Comparative study of WipFrag image analysis and Kuz-Ram empirical model in granite aggregate quarry and their application for blast fragmentation rating

Shehu, Shaib Abdulazeez; Yusuf, Kudirat Oziohu; Hashim, M.H.M.

doi:10.1080/17486025.2020.1720830

Cited by 19 publications

(24 citation statements)

References 15 publications

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“…These programs are widely used in scientific practice for automated manual determination of morphological parameters of various scanned images of objects. Processing of scanned images was performed in full accordance with the relevant manual for each program (Maerz & Palangio, 1999;Whan et al, 2014;Broeke et al, 2015;Rueden et al, 2017;Hu et al, 2019;Shehu et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Selection of Effective Software for the Analysis of the Fractional Composition of the Chaotic Seed Layer Using the Example of Oilseed Radish

Tsytsiura

2021

Eng. Agríc.

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Section: Methodsmentioning

confidence: 99%

Selection of Effective Software for the Analysis of the Fractional Composition of the Chaotic Seed Layer Using the Example of Oilseed Radish

Tsytsiura

2021

Eng. Agríc.

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“…The two results from Split Desktop and Kuz-ram model were compared using statistical analyses. The Kuz-Ram model has three key equations; the Kuznetsovs Equation, Rosin-Rammler Equation and the Uniformity index as shown in Equations 1-3 (Shehu et al, 2020;Lawal 2021).…”

Section: Resources and Methods Usedmentioning

confidence: 99%

“…Fragmentation degree plays important role in control and reduction of loading, transportation and crushing costs (Siddiqui et al, 2009;Akande and Lawal, 2013). Blasting could be seen as the first comminution process in quarrying and mining and thus, the size of fragments obtained should not exceed the gape of crusher for efficient operation (Shehu et al, 2020;Lawal 2020;Lawal et al, 2021). Elimination of extremely big fragments and minimisation of excessive fines in the muckpile are usually considered to essentially optimize blast fragmentation.…”

Section: Introductionmentioning

confidence: 99%

“…(Dahlhielm, 1996;Havermann and Vogt, 1996;Maerz et al, 1996;Schleifer and Tessier, 1996;Raina et al, 2002, Sanchidrian et al, 2009Siddiqui et al, 2009). Thus, the choice of a specific blast fragmentation method to be adopted depends on the needs that *Manuscript received February 20, 2021 Revised version accepted June 17, 2021 https://doi.org/10.4314/gm.v21i1.5 necessitated the analysis (Shehu et al, 2020;Lawal and Kwon, 2020). The image analysis technique is highly advantageous because it has no limitation on the size of samples that can be analysed and does not interrupt the production process.…”

Section: Introductionmentioning

confidence: 99%

“…The image analysis technique is highly advantageous because it has no limitation on the size of samples that can be analysed and does not interrupt the production process. They are usually in the form of computer software or programs and highly affordable (Shehu et al, 2020;Lawal, 2021). However, the inherent errors of the method could be minimised by capturing a large number of highquality images for analysis.…”

Section: Introductionmentioning

confidence: 99%

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Application of Split Desktop Image Analysis and Kuz-Ram Empirical Model for Evaluation of Blast Fragmentation Efficiency in a Typical Granite Quarry

Idowu¹,

Olaleye²,

Saliu³

2021

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Evaluation of fragmentation efficiency is an integral aspect of blasting operation. This study therefore assesses the efficiency of fragmentation size at Eminent granite quarry, Ibadan, Nigeria using Split Desktop software and Kuz-Ram empirical model. Five muckpiles of blasted rocks with the same blast design were analysed. The muckpile images were captured using smart high precision digital camera and uploaded into computer for Split Desktop analysis. The results of the fragment size distribution obtained from Kuz-Ram vary slightly with that of the Split Desktop but follow similar trend. The average values of F80 and F90 from the Split Desktop image analysis were 90.96 cm and 98.24 cm respectively. The Kuz-Ram model values for F80 and F90 were 88.52 cm and 92.95 cm respectively. The results of the Split Desktop were compared to the results obtained from the Kuz-Ram experiential model. The findings showed that the results obtained from Kuz-Ram empirical model were in conformity with the results from the Split Desktop software based on empirical relationship. Hence, the model is good for preliminary evaluation of blast design. Keywords: Blasting, Particle Size Distribution, Split Desktop Software, Muckpile, Fragmentation Indicator

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Performance evaluation of rock fragmentation prediction based on RF‐BOA, AdaBoost‐BOA, GBoost‐BOA, and ERT‐BOA hybrid models

Zhao,

Li,

Zhou

et al. 2024

Deep Underground Science and Engineering

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Rock fragmentation is an important indicator for assessing the quality of blasting operations. However, accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties. For this reason, optimized by the Bayesian optimization algorithm (BOA), four hybrid machine learning models, including random forest, adaptive boosting, gradient boosting, and extremely randomized trees, were developed in this study. A total of 102 data sets with seven input parameters (spacing‐to‐burden ratio, hole depth‐to‐burden ratio, burden‐to‐hole diameter ratio, stemming length‐to‐burden ratio, powder factor, in situ block size, and elastic modulus) and one output parameter (rock fragment mean size, X50) were adopted to train and validate the predictive models. The root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination () were used as the evaluation metrics. The evaluation results demonstrated that the hybrid models showed superior performance than the standalone models. The hybrid model consisting of gradient boosting and BOA (GBoost‐BOA) achieved the best prediction results compared with the other hybrid models, with the highest R2 value of 0.96 and the smallest values of RMSE and MAE of 0.03 and 0.02, respectively. Furthermore, sensitivity analysis was carried out to study the effects of input variables on rock fragmentation. In situ block size (XB), elastic modulus (E), and stemming length‐to‐burden ratio (T/B) were set as the main influencing factors. The proposed hybrid model provided a reliable prediction result and thus could be considered an alternative approach for rock fragment prediction in mining engineering.

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Comparative study of WipFrag image analysis and Kuz-Ram empirical model in granite aggregate quarry and their application for blast fragmentation rating

Cited by 19 publications

References 15 publications

Selection of Effective Software for the Analysis of the Fractional Composition of the Chaotic Seed Layer Using the Example of Oilseed Radish

Selection of Effective Software for the Analysis of the Fractional Composition of the Chaotic Seed Layer Using the Example of Oilseed Radish

Application of Split Desktop Image Analysis and Kuz-Ram Empirical Model for Evaluation of Blast Fragmentation Efficiency in a Typical Granite Quarry

Performance evaluation of rock fragmentation prediction based on RF‐BOA, AdaBoost‐BOA, GBoost‐BOA, and ERT‐BOA hybrid models

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