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The accuracy and rapidity of total iron content (TFE) analysis can accelerate iron ore production. Although the conventional TFE detection methods are accurate, its detection speed presents difficulties in meeting production requirements. Therefore, this paper proposes a method of TFE detection based on reflectance spectroscopy (wavelength range: 340–2500 nm) and remote sensing. Firstly, spectral experiments were conducted on iron ore using the HR SVC-1024 spectrometer to obtain spectral data for each sample. Then, the spectra were smoothed and dimensionally reduced by using wavelet transform and principal component analysis. To improve the detection accuracy of TFE, a two hidden layer extreme learning machine with variable neuron nodes based on an improved sparrow search algorithm and batch normalization optimization (MSSA-BNVTELM) is proposed. According to the experimental results, MSSA-BNVTELM exhibited superior detection accuracy in comparison to other algorithms. In addition, this research established a remote sensing detection model using Sentinel-2 data and MSSA-BNVTEM to detect the distribution of TFE in the mining area. The distribution of TFE in the mine area was plotted based on the detection results. The results show that the remote sensing of the mine area can be useful for detection of the TFE distribution, providing assistance for the mining plan. [ABSTRACT FROM AUTHOR]

The safety retaining wall is a critical infrastructure in ensuring the safety of both rock removal vehicles and personnel. However, factors such as precipitation infiltration, tire impact from rock removal vehicles, and rolling rocks can cause local damage to the safety retaining wall of the dump, rendering it ineffective in preventing rock removal vehicles from rolling down and posing a huge safety hazard. To address these issues, this study proposed a safety retaining wall health assessment method based on modeling and analysis of UAV point-cloud data of the safety retaining wall of a dump, which enables hazard warning for the safety retaining wall. The point-cloud data used in this study were obtained from the Qidashan Iron Mine Dump in Anshan City, Liaoning Province, China. Firstly, the point-cloud data of the dump platform and slope were extracted separately using elevation gradient filtering. Then, the point-cloud data of the unloading rock boundary was obtained via the ordered crisscrossed scanning algorithm. Subsequently, the point-cloud data of the safety retaining wall were extracted using the range constraint algorithm, and surface reconstruction was conducted to construct the Mesh model. The safety retaining wall mesh model was isometrically profiled to extract cross-sectional feature information and to compare the standard parameters of the safety retaining wall. Finally, the health assessment of the safety retaining wall was carried out. This innovative method allows for unmanned and rapid inspection of all areas of the safety retaining wall, ensuring the safety of rock removal vehicles and personnel. [ABSTRACT FROM AUTHOR]

Step-feature lines are one of the important geometrical elements for drawing the status quo maps of open-pit mines, and the efficient and accurate automatic extraction and updating of step-feature lines is of great significance for open-pit-mine stripping planning and analysis. In this study, an automatic extraction method of step-feature lines in an open-pit mine based on unmanned-aerial-vehicle (UAV) point-cloud data is proposed. The method is mainly used to solve the key problems, such as low accuracy, local-feature-line loss, and the discontinuity of the step-feature-line extraction method. The method first performs the regular raster resampling of the open-pit-mine cloud based on the MLS algorithm, then extracts the step-feature point set by detecting the elevation-gradient change in the resampled point cloud, further traces the step-feature control nodes by the seed-growth tracking algorithm, and finally generates smooth step-feature lines by fitting the space curve to the step-feature control nodes. The results show that the method effectively improves the accuracy of step-feature-line extraction and solves the problems of local-feature-line loss and discontinuity. [ABSTRACT FROM AUTHOR]

Highlights • We proposed a new IAM-ELM neural network algorithm. • We proposed a rapid coal classification method. • Our method has the advantage of economy, speed and accuracy. • Our method has important practical application value. Abstract Coal classification is an indispensable task in coal mining and production. The traditional method of coal classification has the disadvantages of high cost, low speed and low accuracy. Therefore, a rapid coal classification method based on visible-infrared spectroscopy is proposed in this research. First, we collected samples of different coal types and used spectrometers to measure the spectral data of these samples. Then, we proposed an improved multilayer extreme learning machine algorithm using this algorithm to build a coal classification model. The simulation results showed that the model has good classification results. Compared with the traditional coal classification methods, this method has an unparalleled advantage in economy, speed and accuracy. [ABSTRACT FROM AUTHOR]

In this work, Pr 0.6 Sr 0.4 FeO 3− δ –Ce 0.9 Pr 0.1 O 2− δ (PSFO–CPO) nanofibers were synthesized by a one-step electrospin technique for use in intermediate-temperature solid oxide fuel cell (IT-SOFC) applications. PSFO–CPO nanofibers were produced with a diameter of about 100 nm and lengths exceeding tens of microns. The thermal expansion coefficient (TEC) matches with standard GDC electrolytes and the resulting conductivity also satisfies the needs of IT-SOFCs cathodes. EIS analysis of the nanofiber structured electrode gives a polarization resistance of 0.072 Ω cm 2 at 800 °C, smaller than that from the powdered cathode with the same composition. The excellent electrochemical performance can be attributed to the well-constructed microstructure of the nanofiber structured cathode, which promotes surface oxygen diffusion and charge transfer processes. All the results imply that the one-step electrospin method is a facile and practical way of improving the cathode properties and that PSFO–CPO is a promising cathode material for IT-SOFCs. [ABSTRACT FROM AUTHOR]

A 10 mol%Sc 2 O 3 , 1 mol%CeO 2 stabilized-ZrO 2 (SSZ) powder was successfully prepared using the sol–gel method. Subsequent SSZ electrolyte pellets were prepared by tape casting technique and sintered at 1400 °C, 1450 °C, 1500 °C, 1550 °C and 1600 °C. These were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and electrochemical impedance spectroscopy (EIS). SSZ showed a pure cubic phase after sintering, the grain size of SSZ increased with the increase of sintering temperature. The SSZ sintered at 1550 °C showed the highest ion conductivity. The maximum power densities of Ni-SSZ/SSZ/La 0.8 Sr 0.2 MnO 3− δ (LSM)-SSZ single cells sintered at 1550 °C were 0.18, 0.36, 0.51 and 0.72 W cm −2 at 650, 700, 750 and 800 °C, respectively. The polarization resistance ( R p ) of the single cell attained 0.201 Ω cm 2 at 800 °C. [ABSTRACT FROM AUTHOR]

Cobalt-free composite cathodes consisting of Pr 0.6 Sr 0.4 FeO 3−δ – x Ce 0.9 Pr 0.1 O 2−δ (PSFO- x CPO, x = 0–50 wt%) have been synthesized using a one-pot method. X-ray diffraction, scanning electron microscopy, thermal expansion coefficient, conductivity, and polarization resistance ( R P ) have been used to characterize the PSFO- x CPO cathodes. Furthermore the discharge performance of the Ni-SSZ/SSZ/GDC/PSFO- x CPO cells has been measured. The experimental results indicate that the PSFO- x CPO composite materials fully consist of PSFO and CPO phases and posses a porous microstructure. The conductivity of PSFO- x CPO decreases with the increase of CPO content, but R P of PSFO-40CPO shows the smallest value amongst all the samples. The power density of single cells with a PSFO-40CPO composite cathode is significantly improved compared with that of the PSFO cathode, exhibiting 0.43, 0.75, 1.08 and 1.30 W cm −2 at 650, 700, 750 and 800 °C, respectively. In addition, single cells with the PSFO-40CPO composite cathode show a stable performance with no obvious degradation over 100 h when operating at 750 °C. [ABSTRACT FROM AUTHOR]

In this work, anode-supported solid oxide fuel cells (SOFCs) has been successfully prepared by tape casting and co-sintering technique. The structure contains the thin and dense 10% Sc 2 O 3 -stabilized-ZrO 2 (SSZ) electrolyte layer and the anode layers with continuous porosity gradient. The decomposition of organic additives and the shrinkage of green tape were analyzed by thermo-gravimetric and differential thermal analyses (TG-DSC) and thermal dilatometry. The microstructure morphologies and the porosity of anodes were examined by SEM and Archimedes method. The obtained anodes with continuous porosity gradient facilitate diffusion of the fuel gas and enlarge electrochemical active three phase boundaries. The open circuit voltage (OCV) was close to 1.1 V. The maximum power densities of single cell with graded Ni-SSZ anode were higher than non-gradient Ni-SSZ anode at 600, 650, 700, 750 and 800 °C and were 0.15, 0.30, 0.51, 0.70 and 1.00 W cm −2 , respectively. R p of cell with graded Ni-SSZ anode was lower than that of non-gradient Ni-SSZ anode. [ABSTRACT FROM AUTHOR]

Annular furnaces have multivariate, nonlinear, large time lag, and cross coupling characteristics. The prediction and control of the exit temperature of a tube billet are important but difficult. We establish a prediction model for the final temperature of a tube billet through OS-ELM-DRPLS method. We address the complex production characteristics, integrate the advantages of PLS and ELM algorithms in establishing linear and nonlinear models, and consider model update and data lag. Based on the proposed model, we design a prediction control algorithm for tube billet temperature. The algorithm is validated using the practical production data of Baosteel Co., Ltd. Results show that the model achieves the precision required in industrial applications. The temperature of the tube billet can be controlled within the required temperature range through compensation control method. [ABSTRACT FROM AUTHOR]

Abstract: Understanding the sintering processes extensively is critical in fabricating a flat cell for solid oxide fuel cell stacks, but few have reported the sintering process and stress development during the constrained sintering of the electrolyte layer on anode substrate. In this study, we show that the green tape of half cell fabricated by co-tape casting cracks into several pieces when it is heated directly to 1400 °C of profile I, while it remains flat and complete when the green tape is sintered with additional pre-sintered profile at 1300 °C of profile II. The strain rate characteristics indicate that the difference of 2.43 × 10−6 s−1 between the electrolyte and the anode layer leads to the stress development in the directly sintered cell, while it reduces to 6.7 × 10−8 s−1 for the pre-sintered cell, which is only 3% of that without pre-sintering. The stress based on continuum model calculated results in the sintered cell demonstrates that the stress increases from 0 at about 1000 °C to 2.60 MPa at 1300 °C, and increased from 2.60 MPa to 6.54 MPa in temperature range of 1300–1400 °C. But it was lower than half of the stress for the pre-sintered cell according to profile II. The SEM images, together with a circuit voltage of 2.22 V for two cell stack, indicate that the electrolyte of the unit cell is dense. The power is 41.7 W, with a power density of 0.26 W cm−2 at 1.4 V and 750 °C for a two tells stacks sintered according to profile II. The ASR of the two cells stack is 2.50 Ω cm2. [Copyright &y& Elsevier]

The spatial characteristics of discontinuity traces play an important role in evaluations of the quality of rock masses. Most researchers have extracted discontinuity traces through the gray attributes of two-dimensional (2D) photo images or the geometric attributes of three-dimensional (3D) point clouds, while few researchers have paid attention to other important attributes of the original 3D point clouds, that is, the color attributes. By analyzing the color changes in a 3D point cloud, discontinuity traces in the smooth areas of a rock surface can be extracted, which cannot be obtained from the geometric attributes of the 3D point cloud. At the same time, a necessary filtering step has been designed to identify redundant shadow traces caused by sunlight on the rocks' surface, and a multiscale spatial local binary pattern (MS-LBP) algorithm was proposed to eliminate the influence of shadows. Next, the geometric attributes of the 3D point cloud were fused to extract the potential discontinuity trace points on the rocks' surface. For cases in which the potential discontinuity trace points are too scattered, a local line normalization thinning algorithm was proposed to refine the potential discontinuity trace points. Finally, an algorithm for establishing a two-way connection between a local vector buffer algorithm and a connectivity judgment algorithm was used to connect the discontinuity trace points to obtain the discontinuity traces of the rock mass's surface. In addition, three datasets were used to compare the results extracted by existing methods. The results showed that the proposed method can extract the discontinuity traces of rock masses with higher accuracy, thereby providing data support for evaluations of the quality of rock masses and stability analyses. [ABSTRACT FROM AUTHOR]

• CMC/PAM hydrogel electrolyte with admirable mechanical properties are obtained. • CMC/PAM hydrogel electrolyte can induce the deposition of zinc ions evenly on the zinc electrode surface. • Flexible ZIBs with CMC/PAM hydrogel electrolyte deliver a stable charge-discharge cycle performance. • Flexible ZIBs with CMC/PAM hydrogel electrolyte perform excellent bending resistance. In recent years, flexible devices have promoted extensive research on flexible aqueous Zn-ion batteries (ZIBs). Nevertheless, the dendrite growth on the anode and the poor mechanical properties of the flexible polymer electrolyte have become bottle-necks for the practical applications of flexible ZIBs. Herein, Carboxymethyl cellulose/polyacrylamide (CMC/PAM) hydrogel electrolyte has been constructed for flexible ZIBs. By introducing double-network structure, we dramatically improve the mechanical properties of the hydrogel electrolyte. The electrostatic interaction between carboxyl groups and Zn2+ can evenly induce the deposition of zinc ions on the zinc electrode surface, leading to remarkable reversibility and stability of zinc electrode. Flexible full ZIBs assembled with this electrolyte exhibit competitive electrochemistry performance with 90.18% capacity retention over 500 cycles at 0.308 mA cm−2. Furthermore, such ZIBs offer excellent bending resistance for enduring high mechanical impact. This study provides an effective strategy to address the zinc dendrite issue and reliability of flexible ZIBs. [Display omitted] [ABSTRACT FROM AUTHOR]

• The spectral data are pre-processed using three dimensionality reduction algorithms. • Optimize the parameters of the dimensionality reduction algorithm. • Establishment of a high-precision grade inversion model for Derni copper deposit. At present, chemical analysis is the main method for copper ore grade verification. Because of its long test cycle and lag effect relative to mining and ore blending processes, this approach cannot effectively reduce losses in mineral resource mining. An effective way to solve this problem is to conduct research on in situ grade determination methods for ore bodies based on visible-near-infrared hyperspectral grade modelling techniques. However, the accuracy of the modelling is seriously reduced by the large redundancy of hyperspectral data and the self-limiting nature of some machine learning methods. Therefore, the accurate selection of dimensionality reduction algorithms and machine learning algorithms determines the accuracy of the inverse model, and the setting of parameters such as dimensionality reduction, number of nearest neighbour points and number of hidden layers in neural networks is crucial. In this paper, 190 ore samples were collected from the open pit of the Derni copper deposit in Dawu town, Guoluo Tibetan Autonomous Prefecture, Qinghai Province, China. First, the visible-near-infrared spectral data for the sample set were obtained with an SVC HR-1024 spectrometer. The copper grade of the sample set was verified by chemical analysis. Three algorithms—the Laplacian eigenmap (LE), local tangent space alignment (LTSA) and local linear embedding (LLE)—were used to reduce the dimensionality of the original hyperspectral data. The original data and the three hyperspectral datasets after dimensionality reduction were used as data sources. Several Derni copper grade inversion models based on two machine learning algorithms, the backpropagation neural network and radial basis function (RBF) neural network, were developed. Finally, the coefficient of determination (R2), mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate the accuracy of each model. The results show that the accuracy of the model built with spectral data processed by the dimension reduction algorithm is higher than that of the model constructed with the original spectral data. Among the above combined models, the LLE-RBF model has the highest inversion accuracy. The MAE of the model is 0.117%, the RMSE is 0.136, and R2 is 0.934. [ABSTRACT FROM AUTHOR]

• Combination of multiple pre-processing methods to comprehensively process raw spectral data. • The advantages of this method are high accuracy. • A method for selecting the optimal principle of spectral index is proposed. With saline-alkali land in Zhenlai County, Baicheng city, Jilin Province, China as the research object, a quantitative inversion model for the heavy metal content of manganese (Mn), cobalt (Co) and iron (Fe) in saline-alkali soil and the visible-near-infrared spectroscopy data were established. First, Savitzky-Golay (SG) smoothing, multivariate scattering correction (MSC), continuum removal (CR), and combined transformation methods were performed on the original spectral data. By analysing the correlation between processed data and heavy metal content, the characteristic bands corresponding to different spectral transformations were extracted. Next, the ratio index (RI), difference index (DI) and normalized difference index (NDI) were constructed, and the spectral index was determined to have the most significant correlation with the combination of Mn-, Co- and Fe-content-sensitive bands and its corresponding Spearman rank correlation coefficient. Finally, a quantitative inversion model for the heavy metal content (Mn, Co and Fe) in soda saline-alkali land was established, and its accuracy was verified. The research results show that the optimal band selection principle for the quantitative inversion model of heavy metals in soda saline-alkali land is as follows: Mn selected 219 groups of sensitive bands with correlation coefficient r > 0.70, Co selected 1377 groups of sensitive bands with correlation coefficient r > 0.80, and Fe selected 104 groups of sensitive bands with correlation coefficient r > 0.80. Based on these selection principles, the random forest algorithm was used to perform an inversion for the Mn, Co and Fe contents with the best results, the goodness of fit (R2) values between predicted and measured values were 0.76, 0.92 and 0.91, and the mean relative accuracy was 91.75%, 92.45% and 93.90%, respectively. This method has improved prediction accuracy of the Mn, Co and Fe contents in saline-alkali land. [ABSTRACT FROM AUTHOR]

Spaceborne interferometric synthetic aperture radar (InSAR) techniques are important for landslide detection and monitoring; however, several limitations and uncertainties, such as the unique north–south flying direction and side-look radar observing geometry, currently limit the ability of InSAR to credibly detect landslides, especially those related to high and steep slopes. Here, we conducted experimental and statistical analysis on the feasibility of time-series InSAR monitoring for steep slopes using ascending and descending SAR images. First, the theoretical (TGNSS), practical (PGNSS), and terrain (Hterrain) (T-P-H) indices for sensitivity evaluations of the slope displacement monitoring results from time-series InSAR were proposed for slope monitoring. Subsequently, two experimental and statistical studies were conducted for the cases with and without Global Navigation Satellite System (GNSS) monitoring data. Our experimental results of two high and steep open-pit mines showed that the defined theoretical and practical sensitivity indices can quantitatively evaluate the feasibility of ascending and descending InSAR observations in steep-slope deformation monitoring with GNSS data, and the terrain sensitivity index can qualitatively evaluate the feasibility of landslide monitoring results from ascending and descending Sentinel-1 satellite data without GNSS data. We further demonstrate the generalizability of these proposed indices using four landslide cases with both public GNSS and InSAR monitoring data and 119 landslide cases with only InSAR monitoring data. The statistical results indicated that greater indices correlated with higher reliability of the monitoring results, suggesting that these novel indices have wide suitability and applicability. This study can help to improve the practice of slope deformation monitoring using spaceborne InSAR, especially for high and steep slopes. [ABSTRACT FROM AUTHOR]

Highlights • A coal proximate analysis method based on a combination of visible-infrared spectroscopy and deep neural networks. • This method can fate examines the moisture, ash, volatile matter, fixed carbon, sulphur and low heating value in coal. • Compared with traditional coal analysis, this method has unparalleled advantages and important value for practical applications in terms of efficiency, speed and accuracy. • We proposed a new CNN-AELM deep neural network algorithm. Abstract The proximate analysis of coal is the umbrella term for the six indexes that include the moisture, ash, volatile matter, fixed carbon, and sulphur contents and the heating value. Burning of coal creates carbon dioxide, sulphur dioxide and nitrogen dioxide which are main reasons causing air pollution. Therefore, before utilizing coal, it is indispensable to analyse coal. The traditional proximate analysis of coal mainly relies on chemical analysis, which is time-consuming and costly. Hence, a method to construct a coal analysis is introduced in this paper. By using the method to analyse moisture (%), ash (%), volatile matter (%), fixed carbon (%), and sulphur (%) contents and the low heating value (J/g). We first obtained different coal sample from different coal areas in China. Then, measured the spectral data through the spectral analysis instrument and extracted spectral features through a convolutional neural network. Finally, we applied the extreme learning machine algorithm to construct the prediction and analysis model of the spectral feature data. The experimental result shows that the model in the study can predict the components of coal. Compared with the chemical analysis method, this method has unparalleled advantages in terms of financial efficiency, speed and accuracy. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) is a rapid learning algorithm of the single-hidden-layer feedforward neural network, which randomly initializes the weights between the input layer and the hidden layer and the bias of hidden layer neurons and finally uses the least-squares method to calculate the weights between the hidden layer and the output layer. This paper proposes a multiple hidden layers ELM (MELM for short) which inherits the characteristics of parameters of the first hidden layer. The parameters of the remaining hidden layers are obtained by introducing a method (make the actual output zero error approach the expected hidden layer output). Based on the MELM algorithm, many experiments on regression and classification show that the MELM can achieve the satisfactory results based on average precision and good generalization performance compared to the two-hidden-layer ELM (TELM), the ELM, and some other multilayer ELM. [ABSTRACT FROM AUTHOR]

With the development of modernization, the application of seamless tube becomes widespread. As the first process of seamless tube, piercing is vital for the quality of the tube. The solid round billet will be transformed into a hollow shell after the piercing process. The defects of hollow shell cannot be cleared in the following process, so a monitoring model for the quality of the hollow shell is important. But the piercing process is very complicated, and a mechanism model is difficult to build between the qualities of the hollow shell and measurement variables. Furthermore, an intelligent model is needed. We established two piercing process monitoring and fault diagnosis models based on the multiway principal component analysis (MPCA) model and the multistage MPCA model, respectively, and furthermore we made a comparison between these two concepts. We took three ways to divide the period based on process, K-means, and GA, respectively. Simulation experiments have shown that the multistate MPCA method has advantage over the MPCA method and the model based on the genetic algorithm (GA) can monitor the process effectively and detect the faults. [ABSTRACT FROM AUTHOR]

Quantitative metal grade inversion based on hyperspectral data is an effective approach to achieve the real-time in situ determination of ore body grades and has the advantages of low cost compared with traditional chemical analysis methods. However, the redundant nature of hyperspectral data and the parameter-limiting nature of machine learning algorithms reduce the modeling accuracy and precision, resulting in severe limitations on the application of hyperspectral techniques for the grade inversion of Deerni copper ore bodies. In this paper, we first obtained visible-NIR hyperspectral data for 190 ore samples using a spectrometer and determined the copper content of the sample set using chemical analysis; then, we processed the raw hyperspectral data using three dimensionality reduction algorithms and optimized a BP neural network based on an evolutionary algorithm. Finally, a Deerni copper grade inversion model was established using the hyperspectral data before and after dimensionality reduction, and the inversion accuracy and precision was compared and analyzed with that obtained by the BP neural network, the random forest and the variable hidden layer nodes models. The combination of the LLE dimensionality reduction algorithm and the optimized BP neural network algorithm achieves the highest modeling precision, with an R2 of 0.950. [ABSTRACT FROM AUTHOR]

In this study, the probability integral method, Synthetic Aperture Radar Interferometry (InSAR), and the Okada dislocation model were collaboratively used to analyze deformation in the Alhada Pb-Zn mine. The predicted deformation values of the subsidence centers in three subsidence areas were 107 mm, 120 mm, and 83 mm, respectively, as predicted using the probability integral method. The coherent scatterer InSAR technique was used to analyze the time-series deformation of the mining area, and the same subsidence center locations and similar deformation values were observed. The Okada dislocation model was used to invert the optimal parameters of the underground-mining ore body causing the surface subsidence, indicating that the surface subsidence is mainly caused by the mining of ore bodies in the 888 and 848 middle sections. We further simulated ground deformation using the multi-source Okada model. The results showed that the predicted and modeled deformations are highly correlated with the observed deformation. Through the analysis and comparison of the InSAR results, it was concluded that the three subsidence areas do not threaten the stability of the main buildings in the mining area. Using theoretical, observational, and modeling methods, the development and evolution of the subsidence area in mines can be established, which could provide basic data for subsidence control work and guarantee mine production safety. [ABSTRACT FROM AUTHOR]

When an electromagnetic wave passes through the atmosphere, due to variation of atmospheric parameters, its propagation path is usually altered and the propagation speed also changes. Therefore, errors will be induced in triangulation measurement with electronic surveying robots, delimiting the accuracy of trigonometric leveling. Targeting at improving the accuracy of trigonometric leveling, a correction model based on atmospheric refraction coefficients is proposed in this article. First, based on the meteorological parameters collected on-site and Rüeger equation, influence of the meteorological parameters (e.g., temperature, atmospheric pressure, humidity, and CO2) on the refractive index are analyzed. Furthermore, gradient models of temperature, humidity, and atmospheric pressure with reference to height are established. In order to build the atmospheric gradient models of the optical path, height difference between the optical path and the ground is estimated with a precise digital elevation model generated by laser scanning. On this basis, the vertical atmospheric refraction coefficient model is proposed and used to correct the trigonometric leveling data measured by surveying robots. By conducting experimental studies over two open-pit iron mines with large topographic relief and comparing with first-class leveling data, reliability of the proposed atmospheric refraction coefficient model is assessed. The outcome of this article has demonstrated that by adopting the proposed model, accuracy of trigonometric leveling can meet the requirements of second-class leveling. [ABSTRACT FROM AUTHOR]

• THF as an electrolyte additive to improve the AZIB performance. • THF adsorbing on anode surface to regulate the crystal orientation of Zn metal. • THF inhibiting side reactions by destroying the hydrogen bond network in electrolyte. Aqueous zinc ion batteries (AZIB) are considered to be one of the most powerful substitutes for the next generation of energy storage devices due to their high theoretical specific capacity, high safety, and low cost. However, AZIB is limited by Zn dendrites and side reactions during cycling, resulting in low coulombic efficiency and poor cycle stability. In this study, 5 % tetrahydrofuran (THF) was introduced into 2 M ZnSO 4 electrolyte as an electrolyte additive to improve the AZIB performance. Differential capacitance fitting, theoretical calculation and XRD tests show that THF can adsorb on the surface of Zn anodes to regulate the crystal orientation of Zn metal. Raman and FTIR tests show that THF can also inhibit dendrite growth and side reactions by destroying the hydrogen bond network in electrolyte. Therefore, the THF leads to a stable cycle of over 5300 h at a discharge regime of 1 mA cm−2, 0.5 mAh cm−2 and over 950 h at a higher current density and area capacity of 2 mA cm−2, 2 mAh cm−2. Furthermore, the capacity retention rate of δ-MnO 2 ||Zn full battery reaches 93.63% after 1000 cycles under a current density of 1 A g−1, significantly higher than the 70.55% without THF addition. This strategy provides a simple and low-cost method to solve the common issues of AZIBs. [ABSTRACT FROM AUTHOR]

Rock landslides, one of the most common disasters in an open-pit mine, may occur at any point during the mining process. To manage landslide emergencies and define adequate preventive measures, a two-stage monitoring system was designed in this study to identify the potential landslide area and record multiple real-time data regarding an impending landslide. A case-based reasoning (CBR) approach was then designed to find similar rock landslide cases with detailed geological and monitoring information. The similar cases can provide workable engineering analogies for landslide prediction. Finally, an early warning system including slope stability analysis results, multifactor monitoring data, and early warning indicators obtained by CBR approach was established. Three warning levels were defined to support the system: ordinary "blue" level, attention "yellow" level, and alarm "red" level. A case study in the Dagushan open-pit mine indicates that the proposed system can provide ideas and solutions for rock landslide early warning in similar open-pit mines. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we report a dual-pore anode supported Sc2O3-stabilized-ZrO2 (ScSZ) electrolyte planar SOFC with the cell configuration of Ni-YSZ|Ni-ScSZ|ScSZ|LSM-ScSZ|LSM prepared by a combination of phase-inversion and dip-coating. The Ni-YSZ anode substrate is fabricated by phase-inversion, exhibiting an asymmetric dual-pore structure. A Ni-ScSZ anode functional layer (AFL) is introduced between the ScSZ electrolyte and the Ni-YSZ anode substrate by dip-coating, a process aims at increasing the three-phase boundaries at the anode/electrolyte interface. A single cell with this unique anode structure is successfully fabricated, demonstrating maximum power densities of 0.76, 1.04, 1.32 and 1.97 W cm−2 at 650, 700, 750, and 800 °C, respectively, with humidified (3vol% H2O) hydrogen as the fuel and oxygen as the oxidant. The microstructure morphologies of the SOFC are examined by SEM and comparisons of gas permeability for dual-pore and conventional anodes are also investigated. [Copyright &y& Elsevier]

Abstract: In this study, an anode-supported planar solid oxide fuel cell with a dual-pore anode is fabricated by the phase-inversion technique. The dual-pore anode with thin sponge-like porous layer and thick finger-like porous layer is fabricated in only one-step, and no pore formers are used in the fabrication process. Small sponge-like pores with fine microstructures offer expanding three-phase boundaries (TPBs) at the electrolyte/anode interface and long finger-like pores provide convenient channels for transporting the fuel gas to the anode reaction sites. Single cell with the dual-pore anode is successfully fabricated, demonstrating the maximum power densities of 0.52, 0.72, 0.87 and 1.15Wcm−2 at 650, 700, 750 and 800°C, respectively, with humidified (3% H2O) hydrogen as the fuel and stationary air as the oxidant. [Copyright &y& Elsevier]

Abstract: An anode-supported Sc2O3-stabilized-ZrO2 (ScSZ) electrolyte micro-tubular solid oxide fuel cell (MT-SOFC) with a cell configuration of Ni-YSZ|Ni-ScSZ|ScSZ|LSM-ScSZ|LSM has been prepared by a combination of phase-inversion and dip-coating technology. ScSZ is adopted as the electrolyte, decreasing the ohmic polarization of the cell. Porous Ni-YSZ micro-tubular anode substrate with a continuously transitional structure is prepared by the phase-inversion, and no pore former is used in the fabricating process. Ni-ScSZ is introduced between ScSZ electrolyte and Ni-YSZ micro-tubular anode substrate. LSM-ScSZ composite cathode was prepared by the bush-painting method. Single cell is successfully fabricated, exhibiting maximum power densities of 0.38, 0.54, 0.72 and 1.0Wcm−2 at 650, 700, 750 and 800°C, respectively, with humidified (3%H2O) hydrogen as fuel and stationary air as oxidant. [Copyright &y& Elsevier]

Abstract: A zeolite Y coating has been formed on aluminum alloy 2024-T3 (Al 2024-T3) by a novel three-step synthesis method. In the first step, the bottom layer is formed by synthesizing a high-silica-zeolite (HSZ) ZSM-5 film on the Al 2024-T3 substrate. In the second step, the ZSM-5 film is seeded with zeolite Y followed by a short ZSM-5 synthesis to form the bridging layer. Finally, the low-silica-zeolite (LSZ) Y film is synthesized on the bridging layer by seeded growth to form the top layer. The ZSM-5 film offers excellent corrosion protection for the aluminum under the high pH conditions required for LSZ Y synthesis. The bridging layer is critical in providing adhesion between the bottom and top layers. [Copyright &y& Elsevier]

Improving the density of Na3Zr2Si2PO12 (NZSP) solid electrolyte is crucial for its application in solid-state sodium batteries. Here, bismuth oxide with low melting point has been evaluated as a sintering aid for NZSP solid electrolytes. Research has found that NZSP doped with 5 mass% Bi2O3 increases grain boundary mobility by 3.1 times and has an ionic conductivity of 4.78 × 10−3 S cm−1, with a minimum activation energy of 0.30 eV, approximately 5 times that of undoped Bi2O3. This is because the electrolyte density increased from 77.3 to 93.7%. Compared with the region III process of NZSP without Bi2O3, Bi2O3 promotes the densification process of Bi2O3 dopant coarsening in region III. The constant current cycle of Na | NZSP-5 mass% Bi2O3 | Na symmetric battery indicates that no short circuit will occur within 1200 h. The discharge capacity of solid-state batteries at 0.2 C is 98.5 mAh g−1. This indicates that NZSP doped with 5% Bi2O3 is a good electrolyte choice for sodium-ion batteries. [ABSTRACT FROM AUTHOR]

Copper is an important national resource, which is widely used in various sectors of the national economy. The traditional detection of copper content in copper ore has the disadvantages of being time-consuming and high cost. Due to the many drawbacks of traditional detection methods, this paper proposes a new method for detecting copper content in copper ore, that is, through the spectral information of copper ore content detection method. First of all, we use chemical methods to analyze the copper content in a batch of copper ores, and accurately obtain the copper content in those ores. Then we do spectrometric tests on this batch of copper ore, and get accurate spectral data of copper ore. Based on the data obtained, we propose a new two hidden layer extreme learning machine algorithm with variable hidden layer nodes and use the regularization standard to constrain the extreme learning machine. Finally, the prediction model of copper content in copper ore is established by using the algorithm. Experiments show that this method of detecting copper ore content using spectral information is completely feasible, and the algorithm proposed in this paper can detect the copper content in copper ores faster and more accurately. [ABSTRACT FROM AUTHOR]

Aqueous zinc‐ion batteries (ZIBs) are an alternative energy storage system for large‐scale grid applications compared with lithium‐ion batteries, when the low cost, safety, and durability are taken into consideration. However, the reliability of the battery systems always suffers from the serious challenge of the large Zn dendrite formation and "dead Zn," thus bringing out the inferior cycling stability, and even cell shorting. Herein, a dendrite‐free organic anode, perylene‐3,4,9,10‐tetracarboxylic diimide (PTCDI) polymerized on the surface of reduced graphene oxide (PTCDI/rGO) utilized in ZIBs is reported. Moreover, the theoretical calculations prove the reason for the low redox potential. Due to the protons and zinc ions coparticipant phase transfer mechanism and the high charge transfer capability, the PTCDI/rGO electrode provides superior rate capability (121 mA h g−1 at 5000 mA g−1, retaining the 95% capacity of that compared with 50 mA g−1) and a long cycling life span (96% capacity retention after 1500 cycles at 3000 mA g−1). In addition, the proton coparticipation energy storage mechanism of active materials is elucidated by various ex‐situ methods. [ABSTRACT FROM AUTHOR]

The extensive explorations of potential cathode materials are prominently critical for the rapid development of high performance solid oxide fuel cells (SOFCs). Herein, we develop a novel Nb and Cu co-doped SrCoO 3-δ (SCNC) cathode base on solid state reaction, which exhibits decent compatibility with gadolinium doped cerium oxide (GDC) electrolyte. The SCNC is successfully stabilized with cubic structure at room temperature when incorporating of small amount of high valence Nb5+. Meanwhile, the oxygen vacancy concentration of SCNC is efficiently improved with the addition of Cu. The Nb and Cu co-doping also substantially promotes the electronic conductivity, achieving 550 S cm−1 for the optical doped SrCo 0.85 Nb 0.05 Cu 0.10 O 3-δ (SCNC10) at 400 °C. In addition, the polarization of SCNC is remarkably reduced, reaching as low as 0.021 Ω cm2 for SCNC10 at 700 °C. The activation energy for reaction is also significantly lowered to 0.78 eV. The reaction order m is deduced to be about 0.30, implying that the rate determination step for SCNC10 is the charge transfer reaction. The peak power density of the single cell reaches 780 mW cm−2 at 800 °C. All these outstanding performances demonstrate that SCNC is a promising cathode for SOFCs when operating at intermediate temperature (IT). (a)The EIS of SCNC10 at different oxygen pressure at 700 °C; (b)the relationship of lnRp to ln (Po 2) for SCNC10 measured at 700 °C. The reaction order m is about 0.30, suggesting that the RDS for SCNC10 is probably to be the charge transfer reaction. Image 1 • A novel Nb and Cu co-doped SrCoO 3-δ improved its oxygen vacancy concentration. • The ASR for SCNC10 was only 0.021 Ω cm−2 at 700 °C, with the activation for reaction is only 0.78 eV. • The reaction orders suggest that the rate determine step is probably to be the charge transfer reaction. [ABSTRACT FROM AUTHOR]

A self‐standing hybrid film was fabricated, which consists of SnO2 nanotubes sandwiched by MXene nanosheets through alternate vacuum filtration. As an anode for lithium ion batteries (LIBs), the self‐standing hybrid film delivers a reversible capacity of 663 mAh g−1 after 100 cycles at 100mAg−1 and exhibits excellent rate capability with a reversible capacity of 327mAhg−1 at 5Ag−1. The superior cycle stability and rate performance are achieved through the synergetic effect between SnO2 nanotubes and MXene nanosheets, which provides a reference for the development of flexible batteries. [ABSTRACT FROM AUTHOR]

Derivates of metal-organic frameworks are promising materials of self-supported Li ion battery anodes due to the good dispersion of active materials, conductive scaffold, and mass transport channels in them. However, the discontinuous growth and poor adherence of metal-organic framework films on substrates hamper their development in self-supported electrodes. In the present study, cobalt-based metal-organic frameworks are anchored on Ti nanowire arrays through an electrochemically assistant method, and then the metal-organic framework films are pyrolyzed to carbon-containing, porous, self-supported anodes of Li ion battery anodes. Scanning electron microscope images indicate that, a layer cobaltosic oxide polyhedrons inserted by the nanowires are obtained with the controllable in-situ synthesis. Thanks to the good dispersion and adherence of cobaltosic oxide polyhedrons on Ti substrates, the self-supported anodes exhibit remarkable rate capability and durability. They possess a capacity of 300 mAh g −1 at a rate current of 20 A g −1 , and maintain 2000 charge/discharge cycles without obvious decay. [ABSTRACT FROM AUTHOR]

Porous Co 3 O 4 films derived from Co-based metal-organic framework (MOF) are anchored by Ti nanowire arrays to form self-supported electrodes, which realize remarkable rate capability as Li ion battery (LIB) anodes owing to the homogeneous distribution of active grains, conductive scaffold and mass transport channels in them. The MOF films comprising Co 3 (HCOO) 6 blocks are anchored on Ti nanowire arrays through a hydrothermal process, then the films are pyrolyzed to porous Co 3 O 4 /C films. Scanning electron microscope images reveal the blocks are clenched firmly on Ti substrates by Ti nanowires inserting into them. The LIB battery tests indicate that, thanks to the good adherence, Co 3 O 4 films present outstanding durability (maintaining 2000 cycles at 20 A g −1 without obvious decay). Furthermore, the carbon scaffold generated from the pyrolysis in inert gas is crucial to the rate capability of Co 3 O 4 self-supported anodes. The homogeneous distribution of carbon in Co 3 O 4 films leads to facilitated electron transport, which can help the films releasing their capacitive potential adequately. This enables the films possessing a capacity of 400 mAh g −1 at 20 A g −1 , much better than the films from pyrolysis in air (100 mAh g −1 at 20 A g −1 ). [ABSTRACT FROM AUTHOR]

[Display omitted] • A method for rapid and accurate detection of soil organic matter content. • The method is based on reflectance spectroscopy and machine learning. • Quality band combinations are obtained by spectral pre-processing methods. • Genetic strategy is introduced into the model to enhance its exploration capability. • We propose an improved hybrid extreme learning machine model. The continuous exploitation of mining resources and a number of other factors have led to serious soil pollution problems. Soil organic matter (SOM) can be used as an important measure of soil fertility and ecological environment quality in mining areas. The traditional method of measuring SOM content by chemical methods is costly and time-consuming, so it is important to develop a rapid, accurate and economical method to measure SOM content in reclaimed mines for the ecological restoration and the evaluation of the ecological reclamation effect of mines. In this paper, the visible-near infrared spectral data of soil were obtained using an open pit mine as the study area. The three pre-processing methods of multiple scattering correction, continuous removal and Savitzky-Golay smoothing were compared. Then the two-band ratio spectral index RI was constructed to obtain the sensitive band combinations. To address the problem that the HHO-TELM hybrid model tended to converge prematurely, the GHO-TELM model was proposed in this paper. GHO-TELM introduced a gene learning strategy in HHO-TELM. The crossover, mutation and selection mechanisms were used to cultivate high-quality examples, enhance the quality of individuals and prevent premature convergence of the model. GHO-TELM was compared with several machine learning models. The experimental results showed that GHO-TELM obtained the best performance indexes, with correlation coefficient (r) of 0.9586, and relative percentage deviation (RPD) of 3.0823, indicating that the model had a good prediction effect. [ABSTRACT FROM AUTHOR]

• Pretreatment combined algorithms combining multiple mathematical transformations and dimensionality reduction algorithms to comprehensively process raw spectral data. • The advantage of this method is high accuracy". • A method for selecting the optimal Pretreatment combined algorithm is proposed and can accurately predict the accuracy of skarn-type iron ore. Iron ore grade determination is a key step in mining production, and its efficiency has a significant impact on mining efficiency and profitability. At present, the most commonly used method for ore grade determination is chemical analysis, which not only is expensive but also has a long assay cycle and lag effect relative to the ore allocation process and cannot effectively reduce the loss depletion rate of ore mining. Therefore, in situ iron ore grade data determination technology based on visible and near-infrared hyperspectral data analysis is an effective way to solve the above problems, but the poor quality of raw hyperspectral data and low accuracy of the ore grade inversion model are still one of the bottlenecks in the application of this technology. In this paper, 225 ore samples were collected from Hongling skarn-type iron ore in Chifeng, Inner Mongolia Autonomous Region, China. The visible and near-infrared hyperspectral data of the sample set and the grade data obtained from chemical analysis were used as data sources. The raw spectral data were first smoothed, and then the smoothed spectral data were processed using two algorithms, continuum removal (CR) and standard normal variate transform (SNV). Then, two dimensionality reduction algorithms, principal component analysis (PCA) and genetic algorithm (GA), were used to reduce the dimensionality of the spectral data before and after preprocessing, and the data sources were obtained after the four different preprocessing combination algorithms were processed. Finally, several quantitative inversion models were established based on the random forest algorithm and the backpropagation neural network (BPNN) algorithm. The stability, accuracy and credibility of the models were evaluated by four metrics: the coefficient of determination (R2), root mean square error (RMSE), residual prediction deviation(RPD) and mean relative error (MRE). The results showed that the quantitative inversion model (SNV-PCA-BPNN) established based on the BP neural network algorithm using the data processed by SNV and PCA for dimensionality reduction was the best, with an R2 of 0.99, an RMSE of 0.0048, an MRE of 1.10% and a RPD of 37.70, providing an effective method for the high-precision inversion of iron ore grades. [ABSTRACT FROM AUTHOR]

Geological disasters are responsible for the loss of human lives and for significant economic and financial damage every year. Considering that these disasters may occur anywhere—both in remote and/or in highly populated areas—and anytime, continuously monitoring areas known to be more prone to geohazards can help to determine preventive or alert actions to safeguard human life, property and businesses. Remote sensing technology—especially satellite-based—can be of help due to its high spatial and temporal coverage. Indeed, data acquired from the most recent satellite missions is considered suitable for a detailed reconstruction of past events but also to continuously monitor sensitive areas on the lookout for potential geohazards. This work aims to apply different techniques and methods for extensive exploitation and analysis of remote sensing data, with special emphasis given to landslide hazard, risk management and disaster prevention. Multi-temporal SAR (Synthetic Aperture Radar) interferometry, SAR tomography, high-resolution image matching and data modelling are used to map out landslides and other geohazards and to also monitor possible hazardous geological activity, addressing different study areas: (i) surface deformation of mountain slopes and glaciers; (ii) land surface displacement; and (iii) subsidence, landslides and ground fissure. Results from both the processing and analysis of a dataset of earth observation (EO) multi-source data support the conclusion that geohazards can be identified, studied and monitored in an effective way using new techniques applied to multi-source EO data. As future work, the aim is threefold: extend this study to sensitive areas located in different countries; monitor structures that have strategic, cultural and/or economical relevance; and resort to artificial intelligence (AI) techniques to be able to analyse the huge amount of data generated by satellite missions and extract useful information in due course. [ABSTRACT FROM AUTHOR]

Abstract: Effective sealant for planar solid oxide fuel cell is one technical hurdle for its application. In this article, Ag–CuO braze, which conducted directly at 950°C in air, has been investigated as a sealant candidate between the SS430 interconnect and the Ni/YSZ anode for the planar solid oxide fuel cells. The results for leakage rates indicate that the lowest leakage is as low as 0.001sccmcm−1. SEM images demonstrates that both the pure Ag and 6mol% CuO in the braze integrates the anode inherently. However pure Ag wets poorly on the SS430 interconnect surface but 6mol% CuO braze forms a reaction zone, upon which Ag wets well, so the hermeticity has been improved significantly. Even after experiencing 20 thermal cycles, the leakage rate is still as low as 0.002sccmcm−1. No significant effects of thermal cycles on the interfacial microstructure of the sealant were observed. These shows that Ag–CuO braze is a promising candidate for SOFC sealant. [Copyright &y& Elsevier]

To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research object. The original spectral data were first subjected to Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC) pre-processing, and a combined transformation technique. The pre-processed spectral data were then analysed to construct the difference index (DI), ratio index (RI), and normalised difference index (NDI), and the Spearman rank correlation coefficient (r) between these three spectral indices and the salt content in the samples was calculated, while a combined spectral index (r > 0.8) was eventually selected as a sensitive spectral index. Finally, a quantitative inversion model for the salinity of saline soils was developed, and the model's accuracy was evaluated based on partial least squares regression (PLSR), the random forest (RF) algorithm, and the radial basis function (RBF) neural network algorithm. The results indicated that the inversion of soil salt content using the selected combination of spectral indices based on the RBF neural network algorithm was the most effective, with the prediction model yielding an R2 value of 0.950, a root mean square error (RMSE) of 1.014, and a relative percentage deviation (RPD) of 4.479, which suggested a good prediction effect. [ABSTRACT FROM AUTHOR]

Abstract: During ion exchange under atmospheric conditions, zeolites A, X, and Y facilitate the oxidation of Fe2+ to Fe3+ to very different extents, ranging from complete oxidation of the ferrous cations to ferric species to almost complete preservation of the ferrous state. Drying the exchanged zeolites at elevated temperature favors the oxidation reaction. The chemical environments of the low-silica zeolites A and X seem to promote the oxidation reaction more strongly than that of the more siliceous zeolite Y. Iron-rich surface layers are formed on the external surface of the zeolite particles, resulting in an apparent over-exchange especially for the low-silica zeolites. The structural integrity of the zeolite samples is differently affected by the exchange in the weakly acidic exchange solutions. The observed structural damage ranges from an almost unaffected framework, as observed for zeolite Y after one exchange, to structural collapse, as observed for zeolite X after repeated exchanges. [Copyright &y& Elsevier]

Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocessing and modeling methods of hyperspectral images obtained from an unmanned aerial vehicle (UAV) platform for estimating the soil organic matter (SOM) and soil total nitrogen (STN) in farmland. The results showed that: (1) Multiplicative Scattering Correction (MSC) performed better in reducing image scattering noise than Standard Normal Variate (SNV) transformation or spectral derivatives, and it yielded a result with higher correlation and lower signal-to-noise ratio; (2) The proposed feature selection method combining Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling algorithm (CARS), could provide selective preference for hyperspectral bands. Exploiting this method, 24 and 22 feature bands were selected for SOM and STN estimation, respectively; (3) The particle swarm optimization (PSO) algorithm was employed to obtain optimized input weights and bias values of the extreme learning machine (ELM) model for more accurate prediction of SOM and STN. The improved PSO-ELM model based on the selected preference bands achieved higher prediction accuracy (R2 of 0.73 and RPD of 1.91 for SOM, R2 of 0.63, and RPD of 1.53 for STN) than support vector machine (SVM), partial least squares regression (PLSR), and the ELM model. This study provides an important guideline for monitoring soil nutrient for precision agriculture with imaging spectrometry. [ABSTRACT FROM AUTHOR]

Due to the intrinsic side-looking geometry of synthetic aperture radar (SAR), time series interferometric SAR is only able to monitor displacements in line-of-sight (LOS) direction, which limits the accuracy of displacement measurement in landslide monitoring. This is because the LOS displacement is only a three dimensional projection of real displacement of a certain ground object. Targeting at this problem, a precise digital elevation model (DEM) assisted slope displacement retrieval method is proposed and applied to a case study over the high and steep slope of the Dagushan open pit mine. In the case study, the precise DEM generated by laser scanning is first used to minimize topographic residuals in small baseline subsets analysis. Then, the LOS displacements are converted to slope direction with assistance of the precise DEM. By comparing with ground measurements, relative root mean square errors (RMSE) of the estimated slope displacements reach approximately 12–13% for the ascending orbit, and 5.4–9.2% for the descending orbit in our study area. In order to validate the experimental results, comparison with microseism monitoring results is also conducted. Moreover, both results have found that the largest slope displacements occur on the slope part, with elevations varying from −138 m to −210 m, which corresponds to the landslide area. Moreover, there is a certain correlation with precipitation, as revealed by the displacement time series. The outcome of this article shows that rock mass structure, lithology, and precipitation are main factors affecting the stability of high and steep mining slopes. [ABSTRACT FROM AUTHOR]

• An opencast coal mine monitoring method based on multispectral remote sensing images and field reflectance data. • The advantages of this monitoring method are high speed, high accuracy and low price. • We proposed a new tree root algorithm. • We proposed a tree root extreme learning machine neural network. Coal is currently the main energy in the world. A large amount of gangue is produced during the coal mining process, and the gangue contains a large amount of metal and sulfide, which is one of the environmental pollution sources. Therefore, how to monitor changes in coal mines in real time is an important task. With regard to this problem, we propose a new method of monitoring coal mining area. First, we collect samples of coal, gangue, and other objects from the Shenhua Baori Xile coal mine, and then measure the ground measured spectral reflectance data of these samples. For satellite data, we remove the interference information of the reflectance spectra from these images and established a relationship with the ground measured data. We propose a tree root algorithm and then combine extreme learning machine to build a classification model. Finally, the model is applied to remote sensing images, and a good extraction effect is obtained. Compared with typical monitoring methods, our method has the superiorities of high precision, low-priced, and fast speed, and it can monitor the changes in the coal mine area in real time. This is the basis for improving the environmental pollution around the coal mining area. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning (TLS) techniques have been widely used in open-pit mine applications. It is a crucial task to measure the exploitative volume of open-pit mines, within a specific time interval. One major challenge is posed, however, when conducting accurate registrations for temporal TLS surveys in continuously changing areas, created by excavation activities. In this paper, we propose a coarse-to-fine registration method, based on terrain-invariant regions (TIR), for temporal TLS surveys. More specifically, an approximate four-point congruent set (4PCS) of temporal TLS surveys is first identified, based on affine invariant rules. Second, a set of correspondences among temporal TLS surveys were collected by matching multi-scale sparse features of the 3D neighbors, centered at the approximate 4PCS. Third, the correspondences were used to estimate a rigid motion between the overlapping TLS surveys for the coarse registration, according to which the initial TIR from temporal TLS surveys were identified. Finally, the rigid motion between temporal TLS was iteratively optimized, based on the point clouds, only from the TIR. Based on the fine-level registered TLS surveys, Digital Elevation Models (DEMs) can be generated to calculate the exploitative volume, through a DEM differential. We applied the proposed method to two open-pit mines in China, and also compared our method with five state-of-the-art methods for registering temporal TLS surveys. Experimental results indicated that the proposed method achieved a higher registration accuracy than the state-of-the-art methods. Based on the registered result, our method achieved a 98.03% overall accuracy for measuring the exploitative volume, compared to in-situ measurement. [ABSTRACT FROM AUTHOR]

The mining waste of open pit mines is usually piled-up in dump sites, making a man-made hill more than tens of meters high. Because of the loose structure of the dump sites, landslides or debris flow may occur after heavy rainfall, threatening local lives and properties. Therefore, dump stability analysis is crucial for ensuring local safety. In this paper, a collaborative stability analysis based on multiple remote sensing technologies was innovatively conducted at the Xudonggou dump of the Anqian iron mine. A small baseline subset (SBAS) analysis was used to derive the spatial and temporal distributions of displacements in the line-of sight (LOS) over the whole study area. The deformation in LOS is translated to the slope direction based on an assumption that displacements only occur parallel to the slope surface. Infrared Thermography (IRT) technology was used to detect weak aquifer layers located at the toe of possible landslide bodies. Then, numerical simulations based on the limit equilibrium method were conducted to calculate the factor of safety for three profiles located on the dump site. The results, emerging from multiple remote sensing technologies, were very consistent and, eventually, the landslide hazard zone of the Xudonggou dump site was outlined. [ABSTRACT FROM AUTHOR]