Search Results (15)

As the demand for deep mineral resource extraction intensifies, optimizing pipeline transportation systems in backfill mining has become increasingly critical. Thus, reducing energy loss while ensuring the filling effect becomes crucial for improving process efficiency. Owing to variations among mines, accurately calculating pipeline resistance loss remains challenging, resulting in significant inaccuracies. The rapid development of Industry 4.0 provides intelligent and data-driven optimization ideas for this challenge. This study introduces a novel pipeline resistance loss prediction framework integrating generative artificial intelligence with a TransKAN model. This study employs generative artificial intelligence to produce physically constrained augmented data, integrates the KAN network’s B-spline basis functions for nonlinear feature extraction, and incorporates the Transformer architecture to capture spatio-temporal correlations in pipeline pressure sequences, enabling precise resistance loss calculation. The experimental data collected from pipeline pressure sensors provides empirical validation for the model. Compared with traditional mathematical formulas, BP neural networks, SVMs, and random forests, the proposed model demonstrates superior performance, achieving an R² value of 0.9644, an RMSE of 0.7126, and an MAE of 0.4703. Full article

A novel artificial intelligence (AI) application was proposed in the current study to predict CTF’s compressive strength (CS). The database contained six input parameters: the age of curing for specimens (AS), cement–sand ratio (C/S), maintenance temperature (T), additives (EA), additive type (AT), additive concentration (AC), and one output parameter: CS. Then, adaptive boosting (AdaBoost) was applied to existing AI and soft computing techniques, using AdaBoost, random forest (RF), SVM, and ANN. Data were arbitrarily separated into training (70%) and test (30%) sets. Results confirm that AdaBoost and RF have the best prediction accuracy, with a correlation coefficient (R²) of 0.957 between these sets for AdaBoost. Using Python 3.9 (64-bit), IDLE (Python 3.9 64-bit), and PyQt5 to achieve the machine learning model computation and software function interface development, the application of this software can quickly predict the strength property of CTF specimens, which saves time and costs efficiently for backfill researchers and developing new eco-efficient components. Full article

Addressing the resource constraints, such as manpower and equipment, faced by mine backfilling operations, this study proposed an optimization model for backfill scheduling based on the Resource-Constrained Project Scheduling Problem (RCPSP). The model considered backfilling’s multi-process, multi-task, and multi-resource characteristics, aiming to minimize total delay time. Constraints included operational limits, resource requirements, and availability. The goal was to determine optimal resource configurations for each stope’s backfilling steps. A heuristic genetic algorithm (GA) was employed for solution. To handle equipment unavailability, a new encoding/decoding algorithm ensured resource availability and continuous operations. Case verification using real mine data highlights the advantages of the model, showing a 20.6% decrease in completion time, an 8 percentage point improvement in resource utilization, and a 47.4% reduction in overall backfilling delay time compared to traditional methods. This work provides a reference for backfilling scheduling in similar mines and promotes intelligent mining practices. Full article

With the continuous expansion of the application range of gob–side entry retaining technology, the depth, height, and advancing speed of coal seams also increase, which brings great problems to the stability control of surrounding rock structures of gob–side entry retaining. As one of the main bearing structures of the surrounding rock, the stability of the roadway–side support body is a key factor for the success of gob–side entry retaining. In order to study the deformation characteristics and instability mechanism of roadway-side support body, based on the roadway–side support materials of gob-side entry retaining, the dynamic expansion test of back–filling concrete cracks under uniaxial compression was carried out. The YOLOv5 algorithm was applied to establish the fine identification and quantitative characterization method of macroscopic cracks of the samples, and the dynamic expansion rule of roadway-side support body cracks and its dimensional effect were revealed by combining the fractal theory. The results show that the F1 value and average precision mean of the intelligent dynamic crack identification model reached 75% and 71%, respectively, the GIoU loss value tends to fit around 0.038, and the model reached the overall optimal solution. During the uniaxial compression process, micro cracks on the surface of the back–filling concrete first initiated at the end, and after reaching the yield stress, the macroscopic cracks developed significantly. Moreover, several secondary cracks expanded, pooled, and connected from the middle of the specimen to the two ends, inducing the overall instability of the specimen. The surface crack expansion rate, density, and fractal dimension all show stage change characteristics with the increase in stress, and the main crack expansion rate has obvious precursor characteristics. With the increase in the size, the decrease in crack density after back–filling concrete failures gradually decreases from 93.19% to 4.08%, the surface crack network develops from complex to simple, and the failure mode transits from tensile failure to shear failure. The above research results provide a basic experimental basis for design optimization and instability prediction of a roadway–side support body for engineering-scale applications. Full article

In coal mining, solid backfilling technology is widely used. However, its efficiency is seriously hindered by the following two factors. Firstly, the process flow of the solid backfilling operation is more complicated in the back, and the spatiotemporal linkage (SPL) between actions of the cylinders powering each support and between hydraulic supports in the whole face lacks continuity. Secondly, the coal mining process in the front has a higher level of intelligence and technical maturity than the backfilling operation in the back, the latter permanently staying behind the former. To this end, the present study investigates the SPL of the mining and backfilling operations for single supports in the working and whole faces. The SPL of cylinder actions is analyzed for intelligent backfilling using hydraulic supports. We also investigate the SPL of the positions of each piece of key equipment involved in different steps of intelligent backfilling in the whole face. Formulas are derived for calculating the time required to complete the cyclic hydraulic support movement–discharge–filling operation for single supports and the whole face. The key factors influencing the time required to complete a hydraulic support movement–discharge–filling cycle are analyzed. On this basis, a backfilling efficiency optimization scheme is proposed. It envisages reducing the number of tampings and time gaps in actions of single supports and cylinders, increasing the number of hydraulic supports in parallel operation, and intelligent upgrading of the backfilling operation. These findings help synchronize coal mining and backfilling operations. Full article

This paper analyzes the typical technical problems arising from dumping and tamping collision interferences in the working faces of conventional mechanized solid backfilling mining (SBM). Additionally, the technical and consecutive characteristics of the solid intelligent backfilling (SIB) method, the execution device, and the corresponding process categories of the SIB process are analyzed. A design for an SIB process flow is presented. Critical algorithms, including automatic recognition and optimization planning based on the cost function and laying the algorithm foundation, are proposed to develop a backfilling process control system. A joint simulation test system is built on a MATLAB/Simulink simulation toolkit (MSST) to simulate and test the optimized algorithms. The results show that the optimized algorithm can realize the automatic optimization planning and automatic interference-recognition adjustment of the backfilling process under actual engineering conditions. In conclusion, this paper analyzes typical technical problems in the conventional backfilling process, designs the SIB process flow, and develops key algorithms to achieve the automatic control of the backfilling process. Full article

Coal mine solid waste backfill is a coal mining method employed to safeguard subterranean and surface geological formations, as well as water resources, against impairment. It stands as a pivotal technical approach for realizing ecologically sustainable mining endeavors, aiming to address China’s predicament of ’three down’ coal pressure, coal gangue emissions, and land resource scarcity. This manuscript delves into an in-depth exploration of the evolution and research status pertaining to solid backfill technology, encompassing backfill materials, rock mechanics, backfill processes, and their application across China’s coal sector. The developmental challenges and technical intricacies linked to solid backfill technology within coal mines are meticulously scrutinized. Building upon these challenges and complexities, this study sets forth a progressive trajectory for solid backfill technology within the contemporary era. This trajectory envisions the synchronized advancement of novel solid backfill materials, intelligent surveillance and regulation methodologies, and machine learning technologies for backfill quality assessment. By doing so, the overarching aim of achieving superlative quality, heightened efficiency, and automation in solid backfill practices can be effectively realized. Full article

The utilization of solid waste for filling mining presents substantial economic and environmental advantages, making it the primary focus of current filling mining technology development. To enhance the mechanical properties of superfine tailings cemented paste backfill (SCPB), this study conducted response surface methodology experiments to investigate the impact of various factors on the strength of SCPB, including the composite cementitious material, consisting of cement and slag powder, and the tailings’ grain size. Additionally, various microanalysis techniques were used to investigate the microstructure of SCPB and the development mechanisms of its hydration products. Furthermore, machine learning was utilized to predict the strength of SCPB under multi-factor effects. The findings reveal that the combined effect of slag powder dosage and slurry mass fraction has the most significant influence on strength, while the coupling effect of slurry mass fraction and underflow productivity has the lowest impact on strength. Moreover, SCPB with 20% slag powder has the highest amount of hydration products and the most complete structure. When compared to other commonly used prediction models, the long-short term memory neural network (LSTM) constructed in this study had the highest prediction accuracy for SCPB strength under multi-factor conditions, with root mean square error (RMSE), correlation coefficient (R), and variance account for (VAF) reaching 0.1396, 0.9131, and 81.8747, respectively. By optimizing the LSTM using the sparrow search algorithm (SSA), the RMSE, R, and VAF improved by 88.6%, 9.4%, and 21.9%, respectively. The research results can provide guidance for the efficient filling of superfine tailings. Full article

The stability of high backfill slopes emerges in practice due to the expansion of transportation infrastructures. The seepage and infiltration of rainfall into the backfills brings challenges to engineers in predicting the stability of the slope, weakening the shear strength and modulus of the soil. This study carried out a series of model tests under a plane strain condition to investigate the stability of a high backfill slope moisturized by rainfalls, considering the influences of rainfall duration and intensity. The slope displacements were monitored by a laser displacement sensor and the moisture content in the backfill mass were obtained by a soil moisture sensor. The test results show that increasing the rainfall intensity and duration caused the slope near the surface to be saturated, resulting in significant influences on the lateral displacement of the slope and the reduction of stability as well as the sizes of the sliding mass. Based on the model tests, the numerical analysis was adopted to extend the analysis cases, and the backpropagation (BP) neural network model was further adopted to build a model for predicting the stability of a high backfill slope under rainfall. The trained BP model shows the average relative error of 1.02% and the goodness of fitness of 0.999, indicating a good prediction effect. Full article

The technology known as cemented paste backfill (CPB) has gained considerable popularity worldwide. Yield stress (YS) is a significant factor considered in the assessment of CPB’s flowability or transportability. The minimal shear stress necessary to start the flow is known as Yield stress (YS), and it serves as an excellent measure of the strength of the particle-particle interaction. The traditional evaluation and measurement of YS performed by experimental tests are time-consuming and costly, which induces delays in construction projects. Moreover, the YS of CPB depends on numerous factors such as cement/tailing ratio, solid content and oxide content of tailing. Therefore, in order to simplify YS estimation and evaluation, the Artificial Intelligence (AI) approaches including eight Machine Learning techniques such as the Extreme Gradient Boosting algorithm, Gradient Boosting algorithm, Random Forest algorithm, Decision Trees, K-Nearest Neighbor, Support Vector Machine, Multivariate Adaptive Regression Splines and Gaussian Process are used to build the soft-computing model in predicting the YS of CPB. The performance of these models is evaluated by three metrics coefficient of determination (R²), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The 3 best models were found to predict the Yield Stress of CPB (Gradient Boosting (GB), Extreme Gradient Boosting (XGB) and Random Forest (RF), respectively) with the 3 metrics of the three models, respectively, GB {R² = 0.9811, RMSE = 0.1327 MPa, MAE = 0.0896 MPa}, XGB {R² = 0.9034, RMSE = 0.3004 MPa, MAE = 0.1696 MPa} and RF {R² = 0.8534, RMSE = 0.3700 MPa, MAE = 0.1786 MPa}, for the testing dataset. Based on the best performance model including GB, XG and RF, the other AI techniques such as SHapley Additive exPlanations (SHAP), Permutation Importance, and Individual Conditional Expectation (ICE) are also used for evaluating the factor effect on the YS of CPB. The results of this investigation can help the engineers to accelerate the mixed design of CPB. Full article

In China, cemented paste backfill (CPB) is a common treatment method after the exploitation of basic energy. The homogeneity of slurry influences the performance of CPB. However, the online monitoring and characterization of homogeneity lack relevant technologies and unified standards. This article discusses an online image analysis technique applied to the online monitoring of cemented paste backfill mixing, which is based on the evolution of the texture of images taken at the surface of the mixing bed. First, the grayscale distribution of the image obtained by the high-speed camera in the CPB preparation process was analyzed by Matlab and its variance (s²) was solved, and the texture features of the image were analyzed by the variance of grayscale distribution. Then, a homogeneity discriminant model ($c_{st}$) was established. The results show that the variance value of the grayscale distribution of the slurry image increases rapidly at first, then gradually decreases, and becomes stable in the final stage since it turns a constant value. When the s² value tends to be stable, the slurry gradually reaches homogenization, and the discriminant coefficient of paste homogenization based on the homogenization discriminant model reaches 0.05. The homogenization prediction of CPB proves to be consistent with the backfill performance comparison results. The evolution of the texture allows obtaining important information on the evolution of different formulations during mixing, which can be used for intelligent monitoring of CPB preparation process. Full article

Previously conducted studies have established that surface subsidence is typically avoided by filling coal mined-out areas with solid waste. Backfilling hydraulic supports are critically important devices in solid backfill mining, whose operating performance can directly affect backfill mining efficiency. To accurately evaluate the operating performance, this paper proposes hybrid machine learning models for the operating states. An analysis of the factors that influence operating performance provides eight indices for evaluating backfilling hydraulic supports. Based on the data obtained from the Creo simulation model and field measurement, six hybrid models were constructed by combining swarm intelligent algorithms and support vector machines (SVM). Models of the SVM optimized by the modified sparrow search algorithm have shown improved convergence performance. The results show that the modified model has a prediction accuracy of 95.52%. The related evaluation results fit well with the actual support intervals of the backfilling hydraulic support. Full article

With the continuous innovation and development of science and technology, the mining industry has also benefited greatly and improved over time, especially in the field of backfill mining. Mining researchers are increasingly working on cutting-edge technologies, such as applying artificial intelligence to mining production. However, in addition, some problems in the actual engineering are worth people’s attention, and especially in China, such a big mining country, the actual engineering faces many problems. In recent years, Chinese mining researchers have conducted a lot of studies on practical engineering problems in the stope and goaf of backfill mining method in China, among which the three most important points are (1) Calculation problems of backfill slurry transportation; (2) Reliability analysis of backfill pipeline system; (3) Stope backfill process and technology. Therefore, this final part (Part III) will launch the research progress of China’s practical engineering problems from the above two points. Finally, we claim that Part III serves just as a guide to starting a conversation, and hope that many more experts and scholars will be interested and engage in the research of this field. Full article

The optimization of an integrated coal gangue system of mining, dressing, and backfilling in deep underground mining is a multi-objective and complex decision-making process, and the factors such as spatial layout, node location, and transportation equipment need to be considered comprehensively. In order to realize the intellectualized location of the nodes for the logistics and transportation system of underground mining and dressing coal and gangue, this paper establishes the model of the logistics and transportation system of underground mining and dressing coal gangue, and analyzes the key factors of the intellectualized location for the logistics and transportation system of coal and gangue, and the objective function of the node transportation model is deduced. The PSO–QNMs algorithm is proposed for the solution of the objective function, which improves the accuracy and stability of the location selection and effectively avoids the shortcomings of the PSO algorithm with its poor local detailed search ability and the quasi-Newton algorithm with its sensitivity to the initial value. Comparison of the particle swarm and PSO–QNMs algorithm outputs for the specific conditions of the New Julong coal mine, as an example, shows that the PSO–QNMs algorithm reduces the complexity of the calculation, increases the calculation efficiency by eight times, saves 42.8% of the cost value, and improves the efficiency of the node selection of mining–dressing–backfilling systems in a complex underground mining environment. The results confirm that the method has high convergence speed and solution accuracy, and provides a fundamental basis for optimizing the underground coal mine logistics system. Based on the research results, a node siting system for an integrated underground mining, dressing, and backfilling system in coal mines (referred to as MSBPS) was developed. Full article

Cemented paste backfill (CPB) is widely used in underground mining, and attracts more attention these years as it can reduce mining waste and avoid environmental pollution. Normally, to evaluate the functionality of CPB, the compressive strength (UCS) is necessary work, which is also time and money consuming. To address this issue, seven machine learning models were applied and evaluated in this study, in order to predict the UCS of CPB. In the laboratory, a series of tests were performed, and the dataset was constructed considering five key influencing variables, such as the tailings to cement ratio, curing time, solids to cement ratio, fine sand percentage and cement types. The results show that different variables have various effects on the strength of CPB. The optimum models for predicting the UCS of CPB are a support vector machine (SVM), decision tree (DT), random forest (RF) and back-propagation neural network (BPNN), which means that these models can be directly applied for UCS prediction in future work. Furthermore, the intelligent model reveals that the tailings to cement ratio has the most important influence on the strength of CPB. This research can boost CPB application in the field, and guide the artificial intelligence application in future mining. Full article