Frequency domain characterization of torque in tumbling ball mills using DEM modelling: Application to filling level monitoring

Pedrayes, F.; Norniella, Joaquín G.; Melero, M.G.; Menéndez-Aguado, Juan M.; Díaz, J.

doi:10.1016/j.powtec.2017.10.026

Cited by 20 publications

(17 citation statements)

References 35 publications

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“…To clarify the different comminution mechanisms (impact, friction) operating in the two media configurations (single-ball, multi-bead), we investigated the dynamics of the jar/media system using discrete element method (DEM) simulations [23][24][25][26].…”

Section: Discrete Element Simulations Of Ball and Bead Trajectoriesmentioning

confidence: 99%

Fine Comminution of Pine Bark: How Does Mechanical Loading Influence Particles Properties and Milling Efficiency?

et al. 2019

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The use of lignocellulosic plant biomass as an alternative to fossil feedstocks for chemistry, energy and materials often involves an intense dry comminution step, for which the energy consumed can vary significantly according to the process parameters, the particle size targeted, and the properties of the biomass. Here we studied the fine milling of maritime pine bark in an impact-mill configuration and in an attrition-mill configuration. The properties of the resulting powders (particle size distribution, particle shape, specific surface area, agglomeration level) obtained in each configuration were compared in relation to process energy consumption. Results evidenced that the agglomeration phenomena drive milling efficiency and limit the possibilities for reaching ultrafine particles. Interestingly, impact loading proved more effective at breaking down coarse particles but tended to generate high agglomeration levels, whereas attrition milling led to less agglomeration and thus to finer particles.

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Section: Discrete Element Simulations Of Ball and Bead Trajectoriesmentioning

confidence: 99%

Fine Comminution of Pine Bark: How Does Mechanical Loading Influence Particles Properties and Milling Efficiency?

et al. 2019

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“…The DEM is a numerical method to address the kinematic and mechanical behaviour of complex granular systems involving many discrete units with certain shapes and masses. It has been widely used in ball mills [18,36] to determine the particles behaviour and the torque and energy of ball mills in different working conditions. In DEM each particle is tracked and its motion is governed by Newton's second law: (17) where i (=1, 2, 3) denotes the x, y and z coordinate directions, respectively; F i is the out-of-balance force component of the particle; V i is the translational velocity; m is the mass of the particle; M i is the out-of-balance moment due to the contacts;  i is the rotational velocity; I is the rotational inertia of the particle; g is the global damping coe cient; dt is the time step.…”

Section: Discrete Element Methodsmentioning

confidence: 99%

Discrete element simulation of particle motion in ball mills based on similarity

Jiang

Tan

et al. 2018

Powder Technology

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“…Accordingly, the AE signal processing methods can be classified into three categories: (i) signal processing, (ii) feature extraction, and (iii) pattern recognition. The most important and widely used (Qin et al, 2018) × (Karakus & Perez, 2014) × × × × (Bastari et al, 2011) × (Parsian et al, 2017) × × × (Marinescu & Axinte, 2009) × × (Xiao, Hurich et al, 2018) × (Li et al, 2018) × × (Buj-Corral et al,, 2018) × (Shaffer et al,, 2018) × × × (Wang et al, 2017) × × (Rivero et al, 2008) × (Flegner et al, 2014) × (Feng & Yi, 2017) × (Yari et al, 2017) × (Yari & Bagherpour, 2018 (a&b)) × (Shreedharan et al, 2014) × (Kostur & Futo, 2007) × ( Kawamura et al, 2017) × (Pedrayes et al, 2018) × × (Gradl et al, 2012) × × × (Vardhan et al, 2009) × × × (Beheshtizadeh et al, 2017) × (Miklusova et al, 2006) × × × × (Goyal & Pabla, 2016) × (Liew & Wang, 1998) × × (Kong et al, 2015) × (Jain et al, 2001) The Mining-Geology-Petroleum Engineering Bulletin and the authors ©, 2019, pp. 19-32, DOI: 10.17794/rgn.2019.4.3 methods of the first category (signal processing) include: time series statistical models, Short Time Fourier Transform (STFT), Fast Fourier Transform (FFT), Wavelet Packet Decomposition (WPD), Hilbert-Huang transform (HHT), Wigner-Ville distribution, signal spectrum analysis, Adaptive Line Enhancer (ALE), wavelet transform, and the Peak-Hold-Down-Sample (PHDS) algorithm.…”

Section: Acoustic Signal Processing Feature Extraction and Pattern Rmentioning

confidence: 99%

Application of Acoustic Emission (Ae) in Mining and Earth Sciences: A Review

Khoshouei

Bagherpour

2019

MGPB

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One of the most important products of the majority of tools, processes and, phenomena in mining engineering and earth sciences are high-frequency elastic waves, often referred to as acoustic waves. These acoustic waves can be a useful tool for prediction and measurement, monitoring and diagnosis. Due to the importance of engineering activities that should be specific, measurable, available, reliable and time-based (SMART), Acoustic Emission (AE) techniques can be integrated into engineering operations such as excavation to give exact estimations or simplify some aspects of investigations. Hence, these techniques are expected to play an increasing role in the detection, monitoring, and measurement in engineering applications. With the development of acoustic signal processing methods in earth sciences and the growing need for accurate, fast, and cost-effective prediction, monitoring and measurement methods, AE tools and techniques are expected to turn into powerful alternatives to current methods. The purpose of this paper is to review the application of the acoustic methods as a tool in various projects. For this purpose, the application of AE methods has been reviewed in different fields related to earth sciences including mining engineering, drilling engineering, geology, petroleum engineering, and other related fields. According to the purpose of using these methods, the application of AE is divided into three groups: prediction and estimation, monitoring and performance assessment, and detection. The use of AE methods, among other laboratory or in-situ methods, is very fast and accurate, and by only installing specific sensors with data logging equipment, it has been able to process and analyse data online and remotely. Finally, the possibility of using AE techniques in design and calculating in various projects and as a tool for decision making was evaluated.

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Frequency domain characterization of torque in tumbling ball mills using DEM modelling: Application to filling level monitoring

Cited by 20 publications

References 35 publications

Fine Comminution of Pine Bark: How Does Mechanical Loading Influence Particles Properties and Milling Efficiency?

Fine Comminution of Pine Bark: How Does Mechanical Loading Influence Particles Properties and Milling Efficiency?

Discrete element simulation of particle motion in ball mills based on similarity

Application of Acoustic Emission (Ae) in Mining and Earth Sciences: A Review

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