Grindability soft-sensors based on lithological composition and on-line measurements

Casali, A.; González, Guillermo; Vallebuona, G.; Perez, Claudio A.; Vargas, Regis Nunes

doi:10.1016/s0892-6875(01)00065-6

Cited by 33 publications

(5 citation statements)

References 6 publications

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“…Since 2000, image processing for fragmented rock particles has gained significant research interest, with various algorithms developed for measuring rock fragment sizes in applications like gravitational flows, conveyors, muckpiles, and laboratories [104]. Casali [26] implemented machine vision and image segmentation for ore sorting and particle size distribution in mining processing. Matthew [94] offered a mathematical and image analysis method to determine rock pile size distribution, focusing on surface size class proportions.…”

Section: Uav Photogrammetry In Particle Size Distribution Analysismentioning

confidence: 99%

Particle size distribution (PSD) estimation using unmanned aerial vehicle (UAV) photogrammetry for rockfill shear strength characterization

Arrieta,

Zhang

2024

Acta Geotech.

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The strength of rockfills and waste materials is significantly influenced by their particle size distribution (PSD). For large waste rockfills, PSD is fundamental to determine the shear behavior. Traditionally, PSD for rockfill, used in materials like coarse-grained aggregates, has been obtained through physical sieving. However, the particle sizes in hard rockfills can vary significantly from small particles (< 20 cm diameter) to large blocks or boulders over 100 cm, with the maximum size limited by the in situ ground conditions and blasting performance. Essentially, the sieving process is impractical, considering the scale of the mine waste dumps and the time required. Therefore, in this study, a workflow using digital detection to estimate the PSD is presented, aiming to quantify the waste dump shear strength using Barton–Kjaernsli empirical criterion. PSD from UAV is validated using manual field measurements of individual boulders. The error for coarse characteristic size prediction ranges within ± 4 mm, and the increase in the data collection frequency, area covered, and resolution of fragmentation measurement for rockfills and waste dumps using UAV allows to improve the statistical reliability of the PSD and fragmentation measurement.

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Section: Uav Photogrammetry In Particle Size Distribution Analysismentioning

confidence: 99%

Particle size distribution (PSD) estimation using unmanned aerial vehicle (UAV) photogrammetry for rockfill shear strength characterization

Arrieta,

Zhang

2024

Acta Geotech.

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“…where α i and b are obtained by solving the linear equations in equation (8). In equation (10), K(x, x i ) is a kernel function that satisfies the Mercer condition. e kernel functions commonly used in LSSVM are shown in Table 1 [48], which give the parameters that LSSVM needs to be manually adjusted in the case of different kernel functions.…”

Section: Least Squares Support Vector Machine Regression Modelmentioning

confidence: 99%

“…In the grinding and classifying process, many experts and scholars have made great contributions to the development of soft-sensor models for the grinding process. In the initial stage of soft-sensor modeling of the grinding process, due to the limitations of theoretical knowledge and calculation tools, some researchers (such as Casali) tried to establish a mechanism model to predict the granularity of the grinding process [10][11][12]. When establishing the mechanism model, the entire grinding process needs to be carefully analyzed, and a large number of algebraic and differential equations are used to describe the entire grinding process.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm

et al. 2020

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Soft-sensor technology plays a vital role in tracking and monitoring the key production indicators of the grinding and classifying process. Least squares support vector machine (LSSVM), as a soft-sensor model with strong generalization ability, can be used to predict key production indicators in complex grinding processes. The traditional crossvalidation method cannot obtain the ideal structure parameters of LSSVM. In order to improve the prediction accuracy of LSSVM, a golden sine Harris Hawk optimization (GSHHO) algorithm was proposed to optimize the structure parameters of LSSVM models with linear kernel, sigmoid kernel, polynomial kernel, and radial basis kernel, and the influences of GSHHO algorithm on the prediction accuracy under these LSSVM models were studied. In order to deal with the problem that the prediction accuracy of the model decreases due to changes of industrial status, this paper adopts moving window (MW) strategy to adaptively revise the LSSVM (MW-LSSVM), which greatly improves the prediction accuracy of the LSSVM. The prediction accuracy of the regularized extreme learning machine with MW strategy (MW-RELM) is higher than that of MW-LSSVM at some moments. Based on the training errors of LSSVM and RELM within the window, this paper proposes an adaptive hybrid soft-sensing model that switches between LSSVM and RELM. Compared with the previous MW-LSSVM, MW-neural network trained with extended Kalman filter(MW-KNN), and MW-RELM, the prediction accuracy of the hybrid model is further improved. Simulation results show that the proposed hybrid adaptive soft-sensor model has good generalization ability and prediction accuracy.

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“…In view of the complicated grinding and classifying process, many experts and scholars have proposed various soft-sensing models to predict different production indicators. In the early research of soft-sensing modeling of the grinding process, due to the limitations of theoretical knowledge and computing equipment, some experts tried to analyze the entire grinding process clearly and adopt the algebraic equations or differential equations to establish a mechanism model as a soft-sensor model of the grinding process to predict the ore granularity [11]- [13]. When modeling the mechanism, it is necessary to assume that all working conditions are ideal.…”

Section: Introductionmentioning

confidence: 99%

Extreme Learning Machine Soft-Sensor Model With Different Activation Functions on Grinding Process Optimized by Improved Black Hole Algorithm

Xie

Wang²,

Cheng

et al. 2020

IEEE Access

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Aiming at predicting the key economic and technical indicators (Granularity and Ore content)in the grinding production process, the extreme learning machine (ELM) soft-sensor model with different activation functions on grinding process optimized by improved black hole (BH) algorithm was proposed. Based on the selected auxiliary variables for the soft-sensor model of the grinding, the KPCA method is used to reduce the dimensionality of the high-dimensional data. In order to investigate the influence of different activation functions on the prediction accuracy of the ELM model, seven continuous function (Arctan, Hardim, Morlet, ReLu, Sigma, Sin and Tanh) are used as the activation function of the ELM neural network to establish soft-sensor models respectively. For the shortcomings that ELM model weights and offset values are arbitrarily given so as to result in the low prediction accuracy and low reliability, an improved BH algorithm based on the golden sine operator and the Levy flight operator (GSLBH) was used to optimize the parameters of the ELM neural network. Simulation results show that the model has better generalization and prediction accuracy, and can meet the real-time control requirements of the grinding process. INDEX TERMS Grinding process, soft-sensor, ELM neural network, black hole algorithm, activation function.

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Grindability soft-sensors based on lithological composition and on-line measurements

Cited by 33 publications

References 6 publications

Particle size distribution (PSD) estimation using unmanned aerial vehicle (UAV) photogrammetry for rockfill shear strength characterization

Particle size distribution (PSD) estimation using unmanned aerial vehicle (UAV) photogrammetry for rockfill shear strength characterization

Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm

Extreme Learning Machine Soft-Sensor Model With Different Activation Functions on Grinding Process Optimized by Improved Black Hole Algorithm

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