Laboratory-scale Ball Mill Research Articles

Research on an Accurate Simulation Modeling and Charge Motion Quantitative Evaluation Method for Ball Mill in Confined Space

A ball mill is a type of complex grinding device. Having knowledge of its charge-load behavior is key to determining the operating conditions that provide the optimum mill throughput. An elaborate description of the charge movement inside the ball mill is essential. This study focuses on a laboratory-scale ball mill and utilizes a discrete element simulation model to investigate the impact of mill speed and ball filling on charge-load behavior. Initially, the EDEM 2.7 (Engineering Discrete Element Method) software contact parameters were calibrated through heap-angle experiments. Subsequently, four charge-motion characteristic parameters were defined and analyzed based on Powell’s theory to understand the variations in charge-load behavior. This research proposes a theoretical calculation model for predicting power in a ball mill, highlighting the significance of the CoC (Center of Circulation) and CoM (Center of Mass) in reflecting changes in charge-load behavior. The theoretical model for mill-power prediction is effective and aligns well with the EDEM simulation and experimental results, providing valuable insights for optimizing large-scale ball mill structures and controlling charge motion during production.

Thermodynamic limits of the depolymerization of poly(olefin)s using mechanochemistry.

Mechanochemistry is a promising approach for chemical recycling of commodity plastics, and in some cases depolymerization to the monomer(s) has been reported. However, while poly(olefin)s comprise the largest share of global commodity plastics, mechanochemical depolymerization of these polymers in standard laboratory-scale ball mill reactors suffers from slow rates. In this work, the observed reactivities of poly(styrene), poly(ethylene) and poly(propylene) are rationalized on the basis of thermodynamic limitations of their depolymerization by depropagation of free radical intermediates. In addition, subsequent phase partitioning equilibria for the removal of monomers from the reactor via a purge gas stream are discussed for these polymers. For poly(styrene), a typical vibratory ball mill supplies just enough energy for its depolymerization to be driven by either thermal hotspots or adiabatic compression of the impact site, but the same energy supply is far from sufficient for poly(propylene) and poly(ethylene). Meanwhile, removal of styrene from the reactor is thermodynamically hindered by its lower volatility, but this is not an issue for either propylene or ethylene. The implications of these thermodynamic limitations for mechanochemical reactor design and potential for mechanocatalytic processes are highlighted.

Assessment of the viability of pozzolanic activity of copper slag for use as supplementary cementitious material in ordinary Portland cement

The production of ferrous as well as non-ferrous metals generates slag as a byproduct material. Ferrous slags are extensively used in the construction sector as supplementary cementitious material (SCM) and aggregates. In this regard, the investigation of potential applications in similar areas for slags derived from the production of non-ferrous metals can help to address issues associated with their disposal, dumping, environmental concerns, etc. The primary aim of this research is to assess the pozzolanic activity of copper slag (CS), a type of non-ferrous slag. This investigation is conducted to replace a portion of ordinary Portland cement (OPC) incorporating CS as an SCM for sustainable construction. To assess the reactivity of the CS, comparisons were drawn with a known pozzolanic material fly ash (FA), and an inert material quartz powder (QP). The processing of raw CS (granular material) was carried out using a laboratory scale ball mill to achieve varying fineness to evaluate the effect of specific surface area (SSA) on reactivity. Initially, the investigations were conducted on paste samples of OPC-CS and suspensions of CS-calcium hydroxide (CH) and were later extended to mortar studies. Mechanical characteristics such as compressive strength and open porosity of mortar specimens were determined to correlate with the paste studies results. The findings suggest that CS does exhibit pozzolanic characteristics although its reactivity is comparatively lower than that of FA. An increase in the fineness of the CS resulted in enhanced pozzolanic activity. Analysis of the hydrated suspension samples showed the formation of Fe-siliceous hydrogarnet phase indicating “Fe” from CS was involved in the reaction with CH. Although OPC-CS mortar samples exhibited similar open porosity compared to OPC-QP mortar samples, the interfacial transition zone (ITZ) porosity in mortar samples of OPC-CS was observed to be reduced indicating the densification of the region due to the pozzolanic reaction of CS. The permissible replacement of OPC with CS as a substitute for FA can be adjusted according to the material's fineness and the desired compressive strength.

Heterogeneous selective ensemble learning model for mill load parameters forecasting by using multiscale mechanical frequency spectrum

A ball mill is a heavy mechanical device and its safe operation affects the entire grinding process. Mill load is a key index in the optimum operation of the grinding process, but it cannot be measured directly. In industrial practice, operational experts normally estimate its value based on their experiences and the mechanical signals produced by the ball mill. In this paper, we proposed a heterogeneous selective ensemble method by using a multiscale mechanical frequency spectrum. The multicomponent adaptive decomposition algorithm is first used to decompose the original shell vibration and acoustic signals into sub-signals with different timescales. Then, selective ensemble (SEN) kernel projection to latent structure algorithm is used to model the spectral data of these sub-signals. Furthermore, the latent features of multiscale spectra are extracted to construct SEN models based on fuzzy inference. Finally, the two types of heterogeneous SEN models are fused by using information entropy. The main contribution of this study is that the proposed soft-sensing model has a dual-layer ensemble structure that can fuse multi-source information in different mechanical sub-signals with physical meaning. Moreover, the proposed model can simulate the fuzzy cognitive behavior of domain experts in the mineral grinding process. The effectiveness of the method is verified by the shell vibration and acoustical data of a laboratory-scale ball mill.

EFFECT OF GRINDING MEDIA ON THE MILLING EFFICIENCY OF A BALL MILL

The size of grinding media is the primary factor that affects the overall milling efficiency of a ball mill (e.g. power consumption and particle size breakage). This article tackles the lack of a design tool that could help choose the ball loading composition in mills. Such a tool enables the maximization of the exposed surface area per unit energy (cm2/J). The effect of ball load composition, by varying the grinding media size distribution (e.g. alternatively by mixing four groups of 19.5, 38 mm; 19.5, 50 mm; 38, 50 mm and 19.5, 38, 50 mm), on the milling efficiency of a laboratory scale ball mill has been investigated in this article concerning ball number, total surface area, and ball weight. The results reveal that the amount of required energy is close in values, per each ball loading mixture, concerning three characteristic parameters. The amount of required energy varies between 3.22 kWh/st & 3.65 kWh/st. Moreover, the new surface area per unit energy (e.g. cm2/J) significantly influences milling efficiency. In contrast, the ball weight has a minor effect. This study would be helpful in industries in which comminution is part of the process, such as mining and cement industries.

Kinetics of Dry-Batch Grinding in a Laboratory-Scale Ball Mill of Sn–Ta–Nb Minerals from the Penouta Mine (Spain)

The optimization of processing plants is one of the main concerns in the mining industry, since the comminution stage, a fundamental operation, accounts for up to 70% of total energy consumption. The aim of this study was to determine the effects that ball size and mill speed exert on the milling kinetics over a wide range of particle sizes. This was done through dry milling and batch grinding tests performed on two samples from the Penouta Sn–Ta–Nb mine (Galicia, Spain), and following Austin methodology. In addition, the relationships amongst Sn, Ta and Nb content, as metals of interest, the specific rate of breakage Si, the kinetic parameters, and the operational conditions were studied through X-Ray fluorescence (XRF) techniques. The results show that, overall, the specific rate of breakage Si decreases with decreasing feed particle size and increasing ball size for most of the tested conditions. A selection function, αT, was formulated on the basis of the ball size for both Penouta mine samples. Finally, it was found that there does exist a direct relationship amongst Sn, Ta and Nb content, as metals of interest, in the milling product, the specific rate of breakage Si and the operational–mineralogical variables of ball size, mill speed and feed particle size.

Study of the effect of mechanical alloying on the structure of Ni-Mn-Sn Heusler alloy

The formation process of nanocrystalline Ni-Mn-Sn Heusler alloy was investigated using mechanical alloying route. Powder samples of this alloy were fabricated using high milling energy technology of planetary ball mill in an argon atmosphere. The effects of selected milling time (0 hr, 5 hr, 10 hr and 15 hr) on the crystallite size, lattice strain, morphology and chemical composition of as-milled powder samples were studied using X-ray diffraction (XRD) and scanning electron microscope (SEM) attached with energy-dispersive Xray spectroscopy (EDX). Thermal behavior of the samples was analyzed by differential scanning caloorimetry (DSC). Results indicated that the formation of single phase Ni2MnSn Heusler alloy was obtained after 15 hr of milling through the balance between particle coalescence and fracture accompanied by uniform grain refinement. The optimum milling time resulted from the proper selection of milling parameters. Compositional analysis of 15 hr asmilled sample revealed stoichiometric Heusler composition (X2YZ) of Ni-Mn-Sn alloy with no impurities. The subsequent heating of the powder particles in DSC showed crystallization of Ni-Mn-Sn alloy. The obtained results provide an understanding of the fabrication of Ni-Mn based Heusler alloy powders by mechanical alloying technique by using laboratory scale ball mill with the aim to bring out structural and thermal aspects of the alloy.

Effect of the operating parameter and grinding media on the wear properties of lifter in ball mills

The wear of lifter in ball mill directly affects the grinding efficiency and economic cost. However, how to evaluate the variation of wear process and predict the wear distribution of lifter is poorly developed. To this end, a laboratory-scale ball mill was used to evaluate the variation of wear process of the lifter in different milling conditions of mill speed, ball filling, grinding media size and shape. Besides, a wear prediction method was used to compare and validate the experimental results. The experimental results showed that the Abbott-Firestone curve can evaluate the lifter surface topography. The wear rate of the lifter specimen is increased first and then decreased with mill speed and grinding media size. Increasing ball filling will increase the wear rate, and the grinding media shape of ball has a maximum wear rate. The wear characteristics of the lifter specimen are consisting of impact pit, indentation, plastic deformation and scratch. Furthermore, the discrete element method (DEM) simulation showed that the wear behavior on the upper surface is higher than that on the side surface of the lifter. The DEM simulation with Archard wear model is an effective tool to investigate the wear distribution on the lifter, which is in good consistent with the wear behavior measured by the experiment.

Monitoring the fill level of a ball mill using vibration sensing and artificial neural network

Ball mills are extensively used in the size reduction process of different ores and minerals. The fill level inside a ball mill is a crucial parameter which needs to be monitored regularly for optimal operation of the ball mill. In this paper, a vibration monitoring-based method is proposed and tested for estimating the fill level inside a laboratory-scale ball mill. A vibration signal is captured from the base of a laboratory-scale ball mill by using a ± 5 g accelerometer. Features are extracted from the vibration signal by using different transforms such as fast Fourier transform, discrete wavelet transform, wavelet packet decomposition, and empirical mode decomposition. These features are given as input to an artificial neural network which is used to predict the percentage fill level inside the ball mill. In this paper, the predicted fill level obtained by using different features are compared. It is found that the predicted fill level due to features obtained after fast Fourier transform outperforms other transforms.

Wet and dry grinding of coal in a laboratory-scale ball mill: Particle-size distributions

In this study, experimental data for wet- and dry-ground coal samples under wet and dry grinding are characterized by commonly used distribution functions. First, both the R-R and Swrebec functions have superior fitting performances for cumulative particle size curves compared to the other studied functions. On this basis, a time-dependent expression is drawn to describe the cumulative particle size distribution. Second, the R-R function produces a significantly superior fit to the relative mass distributions of the ground products compared to those of the others at a short grinding time. The goodness of fit for all distribution functions studied performs marginally worse at approximately 3min, which can be associated with a change in the dominant breakage mechanisms from impact to abrasion-chipping. With an increase in the grinding time, the G-G-S function is the optimal function for characterizing the particle size probability mass distributions of wet grinding, whereas the G-M function provides the best fitting performance when applied to the experimental dry-grinding data. Further, the optimal particle size probability density functions are associated with the difference in breakage mechanisms between wet and dry grinding.

Performance comparison of stirred media mill and ball (BOND) mill in bauxite grinding

Nowadays, the demand for a finer grind in mineral processing has set new challenges within the industry, particularly for grinding technology. Apart from a finer grind, energy efficiency is also a key driver in optimizing grinding technology. With increasing need for fine and ultrafine grinding, stirred media mills became an alternative for conventional ball mills. In this study, samples of bauxite whose chemical composition and Hardgrove Index values are known were ground to micronized size by a laboratory scale stirred media mill and its performance was compared with a laboratory scale ball (Bond) mill. Stirred media mill decreased bauxite d50 size from 780 to 5 µm in 3 min. But, the ball mill has reduced to 455 µm at the end of the same grinding period. In addition, the energy consumed by the ball mill for similar fineness (5 µm) was approximately 300 kWh/t energy, whereas the stirred media mill consumes approximately 174 kWh/t energy. As a result, the stirred mill consumed less energy because it could grind in a shorter time.

A STUDY ON THE EFFECT OF PHYSICAL AND MECHANICAL PROPERTIES OF ROCKS ON THEIR GRINDABILITY

Grinding processes are very costly because of significant energy requirements to achieve particle reduction. It was estimated that about 5-6% of the power generated all over the world is consumed in size reduction of raw materials necessary for mineral, cement, and ceramics industries. Mineral processing comminution is always the most energy intensive operation. The energy consumption may vary considerably for different ore types and flowsheets with values ranging from 10 kwh/t to 30 kwh/t and corresponding to 30 to 50% of the total energy cost. Factors affecting the process of comminution include the design and operating parameters of the comminuting machine as well as the physical and mechanical properties of the comminuted rock.Consequently, the main objective of this work is to study the effect of the most important physical and mechanical properties of rocks on their grindability. It is applied on marble, ilmenite ore and granite samples using a laboratory scale ball mill. The performance of each experiment is measured by the percentage of -1.25 mm in the product. 2n factorial method is applied as an experimental design method on each studied rock. Optimum values of the studied operating parameters and their gradation according to their effect on the process are achieved for each studied rock it is concluded that the performance decreases as the compressive strength and hardness of the studied rocks increase and increases as the abrasion value increases. It is also found that optimum values of the studied operating parameters and their gradation depend on the mechanical properties of the studied rocks.

Effect of Grinding Aids in Cement Grinding

In fine grinding of cement in a ball mill, it is sometimes impractical to grind finer in a dry state. Even though the chemical reactivity requires the material to be dry ground, it is sometimes necessary to use different breakage machines to obtain the product, but it is usually more expensive, requires more energy and reduces capacity. The economic alternative is to use a grinding aid. Grinding aid or grinding additives refer to substances which when mixed into the mill contents cause an increase in rate of size reduction and flowability. Grinding process of clinker was carried on with a laboratory scale ball mill by varying different type of grinding additives and dosage while the operating conditions of the mill was kept constant. These additives were added into mineral in certain ratio based on the mineral weight and the grinding has been done for a definite time at the same condition. Analysis were then conducted for their size distribution and compaction factor. Results shows that there were significant improvement in size distribution as the dosage and types of grinding additives changes. Different types of grinding additives were found only suitable for clinker grinding while other give no significant results. The results obtained showed that with the addition of grinding additives can improve the grindability by decreasing agglomeration and increase breakage and hence, reducing ball coating Industrials application of grinding additives also give significant improvement in terms of throughput and size distributions.

Optimized ensemble modeling based on feature selection using simple sphere criterion for multi-scale mechanical frequency spectrum

Several parameters of industrial processes are indirectly measured by multi-scale mechanical frequency spectrum. Selecting suitable mechanical sub-signals and relevant frequency spectral features for different process parameters remains an open issue. This study proposes a new optimized ensemble model based on feature selection using simple sphere criterion (SSC). Mechanical signals are adaptively decomposed and transformed into frequency spectral data with different timescales. These spectral data are fed into adaptive multi-scale spectral feature selection and modeling framework, in which local-scale frequency spectral features are adaptively selected with concurrent projection to latent structures and SSC based on unscaled data. The optimized ensemble model is constructed with selective information fusion strategy based on reduced frequency spectral data. The feature selection and model learning parameters are jointly selected. Simulation results based on the mechanical vibration and acoustic signals of an experimental laboratory-scale ball mill show the effectiveness of the proposed scheme.

Effect of grinding methods on powder quality of king chilli

The article aims to investigate the influence of cryogenic and ambient grinding on powder quality of king chilli (Capsicum chinense L.). The grinding experiments were performed using a laboratory scale ball mill. Quality of the powders was accessed by measuring the properties such as densities, Hausner ratio, compressibility index, particle size distribution, colour change, microstructural changes, and mineral compositions. Bulk density (483 kg m−3) and tapped density (556 kg m‒3) of ambient ground powder was relatively higher than that of cryo-ground powder, (bulk density 414 kg m‒3 and tapped density 480 kg m‒3). However, Hausner ratio and compressibility index of ambient ground chilli powder were significantly lower than that of cryo-ground powder. The surface morphology, shape, and size of particles of cryo-ground powder were comparatively smoother, regular and smaller in size. Moreover, the major mineral (K) content and the colour (redness, yellowness, and lightness) were found to be relatively superior for cryo-ground powder. The overall results showed cryogenic grinding provides a better and finer quality ground powder than the conventional grinding methods. The results of this study will provide the spice industries, an opportunity to select the better grinding method based on the flow and sensory quality of the powder. The powder properties may further be utilized in the process modelling and design of packaging, grinding and handling equipment.

Effect of ball and feed particle size distribution on the milling efficiency of a ball mill: An attainable region approach

In this article, alternative forms of optimizing the milling efficiency of a laboratory scale ball mill by varying the grinding media size distribution and the feed material particle size distribution were investigated. Silica ore was used as the test material. The experimental parameters that were kept constant in this investigation was the grinding media filling, powder filling and the mill rotational speed. The data obtained from these batch tests was then analyzed using a model free technique called the Attainable Region method. This analysis technique showed that the required product fineness is a function of grinding media and feed material size distributions. It was also observed from the experimental results that in order to increase the milling efficiency of a ball mill, towards optimum production of material in the desired size class, there is a need to correlate the ball size and the feed size distributions.

Mill Load Parameter Forecasting Based on Multi-source Single-scale Mechanical Frequency Spectral Multiple feature Subsets

Multi-source mechanical signals at different locations of the ball mill system have different contributions for constructing mill load parameter forecasting (MLPF) model. It is necessary to select suitable multi-source mechanical signals and their frequency spectral feature subsets. Aim at these problems, a new MLPF approach based on LASSO algorithm and selective ensemble (SEN) modeling strategy by using multi-source single-scale mechanical frequency spectrum is proposed. At first, Fast Fourier transformer (FFT) is used to obtain single-scale frequency spectrum of these multi-source (channel) mechanical signals. Then, SEN-LASSO modeling approach based on candidate regularization parameter set is used to obtain single-scale frequency spectral feature subsets and to build single-channel SEN MLPF model jointly. Finally, SEN strategy is employed again to construct the final MLPF model by selecting and combining these single-channel SEN MLPF models. Simulation results based on a laboratory-scale ball mill validate effectiveness of this approach.

Experimental study of charge dynamics in a laboratory-scale ball mill

To understand and describe the behavior of charge dynamics in mills, a series of dry and wet grinding tests were performed on a laboratory-scale ball mill. The comparisons between experimental results and grinding media trajectory simulations were addressed. Results show that the grinding media trajectory simulations exhibit a good agreement with the experimental results. The shoulder angle was proportional to mill speed and ball filling. The toe angle was inversely proportional to ball filling, but the impact point angle was appeared to invariant to ball filling and inversely proportional to mill speed. By means of motion analysis of the charge, a good grinding efficiency can be obtained when the ball filling ranging from 20% to 40% and the mill speed ranging from 70% to 80%. For dry tests, the orthogonal analysis indicates that the influence order of four factors on power-mass ratio is ball filling, mill speed, powder-grinding media ratio and lifter profile and the influence order of four factors on −0.074 mm yield is mill speed, ball filling, powder-grinding media ratio and lifter profile. The best dry tests are a combination of 70% of critical speed, 20% of ball filling, 0.8 of powder-grinding media ratio and waveform lifter. Correspondingly, the power-mass ratio can increase by 28.27% and the production of −0.074 mm can increase by 50.38%. For wet tests, the variations of −0.074 mm yield on mill speed and moisture content increase up to a maximum and then decrease rapidly. The −0.074 mm yield can reach a maximum at the 80% of mill speed and 50% of moisture content.

Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features

Abstract Frequency spectral data of mechanical vibration and acoustic signals relate to difficult-to-measure production quality and quantity parameters of complex industrial processes. A selective ensemble (SEN) algorithm can be used to build a soft sensor model of these process parameters by fusing valued information selectively from different perspectives. However, a combination of several optimized ensemble sub-models with SEN cannot guarantee the best prediction model. In this study, we use several techniques to construct mechanical vibration and acoustic frequency spectra of a data-driven industrial process parameter model based on selective fusion multi-condition samples and multi-source features. Multi-layer SEN (MLSEN) strategy is used to simulate the domain expert cognitive process. Genetic algorithm and kernel partial least squares are used to construct the inside-layer SEN sub-model based on each mechanical vibration and acoustic frequency spectral feature subset. Branch-and-bound and adaptive weighted fusion algorithms are integrated to select and combine outputs of the inside-layer SEN sub-models. Then, the outside-layer SEN is constructed. Thus, “sub-sampling training examples”-based and “manipulating input features”-based ensemble construction methods are integrated, thereby realizing the selective information fusion process based on multi-condition history samples and multi-source input features. This novel approach is applied to a laboratory-scale ball mill grinding process. A comparison with other methods indicates that the proposed MLSEN approach effectively models mechanical vibration and acoustic signals.

Prediction of Bond's work index from field measurable rock properties

In mineral beneficiation, grinding is the final stage in the process of size reduction. The power consumed in this stage is higher when compared to other stages, owing to increased size reduction ratio. The primary purpose of grinding is to reduce the particle size to optimum so that mineral particles can be extracted more economically. Decision making plays an important role here, as it involves determining and comparing the energy that is required to perform the grinding process and also determining the amount of minerals lost as the coarser size particles are arrived at in mineral beneficiation. In general, Bond's work index is used to determine the grinding efficiency and also to calculate the power requirement. The process is very time consuming and it requires skilled labor and specialized mill. A systematic investigation was carried out to predict Bond's work index using simple field measurable properties of rocks. Tests were conducted on Basalt, Slate and Granite using a laboratory scale ball mill and rock properties namely density, Protodyakonov's strength index and rebound hardness number were determined. The results were analyzed using artificial neural networks and regression analysis. Mathematical equations were developed to predict Bond's work index based on rock properties using regression analysis, which resulted a very good correlation co-efficient values.