Abstract:To address the challenges in coal preparation plants,including the difficulty of integrating multi-source heterogeneous data,the low efficiency of traditional manual data management,and significant data silos,a production data integration system was developed based on Python and Selenium to enhance production man-agement efficiency and data application value.The system adopts a four-layer architecture encompassing data acquisition,processing,storage,and application.It collects both directly displayed webpage data and data dis-played within ActiveX controls through automated login,web scraping,and screenshot recognition.The ac-quired data then undergoes time-series processing,parsing,deduplication,and OCR(Optical Character Recogni-tion)identification to achieve structured storage.This system implements functionalities such as segmented pro-duction data comparison,data alerts and anomaly alarms,visual analysis of time-series production data,and cross-system automatic data pushing.This technical solution has successfully achieved the integration and pre-liminary analytical application of heterogeneous industrial data across different systems,effectively resolving the problem of data silos inherent in traditional production management systems.It provides a feasible path for data integration in legacy information systems and serves as a practical example for the coal preparation in-dustry in deep data mining,collaborative application of industrial knowledge,and digital transformation.
Abstract:Aiming at the problems in the coal slime filter pressing process,such as excessively long pressing time,the difficulty of achieving real-time moisture detection throughout the process with existing filter presses,high energy consumption,and long filtration cycles,this study investigates the variation pattern of moisture re-moval from filter cake with pressing time.Using an automatic constant-stress testing machine,the evolution characteristics of dewatering for coal slime filter cakes under different particle sizes,slurry viscosities,and pres-sures were analyzed.A mathematical derivation model based on filtrate flow rate and filter cake moisture was established,and a moisture detection device for coal slime filter cake was designed to achieve real-time mois-ture monitoring throughout the filter pressing process.Experimental results show that under the same pressure,filter cake moisture decreases as coal slime particle size increases.Higher slurry viscosity makes dewatering dur-ing filter pressing more difficult.Increasing the filter pressing pressure can effectively reduce filter cake mois-ture.When the feeding pressure increased from 5 MPa to 10 MPa,the filter cake moisture decreased from 24.7%to 13.7%.Compared with traditional coal slime moisture detection methods,the developed moisture detection device achieves an accuracy of up to 97%.The research results provide theoretical and practical technical sup-port for reducing energy consumption in the coal slime filter pressing process,improving solid-liquid separation efficiency,and advancing intelligent coal slime filter pressing.
Abstract:To address the technical challenge of efficiently separating fine,difficult-to-wash coal slime using tra-ditional flotation methods,a study was conducted using coal slime from the Guandi Coal Preparation Plant as the sample.A self-developed nanobubble flotation system was employed to investigate the stability of hydrodynam-ic cavitation-generated nanobubbles and their impact on flotation performance,while also analyzing the mechan-ism by which nanobubbles enhance fine particle separation.The results indicate that after more than 5 minutes of cavitation treatment,nanobubble size stabilized within the range of 175～225 nm,maintaining high stability even after one hour of standing.In the presence of nanobubbles,a frother dosage of 0.3 kg/t achieved a combust-ible recovery rate of 91%,representing a 25%reduction in frother consumption compared to conditions without nanobubbles.When the aeration rate ranged from 0.4 cm/s to 1.6 cm/s,the combustible recovery rate in the nan-obubble-containing slurry system remained around 90%.However,the ash content of the product increased with higher aeration rates,attributed to nanobubbles enhancing the hydrophobicity of coal particles while simultan-eously increasing the probability of attachment of less hydrophobic mineral particles.Nanobubbles exhibit ex-cellent stability and significantly enhance coal slime flotation through multiple mechanisms,including promot-ing the aggregation of fine particles,capillary bridging,and destabilizing liquid films.This provides a new path-way for the efficient separation of fine-grained coal.
Abstract:Due to single-unit upsizing and increased quantity,the failure rate of vibrating screens in large coal preparation plants has risen.Maintenance work primarily focuses on emergency repairs,while existing research is predominantly experimental,with monitoring points mainly concentrated on damping springs.In response to the aforementioned issues,and using the Xiaobao Dang Coal Preparation Plant as the application scenario,a smart monitoring and diagnostic system for vibrating screens was developed based on an analysis of the screen's structure and motion characteristics.The system is designed for practical industrial environments and consists of three layers:data acquisition,analytical diagnosis,and application.Key measurement points were selected at four critical locations on the exciter and screen side plates.Hardware such as uniaxial/triaxial IEPE acceleromet-ers and temperature sensors were deployed to collect vibration and temperature signals.By analyzing trends in data patterns such as acceleration,temperature,and motion trajectory,the system provides feedback on the oper-ational status of the vibrating screen,classifying it into different health and degradation levels.The results show that after the system was put into use,it successfully gave early warnings of the failure of the shock-absorbing spring at the rear end of the non-driving end and a universal shaft fault of the 2214#raw coal classifying screen,and the equipment returned to normal after maintenance.The number of unplanned shutdowns decreased from 2 times before the system was put into use to 0,the fault duration was reduced by 120 minutes,the daily mainten-ance workload decreased by 33.33%,and the operating duration increased by 0.65%.The system has effectively realized real-time monitoring and fault early warning of vibrating screens,promoted the transformation of main-tenance work from passive emergency repair to active prevention,improved the equipment management level of coal preparation plant,and provided a useful reference for intelligent transformation of the industry.
Abstract:As a critical energy source and chemical raw material in China,coal plays a vital role in economic de-velopment,making its rational exploitation and utilization highly significant.Currently,the coal industry is un-dergoing a crucial period of intelligent transformation,and the intelligentization of coal preparation plants rep-resents a core step in shifting the sector from being"labor-driven"to"technology-driven."Ash content,being one of the key indicators for evaluating coal product quality,requires real-time and accurate detection,which has become essential for achieving clean and efficient utilization of coal.The article outlines the principles and oper-ational procedures of offline coal ash content detection technologies,represented by the slow ashing method and rapid ashing method.It points out that while these methods offer high accuracy,they suffer from limitations such as complex operations,long processing times,and the inability to provide real-time ash content data.Currently,they are primarily used to verify the accuracy of online detection methods.The article also introduces five types of online ash detection technologies based on γ-rays,X-rays,natural coal radioactivity,neutron activation,and laser techniques.It elaborates on the structural features,working conditions,and performance indicators of vari-ous online ash analyzers,with a focused comparative analysis of their advantages and disadvantages in practical applications.Analysis indicates that different online ash detection technologies vary in applicable scenarios,per-formance advantages,and limitations due to differences in their working principles.The selection of an appro-priate method must comprehensively consider specific application contexts,coal characteristics,and costs.Fi-nally,it is pointed out that the future development of coal ash detection technology should focus on intelligence,precision,and efficiency.Efforts should be made to enhance technical capabilities and support the coal industry by strengthening R&D to drive innovation,deeply integrating with intelligent coal preparation plant systems,es-tablishing comprehensive technical standards and regulations,prioritizing safety and environmental protection,and fostering talent development.This article can provide valuable insights for coal enterprises and related re-searchers in selecting appropriate ash detection methods.
Abstract:To fundamentally eliminate potential safety hazards,reduce occupational health risks,improve the on-site environment of coal slime flotation,enhance flotation efficiency and resource utilization,and fundamentally resolve the issue of"heavy medium ash carry-over"caused by fluctuations in clean coal ash content,this study adhered to the principles of safety,environmental protection,efficiency,and economy.Through comparative laboratory-scale flotation tests and industrial trials on coal slime at Shaqu Coal Preparation Plant,the physico-chemical properties of the new collector DVS4U058 and frother N88050 were investigated to verify their adapt-ability,separation efficiency,and economic feasibility.The results demonstrated that DVS4U058 and N88050 fully comply with green standards for coal flotation reagents.Their enhanced safety and environmental perform-ance better ensured safe production and improved the operational environment during flotation,while exhibiting higher selectivity.The comprehensive unit consumption of reagents decreased by 28.57%year-on-year,the clean coal yield increased by 2.59 percentage points,the standard deviation of clean coal ash content was only 0.11%,and the tailings ash content increased by 1.98 percentage points.Compared with the existing process,the annual sales revenue could increase by 8.845 6 million yuan,demonstrating significant environmental and eco-nomic benefits.
Abstract:To address the issues of limited processing capacity and low intelligence level of existing dry separat-ors,which are difficult to meet the coal separation requirements of modern large-scale coal mines,a large-scale intelligent dry separator is developed.This equipment adopts a crank-rocker drive mode,is equipped with a 32 m2 large sorting bed and a serrated screen plate,and integrates a air supply and dust removal system,which can achieve efficient sorting.Meanwhile,intelligent technologies such as fault diagnosis technology,process control systems and digital twin platforms is developed,enhancing the intelligence level of the equipment.These tech-nologies are put into operation and application at the Shengli No.1 Open-pit Coal Mine of the National Energy Group.The research and application results show that its maximum processing capacity can reach 600 t/h when sorting fine coal ranging from 0 to 25 mm,and 500 t/h when sorting lump coal ranging from 25 to 70 mm,which is about 40%higher than that of the existing dry separator.The dust emission concentration is lower than 10 mg/m3,far below the national standard.The electricity consumption per ton of coal is approximately 1.37 kW,achieving energy savings of over 30%.The gangue content of the selected block clean coal is 1.85%,that of the fine clean coal is 2.52%,and that of the clean coal in the gangue is 2.49%.The separation performance is good.The annual income increases by 32.917 million yuan,with remarkable economic benefits.Moreover,the intelli-gent system significantly reduces the number of production personnel and their labor intensity,effectively solv-ing the problem of lignite sorting.
Abstract:To address the limitations of non-radioactive meter with single γ-ray detectors—including inad-equate counting rates for low natural radioactivity coal ash measurement,inability to cover the full cross section of wide conveyor belts,and susceptibility to environmental background fluctuations—a non-radioactive meter with multiple γ-ray detector is developed to enhance system performance and applicability.A 2 L NaI(Tl)scin-tillation detector is selected as the core detection component;each unit can be configured with up to 4 main de-tectors and 1 environmental background detector.The main detectors are flexibly arranged in transverse linear arrays or longitudinal area arrays,while the environmental detector is used to compensate for background inter-ference.Transverse linear arrays cover 1 800～2 200 mm bandwidth,and longitudinal area arrays extend the measurement zone.TEC semiconductor temperature control technology ensures detector temperature fluctu-ations remain below 0.5℃ within 24 hours.Dynamic regulation of high-voltage power supply improves the sta-bility of the non-radioactive meter,stabilizing the 1 460 keV characteristic energy peak around channel 256 in the multi-channel spectrum.Comparative single/double-detector tests are conducted on low-radioactivity coal samples from Eastern Inner Mongolia,and measurement resolution,precision,and long-term stability evalu-ations are performed on clean coal samples from Guangwang Mining Area.Results indicate that compared with single detector systems,dual detector meters achieve effective measurements.For low-radioactivity coal samples,the counting rate of dual detectors is approximately twice that of single detectors,the random error is reduced to 70％.In clean coal measurements,the unit load counting rate is 0.75 cps,with a 5.4 cps increase per 1％ash content rise.Measurement precision reaches 0.19％,and linear correlation(R2)is 0.96.Regarding long-term stability,the range of 24-hour continuous measurements is lower than 0.18％,fully meeting on-site applica-tion requirements.By enhancing counting rates,expanding detection coverage,and optimizing environmental compensation,the non-radioactive meter with multiple γ-ray detectors satisfies measurement demands for low-radioactivity coal and high-precision clean coal,demonstrating significant performance advantages and consider-able market potential with promising application prospects.
Abstract:To reveal the influence mechanism of water quality on coal slime flotation effect during coal prepara-tion,this study focuses on key water quality parameters such as ion types(Ca2+and Al3+),ion concentration,pH value,and water hardness.It systematically investigates their action laws on the coal particle-bubble encapsula-tion angle and verifies the correlation between the encapsulation angle and flotation effect through tests.The re-search uses high-speed camera technology to capture the collision and adhesion process between coal particles and bubbles,characterizes the interaction intensity between them by measuring the encapsulation angle,and syn-chronously analyzes the changes in clean coal yield,ash content,and flotation perfection index under different water quality environments.The results show that:with the increase of ion concentration,the coal particle-bubble encapsulation angle shows an upward trend,among which the coking coal particle-bubble encapsulation angle responds most obviously.During the flotation process,a lower ion concentration(<10-4 mol/L)should be maintained,at which a higher flotation clean coal yield and qualified ash content can be obtained;within the pH range of 5～9,the encapsulation angle is less affected by pH value,reaching the highest value at pH=7,and slightly decreasing when pH>7.The flotation clean coal yield first increases and then decreases with the in-crease of pH value.When the solution is neutral or weakly alkaline(pH=7 or 8),a higher flotation clean coal yield and lower clean coal ash content can be obtained;water hardness has a significant impact on the encapsula-tion angle,which reaches the maximum value when the water hardness is about 400 mg/L.When the flotation water hardness is 300～400 mg/L,the flotation clean coal yield of the three coal types is relatively high,reach-ing more than 75.00%,with lower clean coal ash content and relatively high flotation perfection index.There-fore,flotation can be carried out in an environment with lower water hardness.This study clarifies the internal mechanism by which water quality parameters affect the flotation performance by regulating the encapsulation angle,providing theoretical support and practical guidance for optimizing coal slime flotation process paramet-ers and improving separation efficiency.
Abstract:In order to more effectively reduce medium consumption in coal preparation plants,experimental study is conducted with the currently used HDMA-6 counter-current wet drum magnetic separator,with angle of magnetic system and recovery rate of magnetic matters as test factor and indicators,respectively.As indicated by test results,within the testing range,despite the fact that a high recovery of magnetic matters of around 98%on average can be obtained at different angels of the magnetic system,there still exist some differences in the re-covery rate when the angle varies;the magnetic system should be most preferably set at an angle of 10° for clean coal/middling magnetic separator and set at angle of 5° for refuse magnetic separator,and,in this case,the two separators can respectively see a rise of recovery rate by 0.23 and 0.24 percentage points as compared with the case that the angle remains at an unchanged state of 0.The research findings can help improve the efficiency of magnetic separator from a long-term point of view.
Abstract:Slurry preprocessing is a crucial step before flotation operations,and its energy input significantly af-fects the adsorption efficiency between coal slime particles and flotation reagents.An orthogonal experimental method is used to investigate the optimal process conditions for conditioning time,mechanical agitation intens-ity,and collector dosage in flotation kinetics.A flotation kinetics energy adaptation model for different particle size fractions of coal slime is established.Utilizing the orthogonal experiments,the significant impact of condi-tioning time,agitation intensity,and collector dosage on flotation performance is determined,and the optimal factor levels are identified.The order of significance is:conditioning time>collector dosage>stirring speed.The optimal factor combination scheme for conditioning is determined as:conditioning time of 40 s,collector dosage of 2.07 g per ton of dry coal slime,and stirring speed of 500 r/min.The influence of each factor on flota-tion indicators is also examined.The research results aim to provide theoretical guidance for reducing energy consumption and reagent waste in coal slime flotation,and to offer theoretical support for coal preparation plants to improve flotation equipment and enhance flotation efficiency.
Abstract:Flotation serves as the primary method for separating fine coal slime in coal preparation.Condition-ing pretreatment achieves dispersion and homogenization of coal particles and reagents,promotes reagent ad-sorption on particle surfaces,creates favorable conditions for coal slime flotation,and enables efficient separa-tion.To improve flotation conditioning performance and enhance flotation efficiency,this study examines the turbulent forced mixing conditioning process modification at the Coal Preparation Plant of Pingdingshan Tian'an Coal Mining Co.Ltd.(No.8 Mine).Using computational fluid dynamics,the operational features and flow field characteristics of the turbulent forced mixing conditioner are analyzed.Industrial flotation comparative trials val-idated the production effectiveness and feasibility of this modification.The study shows that the conditioner gen-erates a uniform,high-velocity flow field with intense turbulent motion,strengthening particle-reagent interac-tions and improving conditioning efficiency.Production results show that significant flotation performance im-provements:clean coal yield increased by nearly 1 percentage point while maintaining comparable clean coal ash content,alongside substantial reductions in reagent consumption—collector dosage decreased by 53.12%and frother dosage by 30.55%.These findings confirm that the turbulent forced mixing conditioner significantly enhances particle-reagent interaction efficiency,establishing optimal conditions for flotation separation.This ef-fectively resolves the issues of high reagent consumption and low flotation efficiency at the No.8 Mine plant caused by deteriorating feed slime properties.
Abstract:In order to explore the influence mechanism of lattice Mg(Ⅱ)impurities on the surface charge char-acteristics of kaolinite,Mg(Ⅱ)doped kaolinite is taken as the research object.The surface charging charac-teristics of Mg(Ⅱ)doped kaolinite are systematically studied by means of Density Functional Theory(DFT)and classical Molecular Dynamics(MD).The molecular simulation results are verified by Zeta potential test-ing of Mg(Ⅱ)doped kaolinite samples.The results of DFT show that:after Mg(Ⅱ)doping,the Mg and ad-jacent oxygen atoms have formed new bonds,which makes the charge on the surface of kaolinite rearrange and present a negative charge.MD results show that the negative surface charge of kaolinite(001)surface increases and the surface potential decreases with the increase of Mg(Ⅱ)doping.The results of Zeta potential test show that the deprotonation on kaolinite basal and edge surfaces gradually increases with the increase of solution pH value,and the surface potential of kaolinite surfaces with different Mg(Ⅱ)doping amounts gradually de-creases,and the negative surface charge of Mg(Ⅱ)doped kaolinite is significantly stronger than that of pristine kaolinite.The experimental results are in good agreement with the molecular simulation results,which verifies the accuracy of the simulation.The influence mechanism of Mg(Ⅱ)doping on the charge of kaolinite is mainly that the negative charge characteristics of kaolinite surface are significantly enhanced,and the elec-trostatic repulsion between particles is enhanced,resulting in difficult agglomeration of fine kaolinite particles.The research can provide further theoretical support for the efficient sedimentation of coal slurry.
Abstract:To address the challenge of producing ultra-low ash silicon coal from raw coal through single-pass washing on a large scale,Qinhua Coal Preparation Plant replaces the original two-stage three-product jig with an SKT four-stage five-product jig and implements corresponding modifications to the production process.Through systematic analysis of the characteristics of coal for industrial silicon smelting,raw coal quality,jig structure,separation efficiency,production data before and after the upgrade,and product economic benefits,the results indicate that the upgraded jig delivers stable product specifications,flexible variety adjustment,and a separation efficiency of 89.91%.This upgrade generates an additional annual economic benefit of approximately 147 mil-lion yuan for the enterprise,fully achieving the expected transformation goals.Production practice confirms that using a four-stage jig for single-pass washing of raw coal to produce ultra-low ash silicon coal is economically viable.This approach not only resolves issues associated with secondary rewashing of traditional jig clean coal,such as secondary transportation,secondary fine coal generation,and capacity occupation,but also avoids mag-netite powder contamination compared to dense medium separation.Furthermore,it offers advantages of low production maintenance costs and a simplified process flow.This innovative technology opens a new pathway for ultra-low ash silicon coal production,demonstrating high potential for widespread application and promising market prospects.
Abstract:In order to construct an efficient,low-cost and highly adaptable coal blending solution that tackles the problems of traditional methods such as reliance on artificial experience,computational complexity,and poor result stability,a structured coal blending model based on adaptive particle swarm optimization algorithm is pro-posed.This study takes coal samples of different grades from seven mining areas in the Cixi Coalfield.First,the coal quality indicators of blended coal—including ash content,moisture content,volatile matter,sulfur content,and caking index—are experimentally measured to analyze prediction errors in the traditional linear weighting model.For the indices of ash content,volatile matter and sulfur content,a linear prediction model is used for prediction;aiming at the nonlinear characteristics of the caking index,the principle of support vector machine is introduced to construct a nonlinear prediction model containing Gaussian function terms.Subsequently,a multi-constraint coal blending structural model is established with the objectives of minimizing costs,minimizing high-quality coal ratio,and maximizing low-quality coal ratio,Genetic Algorithm(GA),Particle Swarm Optimiza-tion(PSO1),and Adaptive Particle Swarm Optimization(PSO2)are used for solution optimization,with a focus on improving the dynamic adjustment strategies of learning factors and inertia weights.The results show that:ash content,volatile matter,and sulfur content can be effectively predicted by the linear prediction model(R2 values close to 0.9).The R2 value of the nonlinear prediction model for the caking index reaches 0.927,signific-antly better than traditional weighted formula.In model solutions,the PSO2 algorithm demonstrates superior performance compared to GA and PSO1,exhibiting reduced prediction errors and achieving the lowest coal blending cost of 1 502.80 yuan.Furthermore,the iterative process of PSO2 is more stable,with performance es-sentially stabilizing after 20 generations.The structured coal blending model based on adaptive particle swarm optimization algorithm can effectively achieve high-precision prediction of blended coal quality indicators and rapid optimization of blending schemes.It can provide technical support for the clean and efficient utilization of coal and has good engineering application value.
Abstract:With an aim to tackle the problems faced by Tianchen Coal Mine's coal preparation plant in opera-tion of its flotation process control—including limited diversity in agent adjustment,unstable indicators,agent waste and high manual labor intensity—an autonomous adjustment system based on multivariable coupling technology is developed.Based on the production status of the flotation system at Tianchen coal preparation plant,the multivariable coupling relationships in coal slime flotation are analyzed,clarifying the interactions among factors such as feed characteristics and agent regime.An integrated measuring device for pulp level and froth layer limit is developed to achieve real-time collection and monitoring of pulp level and froth thickness.The automation upgrade of the pulp level adjustment mechanism is carried out to stabilize pulp level control in flotation cells.An aeration rate measurement and control device is added to enable online regulation of aeration volume.Multiple systems are integrated to establish an autonomous adjustment system for flotation process con-trol,with a B/S architecture-based software platform developed for centralized monitoring and data interoperab-ility.A product index prediction model is constructed to forecast tailings ash content,achieving a mean square error of 1.56%in tailings ash prediction.Application results demonstrate that the overall flotation performance improved significantly after implementing the autonomous adjustment system for flotation process control.The clean coal yield increased from 76.36%to 77.42%,generating an annual economic benefit of 1.285 million yuan.Through the implementation of this adjustment system,Tianchen coal preparation plant effectively optim-izes flotation performance.Despite deteriorating feed coal quality,the system enhances product stability,re-duces reagent consumption,and increases clean coal yield,delivering tangible economic,social,and environ-mental benefits.With future improvements in ash data reliability,the predictive model precision is projected to advance continuously,providing robust support for intelligent flotation process upgrades.
Abstract:The physical characteristics of low-moisture coal slime—marked by a low Proctor hardness(～1)and weak shear resistance—are analyzed to assess its crushability.A comparative evaluation of jaw crushers(low ef-ficiency),hammer crushers(prone to adhesion),and toothed roll crushers supports the selection of a shear-dom-inant double-tooth roll crusher.The design integrates anti-adhesion comb-tooth structures and a spiral tooth-roll configuration.A processing capacity model is developed by introducing a void-filling coefficient(K=0.2)and single-pass crushing efficiency(e=0.75).Motor power parameters are calculated based on the shear strength of the coal slime.Results indicate that the effective working length of the rolls is 1.5 m,with staggered spiral teeth enhancing agitation and overturning.Theoretical throughput reaches 390.8 t/h,and a 90 kW motor(or dual 45 kW motors)is recommended.The system effectively addresses material adhesion and blockage issues,deliver-ing uniform particle size suitable for blending with lump coal.Tailored for the properties of low-moisture coal slime,the crusher combines shear-based breaking with anti-adhesion design.It overcomes the limitations of con-ventional equipment and provides a scalable,practical solution for efficient coal slime crushing.
Abstract:As one of the main energy sources in China,coal is in urgent need of industrial transformation driven by the"dual-carbon"strategy.As a key link in coal processing,the automation and intelligence transformation of coal preparation plants holds significant strategic importance for enhancing energy utilization efficiency,pro-pelling corporate upgrading,and achieving sustainable development in the industry.In response to the develop-ment needs of the coal preparation industry under this context,this study elaborates in detail on the definitions and distinctions between automation and intelligence.It systematically reviews the current application status of automation technology in heavy medium coal preparation processes,dewatering processes,flotation processes,and supervision and management process,alongside the implementation of intelligent technologies in heavy me-dium coal preparation,flotation,coal slurry water treatment,and information systems.Concurrently,it analyzes the application status of key equipment such as ash content monitors,density monitors,and various sensors.The article argues that the application of automation technology in coal preparation not only significantly enhances production efficiency and reduces manual involvement,but also improves product quality while strengthening the stability and safety of the entire production process.Intelligent technologies are also being progressively im-plemented across key stages of the coal preparation process,representing a necessary step to respond to evolving market demands and address resource management challenges.They not only significantly elevate the intelli-gence and informatization levels of production processes but also drive the transformation of whole industry to a more efficient and green direction;Detection instruments and equipment in coal preparation have progressively undergone transformation toward automation,intelligence,systematization,and comprehensiveness,enhancing production stability and safety while boosting separation efficiency and precision.In the future,the automation and intelligent transformation of coal preparation process needs to establish a unified intelligent platform,pro-mote the application of machine learning and cloud services,improve the standardization of intelligent coal pre-paration and talent training system,so as to help the coal preparation industry to achieve efficient,green and sus-tainable development,and promote the upgrading and reform of the whole coal industry.
Abstract:The movable-bed jig used by Longquan Plant for removal of gangue from raw coal is seen to have the problems of high loss of coal in reject product and high fault rate of equipment.To deal with this situation,work is made on technical transformation of the gangue preremoval process.Based on the results of technical survey of related equipment and raw coal analysis,the plant opts to use an intelligent dry separator operating with dual-energy X-ray plus image recognition technology to replace the movable-bed jig.Field application of the separat-or shows that when used for treating the 200～50 mm lump coal,the gangue in the coal can be effectively re-moved with a removal rate of>90%and a loss of coal in gangue of<2%,down 9 percentage points from the figure in the case of using the movable-bed jig;and the separator works remarkably well,creating an extra rev-enue of about 19.487 9 million yuan per year for the plant.The technical transformation work can serve as a ref-erence for other plants facing similar problems in raw coal pretreatment process.