Classification of granular materials via flowability-based clustering with application to bulk feeding

Torres-Serra, Joel; Ferran, Antonio Rodríguez; Romero, E.

doi:10.1016/j.powtec.2020.09.022

Cited by 8 publications

(5 citation statements)

References 43 publications

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“…There are various complex states of motion during the formation of the accumulation angle of the bulk material, which are related to the physical properties of the contact material and the material itself, so it can be applied to the contact parameter calibration test of the discrete element model (Hamzah and Omar 2018;Torres-Serra et al 2021). In this work, the stacking angle was measured by the cylinder lifting method and the injection funnel method based on existing studies.…”

Section: Actual Stacking Testmentioning

confidence: 99%

Discrete element contact parameters measurement and calibration of sugarcane leaves based on RSM-PSO

Lei

Ren

Liu

et al. 2023

BioRes

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In response to the lack of accurate and reliable parameters in the discrete element simulation analysis of the sugarcane leaf crushing and return device, in this work, the actual and simulated errors of two stacking angles α and β of sugarcane leaves were used as indicators to calibrate the discrete element parameters. The second-order regression models between the important parameters and the indicators were obtained by Plackett-Burman test, steepest climb test, and Box-Behnken optimization test, and the analysis of variance and interaction factors were performed. The response surface method and particle swarm optimization algorithm were used to find the best significance parameters, and the best combination of significance parameters was obtained: the static friction coefficient between sugarcane leaves was 0.306, the rolling friction coefficient between sugarcane leaves was 0.198, and the recovery coefficient of sugarcane leaf-plate collision was 0.102. The relative errors of the simulation results and the physical test stacking angle α and stacking angle β were 0.609% and 1.643%, respectively. The calibration parameters can provide a theoretical reference for the design and research of sugarcane leaf crushing and returning machines, as well as the calibration of discrete element model parameters for leaf crops with high water content.

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Section: Actual Stacking Testmentioning

confidence: 99%

Discrete element contact parameters measurement and calibration of sugarcane leaves based on RSM-PSO

Lei

Ren

Liu

et al. 2023

BioRes

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“…Various complex motion states in the process of forming the accumulation angle of the bulk material can better characterize the scattering, flow, and friction characteristics of the bulk material [27]. The stacking angle and angle of repose are important basic data for material transportation, storage, harvesting, sowing, and other links [27,39,40]. Refer- There was a positive correlation between the rolling friction coefficient and horizontal rolling distance, and the coefficients of determination of the equations R 2 were all greater than 0.99.…”

Section: Stacking Angle and Angle-of-repose Testmentioning

confidence: 99%

“…Various complex motion states in the process of forming the accumulation angle of the bulk material can better characterize the scattering, flow, and friction characteristics of the bulk material [27]. The stacking angle and angle of repose are important basic data for material transportation, storage, harvesting, sowing, and other links [27,39,40]. Referring to the research results of Liu et al [19,38], a self-made container was selected (300 mm in length, 150 mm in width, and 310 mm in height).…”

Section: Stacking Angle and Angle-of-repose Testmentioning

confidence: 99%

Simulation Parameter Calibration and Experimental Study of a Discrete Element Model of Cotton Precision Seed Metering

Bai

Yuan

Kang³

et al. 2022

Agriculture

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To improve the accuracy of the parameters used in the discrete element simulation test, this study calibrated the simulation parameters of cotton seeds by combining a physical test and simulation test. Based on the intrinsic parameters used for the physical test of cotton seed, according to the freefall collision method, inclined plane sliding method, and inclined plane rolling method, the contact parameters of cotton seeds and cotton seeds, stainless steel, and nylon were measured, respectively. The physical test of the accumulation angle and angle of repose of the cotton seeds was conducted. It was obtained to process the image of the seed pile with Matrix Laboratory software. The Plackett–Burman test was used to screen the significance of the simulation parameters. The optimal value range of the significant parameters was determined according to the steepest climbing test. The second-order regression model of the significant parameters, the stacking-angle error, and the angle-of-repose error were obtained according to the Box–Behnken design test. Taking the minimum stacking-angle error and angle-of-repose error as the optimization target values, the following optimal parameter combination was obtained: the interspecies collision recovery coefficient was 0.413, the interspecies static friction coefficient was 0.695, and the interspecies rolling friction coefficient was 0.214. Three repetitive simulation experiments were conducted to prove the reliability of the calibration results. The research results can be used for discrete element simulation experiments for cotton precision seed metering.

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“…For instance, machine learning based on image analysis was proposed to relate third body (the interfacial layer of material between contacting particles) morphology to contact rheology 8 , to relate the grain velocity field to the frictional state of a sheared layer undergoing stick-slip 9 , to enable prediction of the discharge time of granular flows in hoppers 10 , and to infer micromechanical parameters from X-ray imaging 11 . Machine learning methods that do not rely on images were found to enable prediction of seismicity of laboratory-scale earthquakes, specifically of their stick-slip stress time series 12 , and to enable optimal bulk-feeder system selection based on grain micro-mechanical properties 13 . More generally, a Support Vector Machine method was shown to enable the prediction of particle rearrangement from the knowledge of their microstructure in a variety of flowing soft materials 14 .…”

Section: Introductionmentioning

confidence: 99%

Classifying grains using behaviour-informed machine learning

Laudari

Marks

Rognon

2022

Sci Rep

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Sorting granular materials such as ores, coffee beans, cereals, gravels and pills is essential for applications in mineral processing, agriculture and waste recycling. Existing sorting methods are based on the detection of contrast in grain properties including size, colour, density and chemical composition. However, many grain properties cannot be directly detected in-situ, which significantly impairs sorting efficacy. We show here that a simple neural network can infer contrast in a wide range of grain properties by detecting patterns in their observable kinematics. These properties include grain size, density, stiffness, friction, dissipation and adhesion. This method of classification based on behaviour can significantly widen the range of granular materials that can be sorted. It can similarly be applied to enhance the sorting of other particulate materials including cells and droplets in microfluidic devices.

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Classification of granular materials via flowability-based clustering with application to bulk feeding

Cited by 8 publications

References 43 publications

Discrete element contact parameters measurement and calibration of sugarcane leaves based on RSM-PSO

Discrete element contact parameters measurement and calibration of sugarcane leaves based on RSM-PSO

Simulation Parameter Calibration and Experimental Study of a Discrete Element Model of Cotton Precision Seed Metering

Classifying grains using behaviour-informed machine learning

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