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Blasting is an economical method for rock breakage in open-pit mines. Backbreak is an undesirable phenomenon induced by blasting operations and has several unsuitable effects such as equipment instability and decreased performance of the blasting. Therefore, accurate estimation of backbreak is required for minimizing the environmental problems. The primary purpose of this paper is to propose a novel predictive model for estimating the backbreak at Shur River Dam region, Iran, using particle swarm optimization (PSO).

For this work, a total of 84 blasting events were considered and five effective factors on backbreak including spacing, burden, stemming, rock mass rating and specific charge were measured. To evaluate the accuracy of the proposed PSO model, multiple regression (MR) model was also developed, and the results of two predictive models were compared with actual field data.

Based on two statistical metrics [i.e. coefficient of determination (R²) and root mean square error (RMSE)], it was found that the proposed PSO model (with R² = 0.960 and RMSE = 0.08) can predict backbreak better than MR (with R² = 0.873 and RMSE = 0.14).

The analysis indicated that the specific charge is the most effective parameter on backbreak among all independent parameters used in this study.

Emerging technologies have been transforming most industries. A wide range of emerging technologies such as blockchain, internet of things (IoT), artificial intelligence (AI), machine learning (ML), robotics and many others have changed the way in which firm value chain activities or processes were executed traditionally. The mining industry has also witnessed the introduction of these emerging technologies in various processes from the exploration stage to the final processing of ores. The purpose of this paper is to understand the pace of adoption of emerging technologies in the Indian mining industry and identify the challenges that managers confront while adopting emerging technologies.

The authors undertook qualitative research. Data collection was done in two stages. Secondary research was conducted to arrive at a repository of use cases of the adoption of emerging technologies in the global mining industry. Primary data collection was also done. The insights on emerging technology adoption and challenges faced in the Indian mining industry were captured by in-depth interviewing of subject matter experts. The authors interviewed 21 mining subject matter experts with a semi-structured open-ended questionnaire. The responses were content analyzed by thematic content analysis. Technological-organizational-environmental (TOE) and diffusion of innovation (DOI) frameworks were applied to segregate different factors affecting the adoption of emerging technologies in the Indian mining industry.

Emerging technologies such as blockchain, IoT, AI, ML, robotics has been applied across various mining engineering value chain activities such as in drilling, blasting, excavation and ore hauling. However, emerging technologies adoption was hindered because of a lack of managerial awareness, cultural inertia, substantive upfront investments and the nature of intangible benefits in the short run.

The research applied technology adoption frameworks in the mining industry. The authors used TOE and DOI frameworks to understand the challenges faced by Indian mining firms. The research findings, thus added to the conversation of TOE and DOI frameworks in the context of the Indian mining industry.

The research finding would help mining firm managers to anticipate the challenges with respect to technology adoption. This would allow mining executives to create a proper technology adoption plan and intervene proactively. The research would also provide information about the steps taken by competing firms with respect to emerging technologies adoption. The research would help managers to decide technology implementation steps in drilling, blasting, excavation and ore hauling to be undertaken for successful adoption of emerging technologies. Technology firms could gain insights into the issues faced by mining firms in adopting emerging technologies. This research would help managers to influence organizational technology policy and endorse the addition of pro-technology policies in mining activities. Policymakers involved in the mining sector could also incorporate industry-level policy decisions so as to facilitate the adoption of emerging technologies among mining firms and remove the barriers to the adoption of emerging technologies. This would create an opportunity for technology providers to redesign product offerings, which could be a good fit for Indian mining firms.

Indian mining industry contributed significantly to the Indian economy. Despite this, limited focus has been put regarding the adoption of emerging technologies in the mining industry. Mining managers did not have any framework to understand the challenges faced in the adoption of technologies across the mining value chain that is in drilling, blasting, excavation and ore hauling. This study focused on identifying those challenges through the use of technology adoption frameworks. This research was one of the first studies to gain insights on emerging technologies adoption in the context of the mining industry through the theoretical lens of TOE and DOI frameworks.

The purpose of this paper is to propose a gauge for the convergence of the deterministic particle swarm optimization (PSO) algorithm to obtain an optimum upper bound for PSO algorithm and also developing a precise equation for predicting the rock fragmentation, as important aims in surface mines.

In this study, a database including 80 sets of data was collected from 80 blasting events in Shur river dam region, in Iran. The values of maximum charge per delay (W), burden (B), spacing (S), stemming (ST), powder factor (PF), rock mass rating (RMR) and D80, as a standard for evaluating the fragmentation, were measured. To check the performance of the proposed PSO models, artificial neural network was also developed. Accuracy of the developed models was evaluated using several statistical evaluation criteria, such as variance account for, R-square (R²) and root mean square error.

Finding the upper bounds for the difference between the position and the best position of particles in PSO algorithm and also developing a precise equation for predicting the rock fragmentation, as important aims in surface mines.

For the first time, the convergence of the deterministic PSO is studied in this study without using the stagnation or the weak chaotic assumption. The authors also studied application of PSO inpredicting rock fragmentation.