Smoothed particle hydrodynamics(SPH)is a Lagrangian,mesh-free numerical method renowned for its ability to handle fluid dynamics problems with large interface deformations and multiphase flow coupling.This study intro...Smoothed particle hydrodynamics(SPH)is a Lagrangian,mesh-free numerical method renowned for its ability to handle fluid dynamics problems with large interface deformations and multiphase flow coupling.This study introduces an SPH-based multiphase flow model for simulating bubbly flows involving various immiscible fluids.The model uses a volume-based density discretization equation,ensuring numerical accuracy near interfaces,independent of density ratios,thus maintaining accuracy and stability even at high density ratios.By integrating the Continuous Surface Force method for surface tension into the multiphase SPH model,the study simulates interfacial behavior between phases.The model accurately predicts Laplace pressure differences across interfaces,maintaining interface stability at density ratios up to 100.0.Simulations of single and double bubble ascents elucidate the influence of the Bond number on bubble shape,rising distance,and velocity.As the Bond number increases,bubbles flatten and develop tails,affecting their integrity.The study also simulates multiple bubbles ascents in water,showcasing the model's ability to capture complex interfacial behaviors in bubbly flows,including deformation,adsorption,coalescence,and tearing.展开更多
Microseismic source localization plays a critical role in monitoring mining-induced dynamic disasters,assessing rock mass stability,and analyzing excavation-induced disturbances.With increasing monitoring accuracy and...Microseismic source localization plays a critical role in monitoring mining-induced dynamic disasters,assessing rock mass stability,and analyzing excavation-induced disturbances.With increasing monitoring accuracy and data volume,various localization techniques have emerged to suit different scenarios.We systematically review the development of current microseismic location methods,which can be broadly categorized into three types:(1)Pickingbased methods,such as the Geiger and double-difference algorithms,which are suitable for well-constrained velocity models;(2)Waveform stacking-based localization methods,such as the source scanning algorithm(SSA)and cross-correlation stacking,which eliminate the need for arrival-time picking.These techniques exhibit strong noise resistance and are particularly well-suited for environments with low signal-to-noise ratios(SNR);and(3)Deep learning-based automatic localization approaches,such as PhaseNet and LOCFLOW,which are suitable for large-scale,intelligent monitoring.Furthermore,key factors affecting localization accuracy,such as sensor array geometry,arrival-time picking errors,and velocity model uncertainties,are discussed,along with optimization strategies including 3D velocity tomography,non-predefined velocity inversion,and time-varying velocity modeling.Finally,we explore future directions,including multi-station collaborative deep learning models,intelligent denoising techniques,and risk prediction frameworks constrained by statistical seismology,aiming to advance microseismic localization toward higher precision and robustness.展开更多
Wet ball mill that has been extensively used to comminute the raw materials in various industries possesses the advantages of large production capacity,high grinding efficiency,low investment and so on.Wet mill is a t...Wet ball mill that has been extensively used to comminute the raw materials in various industries possesses the advantages of large production capacity,high grinding efficiency,low investment and so on.Wet mill is a typical gas-liquid-solid three-phase system,and the interaction between these three phases is quite complex,resulting in the difficulty to simulate the wet milling process.As a consequence,a numerical model by coupling computational fluid dynamics(CFD),discrete element method(DEM)and volume of fluid(VOF)is developed to accurately simulate the wet milling process.A novel scheme is also devised and incorporated into CFD-DEM-VOF model to treat the interphase coupling when the particles and CFD cells have the comparable size that will be encountered in simulating the wet milling process.The accuracy of the established CFD-DEM-VOF coupling model is then validated by several test cases,including the single particle sedimentation in air-liquid domain,water entry of particle assembly and three-phase flows in a lab-scale wet mill and an industrial-scale wet mill.Furthermore,the effects of CFD cell size(2.5,5 and 10 mm)and mill rotation speed(10,30 and 50 r/min)on the prediction accuracy are also explored by the test case of lab-scale mill.The results indicate that using the quite coarse CFD cells can deteriorate the simulation accuracy,and increasing the mill rotation speed will enhance this influence.Nevertheless,adopting the very fine CFD cells(e.g.,2.5 mm in this work)in our model is not necessary in terms of the accuracy in simulating the particle behaviors,and the reliable prediction of particle behaviors can still be obtained while using the relatively large CFD cells(e.g.,5 mm in this paper).展开更多
A design approach for online pre-compensation of three-axis cross-coupled contour errors with mismatched dynamics is proposed.In the context of cross-coupled contour control design,online pre-compensation of contour e...A design approach for online pre-compensation of three-axis cross-coupled contour errors with mismatched dynamics is proposed.In the context of cross-coupled contour control design,online pre-compensation of contour errors is commonly employed.However,establishing a specific relationship between contour errors is challenging for mismatched computer numerical control(CNC)systems.Therefore,the design of interpolation methods for mismatched systems remains crucial,as most existing systems struggle to be adjusted to match seamlessly.This study introduces an online pre-compensation scheme for cross-coupled contour errors in three-axis motion,which constitutes a compensation system for real-time correction of contour error estimation.The coupling control structure,based on a speed loop,comprises a proportion integration differentiation(PID)control feedback controller,a feedforward controller,and an online pre-compensation cross-coupled contour controller.The experimental results demonstrate that the proposed three-axis cross-coupled contour error pre-compensation scheme significantly enhances the contour accuracy compared to traditional cross-coupled control systems.Moreover,the proposed cross-coupled contour error pre-compensation controller exhibits superior contour performance over conventional cross-coupled controllers when tracking high-order curvature bending paths.展开更多
Although reinforcement learning(RL)has shown great potential in industrial process coordinated optimization,its direct application to complex scheduling tasks such as the thickening-dewatering process is still limited...Although reinforcement learning(RL)has shown great potential in industrial process coordinated optimization,its direct application to complex scheduling tasks such as the thickening-dewatering process is still limited by challenges including low sample efficiency and slow convergence.In this work,we propose an expert-augmented dual-stage reinforcement learning(EADS-RL)framework.In the first stage,expert data extracted from real industrial operation logs are used to pre-train both the policy and critic networks via imitation learning(IL),aiming to provide the agent with a reliable initial policy and thus accelerate the learning process.In the subsequent online RL optimization stage,EADS-RL effectively integrates expert priors and high-quality online experiences through a non-parametric regression model and a dynamic expert memory.The former offers reliable behavioral guidance for the agent to explore unknown state spaces,thereby improving exploration efficiency;the latter enables the expert memory to evolve by incorporating excellent trajectories discovered by the agent itself,facilitating continuous self-improvement of the policy.The simulation results demonstrate that EADS-RL significantly outperforms existing benchmark algorithms in terms of both convergence speed and final performance.In scheduling task evaluations,the proposed method not only achieves the lowest energy consumption but also attains the highest key process indicator(underflow concentration),while ensuring safe and stable process operation.These results highlight the substantial potential of EADS-RL for practical industrial applications.展开更多
Ore particle size is a fundamental parameter in mineral processing,directly affecting grinding efficiency,equipment performance,and downstream product quality.Traditional manual and mechanical measurement methods are ...Ore particle size is a fundamental parameter in mineral processing,directly affecting grinding efficiency,equipment performance,and downstream product quality.Traditional manual and mechanical measurement methods are time-consuming,low in accuracy,and unsuitable for continuous monitoring.Recently,deep learning has emerged as a promising solution for automated ore size detection.This review systematically introduces deep learning methods for ore particle analysis,with a focus on two major paradigms:object detection(including anchor-based and anchor-free models)and image segmentation(including semantic segmentation,instance segmentation,and boundary regression).The performance of each method is compared across varying ore stacking scenarios,such as heavy occlusion,irregular particle shapes,and dusty environments,with an emphasis on their respective strengths and limitations.In addition,the review identifies major technical,equipment-related,and data-centric challenges that impede industrial deployment.These challenges include the development of robust algorithms,ensuring reliable real-time operation under adverse conditions,and securing high-quality annotated datasets.Recent advancements are examined,including weak supervision,few-shot learning,and multimodal fusion of RGB(Red,Green,Blue),depth,and infrared data.To enable intelligent and scalable ore particle size monitoring systems,future efforts should focus on building accurate,efficient,and generalizable models supported by self-supervised pretraining and sensor integration.展开更多
Methylene blue(MB)contamination in aqueous environments has emerged as a pressing environmental challenge,thereby necessitating the development of effective remediation strategies.Activated carbons(ACs),as highly prom...Methylene blue(MB)contamination in aqueous environments has emerged as a pressing environmental challenge,thereby necessitating the development of effective remediation strategies.Activated carbons(ACs),as highly promising adsorbent materials,have garnered considerable research attention worldwide for MB removal.This study proposes a machine learning(ML)approach to simulate and predict the performance of ACs in removing MB from aqueous solutions.We compiled a database from 282 literature sources,containing 301 data points encompassing variables across two dimensions:ACs characteristics and operational conditions.Following data preprocessing and logarithmic transformation of the prediction target,a random forest(RF)algorithm was fine-tuned to establish the MB adsorption capacity prediction model.Experimental results demonstrate that the optimized RF model exhibits high predictive accuracy,with R^(2) of 0.9998 and RMSE of 2.446 for the validation set.Among the factors,the specific surface area of ACs,initial MB concentration in water,and pore volume of ACs were identified as the primary influencing factors.Furthermore,partial dependence analysis was employed to investigate the impact of individual variables on adsorption capacity,providing crucial insights for adsorbent design and process optimization.This research develops a comprehensive framework for applying machine learning(ML)to address environmental problems,providing a practical tool to facilitate the design and implementation of ACs-based water treatment systems.展开更多
The vibration mill is a high-efficiency ultrafine grinding device;its dynamic char-acteristics,along with the motion of the grinding medium,directly influence both grinding efficiency and product quality.However,there...The vibration mill is a high-efficiency ultrafine grinding device;its dynamic char-acteristics,along with the motion of the grinding medium,directly influence both grinding efficiency and product quality.However,there is a challenge in efficiently simulating the complex behavior of the grinding media within vibratory mills to maximize energy efficiency and enhance grinding performance.To bridge this,the research employs an integrated kinematic-discrete element method-experimental approach specifically designed for eccentric vibration mills.Kinematic analysis reveals that the mill's motion follows a crank-slider mechanism.Engineering discrete element method(EDEM)simulations,experimentally validated through grinding tests,were used to analyze media collision dynamics(including frequency,contact forces,energy distribution,and trajectories)at filling rates of 60%,70%,80%,and 90%.The results indicate that an 80%media filling rate optimizes performance:the collision number is 36035,contact force reaches about 450 N,showing a wave form of a sine function.Through an actual test of the grinding effect under different media filling rates,the newly generated−0.018 mm size fraction content and grinding efficiency reach their highest levels at 0.441 t/m^(3)·h and 0.00557 t/kW·h,respectively.The particle size distribution of the ground material is uniform,validating simulation rationality.Conversely,60%and 70%filling rates yielded suboptimal grinding efficiency,while a 90%filling rate sharply reduces both efficiency and product uniformity due to concentric media motion and low contact forces.This work successfully maps the relationship between filling rate and crushing energy efficiency,provid-ing a validated framework for the operation of eccentric vibratory mills.展开更多
The construction of the road network is a very critical issue in the transportation industry.An accurate road network can effectively provide vital preconditions for logistics transportation,traffic diversion,vehicle ...The construction of the road network is a very critical issue in the transportation industry.An accurate road network can effectively provide vital preconditions for logistics transportation,traffic diversion,vehicle scheduling,etc.At present,it is still a difficult problem to achieve rapid and accurate extraction of road networks in different transportation environments.In order to solve the problem of road network automatic extraction in open-pit mines,this paper proposes a Rolling Clustering Algorithm(RCA)based on truck GPS trajectory data.The algorithm combines the advantages of road intersection recognition and trajectory clustering,which improves the accuracy of road network extraction while ensuring the topology.First,the original data are preprocessed to eliminate the influence of noise points.Next,all trajectories are divided into road segments through the identification of road intersection nodes,and rolling clustering is performed to extract road skeletons.Finally,a complete road network is generated by connecting the segments and intersection nodes.This study evaluated RCA's performance by comparing it with several representative road inference algorithms.The results show that the proposed algorithm outperformed others in terms of precision and recall.This algorithm achieves the best extraction accuracy while ensuring the road network topology.In the final validation phase,the GPS trajectory data of open-pit mine trucks are adopted for practical application.The proposed framework based on GPS trajectory provides a new solution for the road network extraction problem.展开更多
This study explores the potential of the advanced selective state spaces model(SSSM)in modeling complicated process industries system and proposes the process industry state identification model(PISIM)for controlled p...This study explores the potential of the advanced selective state spaces model(SSSM)in modeling complicated process industries system and proposes the process industry state identification model(PISIM)for controlled prediction of flotation cell pulp level.As a neural system identification model,the PISIM inherits two advantages of the SSSM to address the challenges in identifying flotation systems,including modeling the impact of frequent upstream fluctuations on system states,complex nonlinear physicochemical processes,and long-term dependencies.The first advantage is the ability to capture long-range dependencies,thereby boosting its long-term predictive accuracy.The second lies in the model structure adhering to scaling laws,enabling ongoing enhancements in performance as datasets expand.PISIM is evaluated using a real industrial dataset from a flotation plant at a copper mine in Zambia,with the results demonstrating its theoretical advantages.In a 4.5-hour pulp level prediction task,PISIM outperforms the baseline model by more than 31.34%.Furthermore,a flotation process control simulation experimental system based on PISIM is developed and deployed in a flotation plant in Zambia,assisting engineers in evaluating and optimizing setpoint strategies,ensuring stable production and improving production efficiency.展开更多
Solar photocatalysis is regarded as a clean and sustainable technology which can operate under solar light to generate reactive oxygen species(ROS).However,due to the heterogeneous nature,photocatalysis showed several...Solar photocatalysis is regarded as a clean and sustainable technology which can operate under solar light to generate reactive oxygen species(ROS).However,due to the heterogeneous nature,photocatalysis showed several technological problems such as weak mass transfer and photocatalyst deactivation.Ultrasonic assisted photo(piezo)catalytic process refers to the use of ultrasonic cavitation as external force to boost the generation of ROS and prevent the deactivation of the catalyst.This review aims to discuss the mechanistic pathways of photo(piezo)catalytic systems catalyzed by ultrasonic irradiation.The effect of microjets and shock waves plays a crucial role in maximizing the generation of ROS and activating the surface of the catalyst along with a significant improvement in mass transfer.In such a synergistic system,the mineralization of persistent organic pollutants(POPs)in water can effectively be achieved,leading to high water quality.Ultrasonic assisted catalysis for environmental remediation would be a sustainable tool and could solve several technology issues in photocatalysis,which might help to transfer this technology to real-world applications.展开更多
Flotation is the principal beneficiation method for sphalerite.Sphalerite with different lattice impurities exhibits varied electronic properties and flotation behavior.The crystal structure,band structure,density of ...Flotation is the principal beneficiation method for sphalerite.Sphalerite with different lattice impurities exhibits varied electronic properties and flotation behavior.The crystal structure,band structure,density of states and frontier orbital of Cd-Pb-doped and Cd-Cu-doped sphalerite were studied based on density functional theory(DFT).The calculation results show that the formation of Cd-Pb-doped sphalerite is more difficult than that of Cd-Cu-doped sphalerite.Moreover,the Cd-Pb and Cd-Cu impurity in sphalerite could enhance its conductivity,promote oxidation and improve its interaction with xanthate,thereby influencing the floatability of sphalerite.展开更多
Ground vibration events represent a critical challenge to operational safety and risk management in mining engineering.Reliable estimation of ground shaking intensity serves as a key prerequisite for effective seismic...Ground vibration events represent a critical challenge to operational safety and risk management in mining engineering.Reliable estimation of ground shaking intensity serves as a key prerequisite for effective seismic risk mitigation in mining environments.This study investigates the application of intensity classification techniques to mining scenarios and introduces a deep learning-based Multi-Scale Feature Fusion Classification Network.The model utilizes waveform data within two seconds following P-wave onset to enable rapid and accurate classification of seismic intensity levels.With an intensity threshold of 4.5 used to differentiate low-and high-intensity events,the model achieves a classification accuracy of 90.27%on the test set.Particularly,it surpasses 99%accuracy for samples with intensity levels≤3 or≥6,demonstrating a substantial advantage over traditional approaches that require complete waveform sequences and exhibit limited processing efficiency.Notably,the model maintains over 85%accuracy under-5dB SNR conditions,outperforming state-of-the-art methods in noise resilience,while reducing computational complexity by 78%compared to Transformer-based architectures.The proposed method supports real-time seismic monitoring in mining areas,facilitating rapid assessment of risk levels and providing critical decision support for operational adjustments and emergency response.These capabilities contribute to enhanced seismic resilience and the promotion of safe mining practices.展开更多
Aiming to solve the problem of unstable crystal size during the preparation of anhydrous magnesium carbonate,a back propagation(BP)neural network was introduced to optimize the preparation process.Using magnesite as t...Aiming to solve the problem of unstable crystal size during the preparation of anhydrous magnesium carbonate,a back propagation(BP)neural network was introduced to optimize the preparation process.Using magnesite as the raw material,a four-factor,three-level orthogonal test was designed to analyze the effects of NaHCO_(3) dosage,reaction time,temperature,and Mg(HCO_(3))_(2)concentration on the particle size of the products.A three-layer BP neural network model(topology 4-10-1)was constructed based on the experimental data,and the prediction of process parameters was realized through factor-by-factor and point-by-point training.The results showed that the best process parameters obtained from the optimization were 14 g·L^(−1)NaHCO_(3) dosage,199℃,19 h,and 0.24 mol·L^(−1)Mg(HCO_(3))_(2)concentration,corresponding to a minimum particle size of 12.06μm(which was 13.2%lower than that of the results of orthogonal tests)with an average prediction error of 3.3%.X-ray diffraction(XRD),scanning electron microscopy(SEM),fourier transform infrared spectroscopy(FT-IR),and thermogravimetric-differential thermal analysis(TG-DTA)showed that the optimized products were pure-phase rhombic anhydrous magnesium carbonate crystals with good dispersion,verifying the effectiveness of the BP neural network in process optimization.展开更多
Airlift reactors are used in a wide range of industries,such as hydrometallurgy,biochemical processes,chemical process industry and wastewater treatment.Despite the simple structure of airlift reactors,the flow field ...Airlift reactors are used in a wide range of industries,such as hydrometallurgy,biochemical processes,chemical process industry and wastewater treatment.Despite the simple structure of airlift reactors,the flow field becomes complex with increasing gas velocity,and gas bubbles in the circulating regime can be observed in practice.In this paper,a numerical modelling method based on computational fluid dynamics(CFD)is presented for gas-liquid flow in airlift reactors under different bubble recirculation regimes.Gas-liquid flow was modelled using the Eulerian two-fluid equations,and extra user defined subroutines were incorporated to consider the complex physics,such as bubble-induced turbulence and turbulent dispersion force.Some alternative correlations for drag coefficient were tested to compare their ability to capture the bubble distributions in the riser and downcomer of the airlift reactors,with consideration of the interaction between bubbles.A model including multiple bubble sizes was applied to obtain more accurate simulation results of gas holdup and water velocity.Also,the use of the inhomogeneous multiple-size-group(MUSIG)model was explored as a way to better predict the complex flow regimes.The modelling method was applied to a laboratory internal loop airlift reactor,and the simulation results were compared with the published experimental measurements for gas holdup and water velocity.Reasonable agreement was obtained over a range of operating conditions,and an improvement was demonstrated using the proposed method.The simulations have shown that the inhomogeneous MUSIG model is a suitable tool to describe the complex gas-liquid interaction in the airlift reactor at a high gas superficial velocity.展开更多
Under the carbon peaking and carbon neutrality goals,the aluminum electrolysis industry faces significant challenges in energy conservation,carbon reduction,and environmental protection.Utilizing new energy electricit...Under the carbon peaking and carbon neutrality goals,the aluminum electrolysis industry faces significant challenges in energy conservation,carbon reduction,and environmental protection.Utilizing new energy electricity is one of the most effective ways to reduce carbon emissions in primary aluminum production.This paper addresses the issue of uncontrolled thermal balance in electrolysis cells caused by large fluctuations in current during the absorption of new energy electricity.A multi-field solidification model of the electric-thermal-flow facing the side-controlled heat dissipation structure is constructed.The study investigates the impact of large current fluctuations,heat exchanger structure and different process parameters on the temperature field and the ledge shape of the electrolysis cell.The optimum process and corresponding heat dissipation structure of electrolysis cell under current fluctuations are summarized to provide theoretical guidance for aluminum electrolysis cells in absorbing fluctuating new energy electricity.The results indicate that the aluminum electrolysis cell with controlled heat exchange with standard rectangular carbon blocks,high-insulation irregular carbon blocks(inner lining type 2),and heat-conducting protrusions with a thermal conductivity of 120 W·m^(-1)·K^(−1) can effectively absorb current fluctuations ranging from−20%to+20%within a short period under collaborative control of side thermal regulation and processes.However,when maintaining a+20%current for prolonged periods,the cell may still experience uncontrolled thermal balance.展开更多
Currently,global mining development has entered a new historical stage.With the gradual depletion of shallow mineral resources and the increasing proven reserves of deep resources,deep resource exploitation has become...Currently,global mining development has entered a new historical stage.With the gradual depletion of shallow mineral resources and the increasing proven reserves of deep resources,deep resource exploitation has become a global mining trend.However,deep resource development faces numerous challenges,including product pricing,production costs,operational safety,and workforce issues.The development of intelligent and even autonomous mining technologies,along with the establishment of smart mines,has become an inevitable choice for deep resource exploitation.A key technology and complete technical system for underground metal mine intelligent mining is constructed around the mining production process,with"ore flow"as the main thread.With"equipment intelligence","precise positioning","real-time dispatching","high-speed communication",and"continuous mining"as the main themes,remote,tele-controlled,and intelligent mine production management is being realized.The achievements are demonstrated in engineering applications at multiple mining enterprises,providing theoretical foundations and technical support for the intelligent,efficient,and green development of deep mineral resources.展开更多
Flow field and mixing characteristics in a flotation cell not only significantly affect particle suspension,reagent distribution and froth stabilization,but also determine the flotation efficiency.This work aims to ob...Flow field and mixing characteristics in a flotation cell not only significantly affect particle suspension,reagent distribution and froth stabilization,but also determine the flotation efficiency.This work aims to obtain a detailed understanding of the water flow in a flotation cell through combined use of single-phase computational fluid dynamics(CFD)model and advanced laser measurement using particle image velocimetry(PIV).The CFD model has been set up to simulate a 1.58 m3 industrial Wemco flotation cell.Following model validation using PIV measurement data taken at several representative planes,the flow dynamics in the flotation cell have been analysed in terms of flow pattern and velocity.The results show that good agreement is achieved between predicted and measured time-averaged flows,with three annular recirculation zones observed.In the mixing characteristics simulations,tracer injection was used to investigate the mixing time,residence time distribution and local flow patterns.Mixing time is found to decrease with increase in rotor speed,proportional to the inverse of speed,as expected for fully turbulent flow.As the through-flow rate increases,the residence time decreases,proportional to the inverse of flow rate,as expected.The position of the cell tailings underflow outlet has a significant influence on the lower recirculation zone,altering the flow patterns and the extent of recirculation within the system.展开更多
As an impurity compound in concrete,kaolin could adsorb much polycarboxylate ether(PCE)superplasticizer,depressing the efficiency of PCE significantly.The mechanisms underlying the absorption of carboxylic acid(PCE-C)...As an impurity compound in concrete,kaolin could adsorb much polycarboxylate ether(PCE)superplasticizer,depressing the efficiency of PCE significantly.The mechanisms underlying the absorption of carboxylic acid(PCE-C),sulfonic acid(PCE-S),and phosphoric acid(PCE-P)groups on the kaolin(001)surface were investigated by adsorption capacity test and density functional theory(DFT).Adsorption experiments show that the adsorption capacity of PCE-P on kaolin is significantly higher than that of PCE-S,while PCE-C shows the least adsorption on kaolin.Based on DFT calculations,the interaction between PCE-P and the kaolin(001)surface is the strongest,mainly due to the formation of a strong chemical bond,along with hydrogen bonding and electrostatic interactions.However,no stable chemical bonds form between the kaolin(001)surface and either PCE-S or PCE-C.Additionally,the adsorption energy suggests that PCE-S has a higher adsorption capacity on the kaolin(001)surface than PCE-C.Studies have shown that kaolin exhibits a strong adsorption capacity for the phosphoric acid.This adsorption significantly reduces the dispersing ability of the superplasticizer,thereby impairing its effectiveness in reducing water demand and improving workability in concrete.This study aims to provide a theoretical basis for choosing PCEs and improving their efficiency when producing concrete made with kaolin-involved aggregates.展开更多
In recent years,with the deterioration of mineral resource endowment and the development of intelligent technologies,traditional flotation machine technology has been rapidly integrated with cutting-edge technologies,...In recent years,with the deterioration of mineral resource endowment and the development of intelligent technologies,traditional flotation machine technology has been rapidly integrated with cutting-edge technologies,such as modern sensing,artificial intelligence,big data,and the Internet of Things.This integration aims to improve the efficiency and controllability of the flotation process,thereby driving the transformation of the mineral processing field toward intelligent,automated,and green directions.However,as a new development,intelligent flotation machines have not yet achieved a unified and clear understanding.This study interprets intelligent flotation machines from three aspects:definition,connotation,and development path.The core characteristics of intelligent flotation machines have been proposed,including self-sensing and self-diagnosis abilities in the whole spatial domain,data-based intelligent control algorithms,predictive maintenance of core components,and coordination of global and local optimization in flotation processes.This study identifies the current challenges faced by intelligent flotation machines,and proposes the future development paths,including enhancing the comprehensive monitoring and intelligent regulation of flotation parameters,improving equipment fault prediction and precise localization,and achieving unmanned operations and intelligent maintenance.By continuously optimizing and refining the design and application of intelligent flotation machines,they can play an increasingly important role in the sustainable development of the mining industry.展开更多
基金the State Key Laboratory of Intelligent Optimized Manufacturing in Mining&Metallurgy Process Open Research Fund(No.JTKY202404622)and(No.BGRIMM-KZSKL-2023-12).
文摘Smoothed particle hydrodynamics(SPH)is a Lagrangian,mesh-free numerical method renowned for its ability to handle fluid dynamics problems with large interface deformations and multiphase flow coupling.This study introduces an SPH-based multiphase flow model for simulating bubbly flows involving various immiscible fluids.The model uses a volume-based density discretization equation,ensuring numerical accuracy near interfaces,independent of density ratios,thus maintaining accuracy and stability even at high density ratios.By integrating the Continuous Surface Force method for surface tension into the multiphase SPH model,the study simulates interfacial behavior between phases.The model accurately predicts Laplace pressure differences across interfaces,maintaining interface stability at density ratios up to 100.0.Simulations of single and double bubble ascents elucidate the influence of the Bond number on bubble shape,rising distance,and velocity.As the Bond number increases,bubbles flatten and develop tails,affecting their integrity.The study also simulates multiple bubbles ascents in water,showcasing the model's ability to capture complex interfacial behaviors in bubbly flows,including deformation,adsorption,coalescence,and tearing.
基金funded by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project(2024ZD1004505)Gansu Provincial Joint Research Fund for the Year 2024(24JRRA803)+1 种基金Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology(2022B1212010002)the National Natural Science Foundation of China(42174128).
文摘Microseismic source localization plays a critical role in monitoring mining-induced dynamic disasters,assessing rock mass stability,and analyzing excavation-induced disturbances.With increasing monitoring accuracy and data volume,various localization techniques have emerged to suit different scenarios.We systematically review the development of current microseismic location methods,which can be broadly categorized into three types:(1)Pickingbased methods,such as the Geiger and double-difference algorithms,which are suitable for well-constrained velocity models;(2)Waveform stacking-based localization methods,such as the source scanning algorithm(SSA)and cross-correlation stacking,which eliminate the need for arrival-time picking.These techniques exhibit strong noise resistance and are particularly well-suited for environments with low signal-to-noise ratios(SNR);and(3)Deep learning-based automatic localization approaches,such as PhaseNet and LOCFLOW,which are suitable for large-scale,intelligent monitoring.Furthermore,key factors affecting localization accuracy,such as sensor array geometry,arrival-time picking errors,and velocity model uncertainties,are discussed,along with optimization strategies including 3D velocity tomography,non-predefined velocity inversion,and time-varying velocity modeling.Finally,we explore future directions,including multi-station collaborative deep learning models,intelligent denoising techniques,and risk prediction frameworks constrained by statistical seismology,aiming to advance microseismic localization toward higher precision and robustness.
基金supported by National Natural Science Foundation of China(No.22078283)State Key Laboratory of Intelligent Optimized Manufacturing in Mining&Metallurgy Process(No.BGRIMM-KZSKL-2022-3).
文摘Wet ball mill that has been extensively used to comminute the raw materials in various industries possesses the advantages of large production capacity,high grinding efficiency,low investment and so on.Wet mill is a typical gas-liquid-solid three-phase system,and the interaction between these three phases is quite complex,resulting in the difficulty to simulate the wet milling process.As a consequence,a numerical model by coupling computational fluid dynamics(CFD),discrete element method(DEM)and volume of fluid(VOF)is developed to accurately simulate the wet milling process.A novel scheme is also devised and incorporated into CFD-DEM-VOF model to treat the interphase coupling when the particles and CFD cells have the comparable size that will be encountered in simulating the wet milling process.The accuracy of the established CFD-DEM-VOF coupling model is then validated by several test cases,including the single particle sedimentation in air-liquid domain,water entry of particle assembly and three-phase flows in a lab-scale wet mill and an industrial-scale wet mill.Furthermore,the effects of CFD cell size(2.5,5 and 10 mm)and mill rotation speed(10,30 and 50 r/min)on the prediction accuracy are also explored by the test case of lab-scale mill.The results indicate that using the quite coarse CFD cells can deteriorate the simulation accuracy,and increasing the mill rotation speed will enhance this influence.Nevertheless,adopting the very fine CFD cells(e.g.,2.5 mm in this work)in our model is not necessary in terms of the accuracy in simulating the particle behaviors,and the reliable prediction of particle behaviors can still be obtained while using the relatively large CFD cells(e.g.,5 mm in this paper).
文摘A design approach for online pre-compensation of three-axis cross-coupled contour errors with mismatched dynamics is proposed.In the context of cross-coupled contour control design,online pre-compensation of contour errors is commonly employed.However,establishing a specific relationship between contour errors is challenging for mismatched computer numerical control(CNC)systems.Therefore,the design of interpolation methods for mismatched systems remains crucial,as most existing systems struggle to be adjusted to match seamlessly.This study introduces an online pre-compensation scheme for cross-coupled contour errors in three-axis motion,which constitutes a compensation system for real-time correction of contour error estimation.The coupling control structure,based on a speed loop,comprises a proportion integration differentiation(PID)control feedback controller,a feedforward controller,and an online pre-compensation cross-coupled contour controller.The experimental results demonstrate that the proposed three-axis cross-coupled contour error pre-compensation scheme significantly enhances the contour accuracy compared to traditional cross-coupled control systems.Moreover,the proposed cross-coupled contour error pre-compensation controller exhibits superior contour performance over conventional cross-coupled controllers when tracking high-order curvature bending paths.
文摘Although reinforcement learning(RL)has shown great potential in industrial process coordinated optimization,its direct application to complex scheduling tasks such as the thickening-dewatering process is still limited by challenges including low sample efficiency and slow convergence.In this work,we propose an expert-augmented dual-stage reinforcement learning(EADS-RL)framework.In the first stage,expert data extracted from real industrial operation logs are used to pre-train both the policy and critic networks via imitation learning(IL),aiming to provide the agent with a reliable initial policy and thus accelerate the learning process.In the subsequent online RL optimization stage,EADS-RL effectively integrates expert priors and high-quality online experiences through a non-parametric regression model and a dynamic expert memory.The former offers reliable behavioral guidance for the agent to explore unknown state spaces,thereby improving exploration efficiency;the latter enables the expert memory to evolve by incorporating excellent trajectories discovered by the agent itself,facilitating continuous self-improvement of the policy.The simulation results demonstrate that EADS-RL significantly outperforms existing benchmark algorithms in terms of both convergence speed and final performance.In scheduling task evaluations,the proposed method not only achieves the lowest energy consumption but also attains the highest key process indicator(underflow concentration),while ensuring safe and stable process operation.These results highlight the substantial potential of EADS-RL for practical industrial applications.
基金supported by National Key R&D Program of China under Grant 2021YFC2902702,2021YFC2902704pilot project of BGRIMM Technology Group under Grant 02-2407youth science and technology innovation fund of BGRIMM Technology Group under Grant 04-2509.
文摘Ore particle size is a fundamental parameter in mineral processing,directly affecting grinding efficiency,equipment performance,and downstream product quality.Traditional manual and mechanical measurement methods are time-consuming,low in accuracy,and unsuitable for continuous monitoring.Recently,deep learning has emerged as a promising solution for automated ore size detection.This review systematically introduces deep learning methods for ore particle analysis,with a focus on two major paradigms:object detection(including anchor-based and anchor-free models)and image segmentation(including semantic segmentation,instance segmentation,and boundary regression).The performance of each method is compared across varying ore stacking scenarios,such as heavy occlusion,irregular particle shapes,and dusty environments,with an emphasis on their respective strengths and limitations.In addition,the review identifies major technical,equipment-related,and data-centric challenges that impede industrial deployment.These challenges include the development of robust algorithms,ensuring reliable real-time operation under adverse conditions,and securing high-quality annotated datasets.Recent advancements are examined,including weak supervision,few-shot learning,and multimodal fusion of RGB(Red,Green,Blue),depth,and infrared data.To enable intelligent and scalable ore particle size monitoring systems,future efforts should focus on building accurate,efficient,and generalizable models supported by self-supervised pretraining and sensor integration.
基金2023 Exploratory experiment construction project of Guangzhou University(SJ202309)Basic and Applied Basic Research Foundation of Guangdong Province(2023A1515010038)Guangzhou University Large-scale Equipment Open Sharing Fund,Youth Foundation of SCIES(PM-zx097-202304-147).
文摘Methylene blue(MB)contamination in aqueous environments has emerged as a pressing environmental challenge,thereby necessitating the development of effective remediation strategies.Activated carbons(ACs),as highly promising adsorbent materials,have garnered considerable research attention worldwide for MB removal.This study proposes a machine learning(ML)approach to simulate and predict the performance of ACs in removing MB from aqueous solutions.We compiled a database from 282 literature sources,containing 301 data points encompassing variables across two dimensions:ACs characteristics and operational conditions.Following data preprocessing and logarithmic transformation of the prediction target,a random forest(RF)algorithm was fine-tuned to establish the MB adsorption capacity prediction model.Experimental results demonstrate that the optimized RF model exhibits high predictive accuracy,with R^(2) of 0.9998 and RMSE of 2.446 for the validation set.Among the factors,the specific surface area of ACs,initial MB concentration in water,and pore volume of ACs were identified as the primary influencing factors.Furthermore,partial dependence analysis was employed to investigate the impact of individual variables on adsorption capacity,providing crucial insights for adsorbent design and process optimization.This research develops a comprehensive framework for applying machine learning(ML)to address environmental problems,providing a practical tool to facilitate the design and implementation of ACs-based water treatment systems.
基金funded by Hebei Province innovation ability improvement plan project(23564201D)Open Foundation of State Key Laboratory of Mineral Processing(BGRIMM-KJSKL-2021-21).
文摘The vibration mill is a high-efficiency ultrafine grinding device;its dynamic char-acteristics,along with the motion of the grinding medium,directly influence both grinding efficiency and product quality.However,there is a challenge in efficiently simulating the complex behavior of the grinding media within vibratory mills to maximize energy efficiency and enhance grinding performance.To bridge this,the research employs an integrated kinematic-discrete element method-experimental approach specifically designed for eccentric vibration mills.Kinematic analysis reveals that the mill's motion follows a crank-slider mechanism.Engineering discrete element method(EDEM)simulations,experimentally validated through grinding tests,were used to analyze media collision dynamics(including frequency,contact forces,energy distribution,and trajectories)at filling rates of 60%,70%,80%,and 90%.The results indicate that an 80%media filling rate optimizes performance:the collision number is 36035,contact force reaches about 450 N,showing a wave form of a sine function.Through an actual test of the grinding effect under different media filling rates,the newly generated−0.018 mm size fraction content and grinding efficiency reach their highest levels at 0.441 t/m^(3)·h and 0.00557 t/kW·h,respectively.The particle size distribution of the ground material is uniform,validating simulation rationality.Conversely,60%and 70%filling rates yielded suboptimal grinding efficiency,while a 90%filling rate sharply reduces both efficiency and product uniformity due to concentric media motion and low contact forces.This work successfully maps the relationship between filling rate and crushing energy efficiency,provid-ing a validated framework for the operation of eccentric vibratory mills.
基金supported by the National Natural Science Foundation of China[Grant No.52374135 and No.52074205]The Shaanxi Province Metal Mine Intelligent Mining Theory and Technology Innovation Team[No.2023-CX-TD-12].
文摘The construction of the road network is a very critical issue in the transportation industry.An accurate road network can effectively provide vital preconditions for logistics transportation,traffic diversion,vehicle scheduling,etc.At present,it is still a difficult problem to achieve rapid and accurate extraction of road networks in different transportation environments.In order to solve the problem of road network automatic extraction in open-pit mines,this paper proposes a Rolling Clustering Algorithm(RCA)based on truck GPS trajectory data.The algorithm combines the advantages of road intersection recognition and trajectory clustering,which improves the accuracy of road network extraction while ensuring the topology.First,the original data are preprocessed to eliminate the influence of noise points.Next,all trajectories are divided into road segments through the identification of road intersection nodes,and rolling clustering is performed to extract road skeletons.Finally,a complete road network is generated by connecting the segments and intersection nodes.This study evaluated RCA's performance by comparing it with several representative road inference algorithms.The results show that the proposed algorithm outperformed others in terms of precision and recall.This algorithm achieves the best extraction accuracy while ensuring the road network topology.In the final validation phase,the GPS trajectory data of open-pit mine trucks are adopted for practical application.The proposed framework based on GPS trajectory provides a new solution for the road network extraction problem.
基金supported by the National Natural Science Foundation of China(62506031,62332017,U22A2022)National Science and Technology Major Project of the Ministry of Science and Technology of China(2024ZD0608100).
文摘This study explores the potential of the advanced selective state spaces model(SSSM)in modeling complicated process industries system and proposes the process industry state identification model(PISIM)for controlled prediction of flotation cell pulp level.As a neural system identification model,the PISIM inherits two advantages of the SSSM to address the challenges in identifying flotation systems,including modeling the impact of frequent upstream fluctuations on system states,complex nonlinear physicochemical processes,and long-term dependencies.The first advantage is the ability to capture long-range dependencies,thereby boosting its long-term predictive accuracy.The second lies in the model structure adhering to scaling laws,enabling ongoing enhancements in performance as datasets expand.PISIM is evaluated using a real industrial dataset from a flotation plant at a copper mine in Zambia,with the results demonstrating its theoretical advantages.In a 4.5-hour pulp level prediction task,PISIM outperforms the baseline model by more than 31.34%.Furthermore,a flotation process control simulation experimental system based on PISIM is developed and deployed in a flotation plant in Zambia,assisting engineers in evaluating and optimizing setpoint strategies,ensuring stable production and improving production efficiency.
文摘Solar photocatalysis is regarded as a clean and sustainable technology which can operate under solar light to generate reactive oxygen species(ROS).However,due to the heterogeneous nature,photocatalysis showed several technological problems such as weak mass transfer and photocatalyst deactivation.Ultrasonic assisted photo(piezo)catalytic process refers to the use of ultrasonic cavitation as external force to boost the generation of ROS and prevent the deactivation of the catalyst.This review aims to discuss the mechanistic pathways of photo(piezo)catalytic systems catalyzed by ultrasonic irradiation.The effect of microjets and shock waves plays a crucial role in maximizing the generation of ROS and activating the surface of the catalyst along with a significant improvement in mass transfer.In such a synergistic system,the mineralization of persistent organic pollutants(POPs)in water can effectively be achieved,leading to high water quality.Ultrasonic assisted catalysis for environmental remediation would be a sustainable tool and could solve several technology issues in photocatalysis,which might help to transfer this technology to real-world applications.
基金the National Natural Science Foundation of China(No.52174246)the Open Foundation of State Key Laboratory of Mineral Processing(No.BGRIMM-KJSKL-2024-03)the Major Science and Technology Projects in Yunnan Province(No.202202AB080012).
文摘Flotation is the principal beneficiation method for sphalerite.Sphalerite with different lattice impurities exhibits varied electronic properties and flotation behavior.The crystal structure,band structure,density of states and frontier orbital of Cd-Pb-doped and Cd-Cu-doped sphalerite were studied based on density functional theory(DFT).The calculation results show that the formation of Cd-Pb-doped sphalerite is more difficult than that of Cd-Cu-doped sphalerite.Moreover,the Cd-Pb and Cd-Cu impurity in sphalerite could enhance its conductivity,promote oxidation and improve its interaction with xanthate,thereby influencing the floatability of sphalerite.
基金supported by National Natural Science Foundation of China(62276058,41774063)Fundamental Research Funds for the Central Universities(N25GFZ011).
文摘Ground vibration events represent a critical challenge to operational safety and risk management in mining engineering.Reliable estimation of ground shaking intensity serves as a key prerequisite for effective seismic risk mitigation in mining environments.This study investigates the application of intensity classification techniques to mining scenarios and introduces a deep learning-based Multi-Scale Feature Fusion Classification Network.The model utilizes waveform data within two seconds following P-wave onset to enable rapid and accurate classification of seismic intensity levels.With an intensity threshold of 4.5 used to differentiate low-and high-intensity events,the model achieves a classification accuracy of 90.27%on the test set.Particularly,it surpasses 99%accuracy for samples with intensity levels≤3 or≥6,demonstrating a substantial advantage over traditional approaches that require complete waveform sequences and exhibit limited processing efficiency.Notably,the model maintains over 85%accuracy under-5dB SNR conditions,outperforming state-of-the-art methods in noise resilience,while reducing computational complexity by 78%compared to Transformer-based architectures.The proposed method supports real-time seismic monitoring in mining areas,facilitating rapid assessment of risk levels and providing critical decision support for operational adjustments and emergency response.These capabilities contribute to enhanced seismic resilience and the promotion of safe mining practices.
基金financially supported by National Natural Science Foundation of China(No.52374271&No.52404290)Liaoning Province'Xing Liao Ying Cai Program'Outstanding Young Talents Project(XLYC2403010)+1 种基金Liaoning Provincial Engineering Research Center for High-Value Utilization of Magnesite(LMKK20240101)Research Fund Project of Liaoning Provincial Education Department(SYLUGXRC+12&LJMKZ20220585).
文摘Aiming to solve the problem of unstable crystal size during the preparation of anhydrous magnesium carbonate,a back propagation(BP)neural network was introduced to optimize the preparation process.Using magnesite as the raw material,a four-factor,three-level orthogonal test was designed to analyze the effects of NaHCO_(3) dosage,reaction time,temperature,and Mg(HCO_(3))_(2)concentration on the particle size of the products.A three-layer BP neural network model(topology 4-10-1)was constructed based on the experimental data,and the prediction of process parameters was realized through factor-by-factor and point-by-point training.The results showed that the best process parameters obtained from the optimization were 14 g·L^(−1)NaHCO_(3) dosage,199℃,19 h,and 0.24 mol·L^(−1)Mg(HCO_(3))_(2)concentration,corresponding to a minimum particle size of 12.06μm(which was 13.2%lower than that of the results of orthogonal tests)with an average prediction error of 3.3%.X-ray diffraction(XRD),scanning electron microscopy(SEM),fourier transform infrared spectroscopy(FT-IR),and thermogravimetric-differential thermal analysis(TG-DTA)showed that the optimized products were pure-phase rhombic anhydrous magnesium carbonate crystals with good dispersion,verifying the effectiveness of the BP neural network in process optimization.
基金support from National Key R&D Program of China(No.2021YFC2902700)pilot project of BGRIMM Technology Group(No.02-2407)special fund of National Key Laboratory of Ni&Co Associated Minerals Resources Development and Comprehensive Utilization(No.GZSYS-KY-2022-014).
文摘Airlift reactors are used in a wide range of industries,such as hydrometallurgy,biochemical processes,chemical process industry and wastewater treatment.Despite the simple structure of airlift reactors,the flow field becomes complex with increasing gas velocity,and gas bubbles in the circulating regime can be observed in practice.In this paper,a numerical modelling method based on computational fluid dynamics(CFD)is presented for gas-liquid flow in airlift reactors under different bubble recirculation regimes.Gas-liquid flow was modelled using the Eulerian two-fluid equations,and extra user defined subroutines were incorporated to consider the complex physics,such as bubble-induced turbulence and turbulent dispersion force.Some alternative correlations for drag coefficient were tested to compare their ability to capture the bubble distributions in the riser and downcomer of the airlift reactors,with consideration of the interaction between bubbles.A model including multiple bubble sizes was applied to obtain more accurate simulation results of gas holdup and water velocity.Also,the use of the inhomogeneous multiple-size-group(MUSIG)model was explored as a way to better predict the complex flow regimes.The modelling method was applied to a laboratory internal loop airlift reactor,and the simulation results were compared with the published experimental measurements for gas holdup and water velocity.Reasonable agreement was obtained over a range of operating conditions,and an improvement was demonstrated using the proposed method.The simulations have shown that the inhomogeneous MUSIG model is a suitable tool to describe the complex gas-liquid interaction in the airlift reactor at a high gas superficial velocity.
基金supported by the National Key Research and Development Program of China(No.2022YFB3304902)the NSFC Joint Fund of China(No.U2202253)+2 种基金the 2023 Cutting-Edge Interdisciplinary Project of Central South University,China(No.2023QYJC007)Yunnan Province Science and Technology Planning Project of China(No.202202AB080017)the Key Fund of the National Natural Science Foundation of China(No.62133016).
文摘Under the carbon peaking and carbon neutrality goals,the aluminum electrolysis industry faces significant challenges in energy conservation,carbon reduction,and environmental protection.Utilizing new energy electricity is one of the most effective ways to reduce carbon emissions in primary aluminum production.This paper addresses the issue of uncontrolled thermal balance in electrolysis cells caused by large fluctuations in current during the absorption of new energy electricity.A multi-field solidification model of the electric-thermal-flow facing the side-controlled heat dissipation structure is constructed.The study investigates the impact of large current fluctuations,heat exchanger structure and different process parameters on the temperature field and the ledge shape of the electrolysis cell.The optimum process and corresponding heat dissipation structure of electrolysis cell under current fluctuations are summarized to provide theoretical guidance for aluminum electrolysis cells in absorbing fluctuating new energy electricity.The results indicate that the aluminum electrolysis cell with controlled heat exchange with standard rectangular carbon blocks,high-insulation irregular carbon blocks(inner lining type 2),and heat-conducting protrusions with a thermal conductivity of 120 W·m^(-1)·K^(−1) can effectively absorb current fluctuations ranging from−20%to+20%within a short period under collaborative control of side thermal regulation and processes.However,when maintaining a+20%current for prolonged periods,the cell may still experience uncontrolled thermal balance.
基金supported by the National Key Research and Development Program of China(No.2023YFC2907400)the National Key Research and Development Program of China(No.2023YFC2907401).
文摘Currently,global mining development has entered a new historical stage.With the gradual depletion of shallow mineral resources and the increasing proven reserves of deep resources,deep resource exploitation has become a global mining trend.However,deep resource development faces numerous challenges,including product pricing,production costs,operational safety,and workforce issues.The development of intelligent and even autonomous mining technologies,along with the establishment of smart mines,has become an inevitable choice for deep resource exploitation.A key technology and complete technical system for underground metal mine intelligent mining is constructed around the mining production process,with"ore flow"as the main thread.With"equipment intelligence","precise positioning","real-time dispatching","high-speed communication",and"continuous mining"as the main themes,remote,tele-controlled,and intelligent mine production management is being realized.The achievements are demonstrated in engineering applications at multiple mining enterprises,providing theoretical foundations and technical support for the intelligent,efficient,and green development of deep mineral resources.
基金supported by the Science and Technology Program of Jiangxi Education Committee,China Scholarship Council and CSIRO(No.GJJ150613).
文摘Flow field and mixing characteristics in a flotation cell not only significantly affect particle suspension,reagent distribution and froth stabilization,but also determine the flotation efficiency.This work aims to obtain a detailed understanding of the water flow in a flotation cell through combined use of single-phase computational fluid dynamics(CFD)model and advanced laser measurement using particle image velocimetry(PIV).The CFD model has been set up to simulate a 1.58 m3 industrial Wemco flotation cell.Following model validation using PIV measurement data taken at several representative planes,the flow dynamics in the flotation cell have been analysed in terms of flow pattern and velocity.The results show that good agreement is achieved between predicted and measured time-averaged flows,with three annular recirculation zones observed.In the mixing characteristics simulations,tracer injection was used to investigate the mixing time,residence time distribution and local flow patterns.Mixing time is found to decrease with increase in rotor speed,proportional to the inverse of speed,as expected for fully turbulent flow.As the through-flow rate increases,the residence time decreases,proportional to the inverse of flow rate,as expected.The position of the cell tailings underflow outlet has a significant influence on the lower recirculation zone,altering the flow patterns and the extent of recirculation within the system.
基金supported by the National Natural Science Foundation of People's Republic of China(NSFC,No.52174246)the Open Foundation of State Key Laboratory of Mineral Processing(No.BGRIMM-KJSKL-2024-03)+2 种基金the Major Science and Technology Projects in Yunnan Province(No.202202AB080012)Guangxi Natural Science Foundation,China(No.2022GXNSFFA035035)Guangxi Science and Technology Major Program,China(Nos.GKAA23073017 and GKAA23023034).
文摘As an impurity compound in concrete,kaolin could adsorb much polycarboxylate ether(PCE)superplasticizer,depressing the efficiency of PCE significantly.The mechanisms underlying the absorption of carboxylic acid(PCE-C),sulfonic acid(PCE-S),and phosphoric acid(PCE-P)groups on the kaolin(001)surface were investigated by adsorption capacity test and density functional theory(DFT).Adsorption experiments show that the adsorption capacity of PCE-P on kaolin is significantly higher than that of PCE-S,while PCE-C shows the least adsorption on kaolin.Based on DFT calculations,the interaction between PCE-P and the kaolin(001)surface is the strongest,mainly due to the formation of a strong chemical bond,along with hydrogen bonding and electrostatic interactions.However,no stable chemical bonds form between the kaolin(001)surface and either PCE-S or PCE-C.Additionally,the adsorption energy suggests that PCE-S has a higher adsorption capacity on the kaolin(001)surface than PCE-C.Studies have shown that kaolin exhibits a strong adsorption capacity for the phosphoric acid.This adsorption significantly reduces the dispersing ability of the superplasticizer,thereby impairing its effectiveness in reducing water demand and improving workability in concrete.This study aims to provide a theoretical basis for choosing PCEs and improving their efficiency when producing concrete made with kaolin-involved aggregates.
文摘In recent years,with the deterioration of mineral resource endowment and the development of intelligent technologies,traditional flotation machine technology has been rapidly integrated with cutting-edge technologies,such as modern sensing,artificial intelligence,big data,and the Internet of Things.This integration aims to improve the efficiency and controllability of the flotation process,thereby driving the transformation of the mineral processing field toward intelligent,automated,and green directions.However,as a new development,intelligent flotation machines have not yet achieved a unified and clear understanding.This study interprets intelligent flotation machines from three aspects:definition,connotation,and development path.The core characteristics of intelligent flotation machines have been proposed,including self-sensing and self-diagnosis abilities in the whole spatial domain,data-based intelligent control algorithms,predictive maintenance of core components,and coordination of global and local optimization in flotation processes.This study identifies the current challenges faced by intelligent flotation machines,and proposes the future development paths,including enhancing the comprehensive monitoring and intelligent regulation of flotation parameters,improving equipment fault prediction and precise localization,and achieving unmanned operations and intelligent maintenance.By continuously optimizing and refining the design and application of intelligent flotation machines,they can play an increasingly important role in the sustainable development of the mining industry.
