrock distance in blasting and thus careful consideration should be given in designing blast patterns to control flyrock distance in open-pit mines.

Lam T.V., Luong P.D., Trong V.D., Bulgakov B.I., Bazhenova S.I.

The existing methods of obtaining cellular concrete products with a variatropic structure are analyzed. It is revealed that each of them has its own advantages and disadvantages. A new technology for the production of cellular concretes of variable density has been developed, which makes it possible to manufacture construction products in Vietnam from local raw materials with high performance characteristics and meeting modern requirements for energy efficiency and durability.According to the test results, it was found that at the hardening age of 28 days, the average density in the dry state and in the state of normal humidity is in the range of 1085–1608 and 960–1517 kg/m3, respectively. Strength tests have shown that the developed concrete reaches an average compressive strength of 13.5–25.4 MPa on the 28th day of hardening. It can be concluded that the combination of foam and gas-forming components used in the formulation made it possible to obtain cellular concrete with an anisotropic structure, having the required indicators of compressive strength and average density in a wet state, which will be in demand in Vietnam during the construction of facilities for various purposes.

Do T., Phan V.

Le N., Männel B., Bui L.K., Schuh H.

Abstract The development of Global Navigation Satellite Systems (GNSS) results in large spatial geodetic networks with a distinct range of accuracy. Thus, classification of the GNSS stations is needed to determine which stations are appropriate for geodetic applications. Additionally, advanced Machine Learning (ML) techniques have been proposed. However, ML algorithms may sometimes be less sensitive due to a lack of samples or anomalies in input data. Therefore, this study introduces an approach in which human-based supervision is integrated into ML processes to improve the ML model’s performance in classifying the continuous GNSS stations. The human factor influences the ML processes through two sampling strategies: “suggest-decide” and “correct-retrain”, where the accuracy of ML models will be improved via human-based corrections. The idea is that the unsupervised ML-based clustering techniques are driven by human-based supervision to create samples for training the supervised ML-based classification models. In this study, we develop a MATLAB app to automate the clustering and labeling processes. Our finding demonstrates that applying these sampling strategies can enhance the accuracy of the ML-based classification models from under 50 % up to $$\sim$$ ∼ 99 % after re-training. Also, this study categorizes almost 9000 continuous monitoring stations in the Nevada database, of which 1900 stations in Europe serve as samples for training the ML-based classification models. Furthermore, the methodologies developed in this study can be applied to warning systems, which utilize internal and external human resources to correct errors, address unusual situations, and provide timely feedback for better performance of ML-based forecasts.

Dung T.T., Kulinich R.G., Duong T.T., Van Sang N., Minh N.Q., Lap T.T., Dai N.B.

This article is devoted to the study of the correlation between the location and strike direction of oil and gas accumulations and tectonic faults in the Western South China Sea. The sedimentary basins of Red River, Phu Khanh, Cuu Long, Nam Con Son, Tu Chinh-Vung Mai, and Malay were selected as the study areas. All available geologic information about these basins was collected for analysis. In addition, marine and satellite gravity data and tilt angle of the horizontal gravity gradient method were used to identify new faults. As a result, a close correlation between the spatial position and strike direction of oil and gas accumulations and fault systems has been revealed in these basins. The discovered correlation allows us to take a new look at the genesis and evolution of oil-gas accumulations and fields in the South China Sea region. The method we used can be applied as an effective tool for oil and gas exploration in sea areas.