Blasting operation involves the use of a specific explosive quantity, detonated to fragment insitu and oversize rock block for particle reduction. Rock fragmentation size distribution has a direct influence on the proposed costs of mining one ton of the ore and the cost of run-off-mine processing. The focus of this study is on investigating the effect of charge load ratio and blast design parameters such as stiffness ratio, maximum instantaneous charge, and specific charge on rock fragmentation particle size distribution in dolomite quarry located at Akoko Edo state, South-west Nigeria. The 50% passing sizes (X50, m), 80% passing size (X80, m), and characteristic size (Xc, m) of blast results were determined using Wipware software. It was observed that the optimum mean size (X50, m), 80% passing fragment size (X80, m), and characteristic size (Xc, m) of rock depends strongly on the explosive bottom and column loading ratio, stiffness ratio, and specific charge. The regression analysis result reveals that the explosive specific charge and stiffness ratio influence the fragment size distribution with a negative correlation relationship, and the explosive bottom and column loading ratio has a positive correlation relationship with the blast fragmentation. Multivariate Regression (MVR) models were developed for the prediction of blast fragmentation sizes (X80, X50, and Xc) with R2 values of 0.76, 0.52, and 0.63 respectively. Based on the low correlation value obtained from the developed models, the proposed multivariate Regression (MVR) models are less suitable for the prediction of blast fragmentation particle size distribution.

Blast-induced noise and ground vibrations are two of the most signi cant challenges in blast rock fragmentation, and they can have an impact on mine stability and the safety of personnel and machines nearby. As a result, evaluating the slope stability and the contribution of powder factor on ground vibration and noise induced by blasting in small scale mines plays an important role in improving mine sustainability. The results of slope stability assessment and the effect of explosive charge on blast induced ground vibration and noise level at selected carbonate rock quarries in Akoko Edo, Nigeria are presented in this paper. The purpose of this research is to examine the slope stability of a mine using kinematic analysis and slope mass rating techniques, as well as to determine the relationship between powder factor and ground vibration and noise caused by blasting. According to the study results, the average blast induced noise and ground vibration from the nine blast rounds are 123-151 Kb and 2.43 mm/s to 5.03 mm/s, respectively. According to the ndings, the powder factor has a positive relationship with blast-induced ground vibration and noise level. The ndings also show that the results of SMR and kinematic slope analysis techniques are similar with less variation and can be used to assess slope stability in practice.

The application of metacarbonate rocks depend majorly on their physical and/or chemical properties. The mineralogical and chemical properties of the metacarbonate rocks from Enwan, Bekuma, and Ekpedo metacarbonate deposits in Akoko Edo, Nigeria were determined using combined modal petrographic analysis and XRS-FP quantitative analysis. The modal analyses show that the metacarbonate rocks from Enwan, Ekpedo and Bekuma contain averages of 96.4%, 58.5%, and 77.35% CaCO3 respectively and 3.6%, 41.5% and 13.1% MgO respectively. The mineralogical constituent include calcite, dolomite, quartz, plagioclase and muscovite. The principal constituents of the carbonate rocks are calcite and dolomite with calcite being the dominant mineral. The marbles can be used in a variety of calcium carbonate industries due to the low quartz content and the mineralogical composition. These characteristics make the metacarbonate rocks suitable for the production of paint, factory lime, pesticide and animal feed, as construction material and for use in the chemical industry.

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Blast fragmentation efficiency prediction can enhance the improvement of blast productivity and the prediction using available software is capital expensive. This study proposed soft computing models based on artificial intelligence approaches and multivariate regression techniques to predict small diameter hole blast fragmentation efficiency at the Akoko Edo dolomite quarry. WipFrag software was used to analyze blast muck pile pictures taken and to evaluate the blast fragmentation performance. The proposed model performance was checked with five model performance indices including average bias error, root means square error, correlation coefficient, and average absolute error were computed. The obtained result reveals that the Adaptive Neuro-fuzzy inference system (ANFIS) proposed model is capable of accurate prediction of blast fragmentation efficiency as compared with artificial neural networks and Multivariate regression models. Sensitivity analysis computed shows that the Powder factor has the highest influence on fragmentation efficiency.