International Journal of Mining and Mineral Engineering (5 papers in press)
Application and comparison of the cokriging and the fractal model for identifying geochemical anomalies in Janja area, SE Iran
by Ali Akbar Daya, Marzieh Hosseininasab
Abstract: In this study, the cokriging model was used for determining the geochemical concentrations in Janja, SE Iran. Cokriging is a multivariate prediction method in spatial statistics which considers the spatial dependence as well as predicting the dependence between variables. In addition, fractal concentration- area model was used for determining the geochemical anomalies in Janja region. Eight elements (Au, Cu, Mn, Zn, Fe, As, Mo, and Pb) from 300 stream sediment samples were used to identify geochemical anomalies. The geochemical maps obtained from cokriging and fractal modeling showed that geochemical anomalies for copper and gold are located in the north, NW, and center of the study area. Comparing the anomalies maps of the elements showed a correlation between elements of gold, copper, molybdenum, and iron in the region. Also, a negative correlation was determined between arsenic with gold and copper elements and there wasnt any correlation between the gold and copper elements of the region with manganese, lead, and zinc. For examining the results of the studies more closely, some of the data were analyzed and evaluated using Concentration Area (C-A) Fractal model.
Keywords: Geochemistry; Anomaly separation; Cokriging; Concentration-Area (C-A) fractal model; Janja.
Peak Phosphate in Jordan
by Awwad Al Titi, Rami Al Rawashdeh, Khalid Al Tarawneh
Abstract: The benefits and impacts of mineral resource extraction and processing in Jordan are changing and whilst our vast endowment of phosphate will not be exhausted soon, extraction and production are becoming more challenging. This paper establishes a conceptual analysis of Peak Phosphate as a powerful tool and it uses Gompertz and logistic models for measuring Jordans phosphate peak year, peak production and depletion time . Our results showed that based on logistic and Gompertz models results, Jordans phosphate is likely to peak in 2044 and 2048 respectively. Phosphate production has already passed the peak year in Al-Hasa and Al-Abiad mines. The logistic model for Jordan phosphate which has a peak year of 2048 and a production volume of 15.2 million tonnes matched exactly the Gompertz model for Al-Shidiyah mine which confirms that Jordans future phosphate production will totally depend on Al-Shidiyah mine.
Keywords: Peak phosphate; Jordan ; Resource depletion; Hubbert curve; Gompertz model; Logistic model; Stripping ratio; Ore gradern.
Use of Iron Ore Mine Tailings in Infrastructure Projects
by Ram Chandar Karra, Shubhanand Rao, Gayana BC
Abstract: Utilization of iron ore tailings in bricks as a replacement for sand will help in sustainable and greener development. The literature shows the potential use of iron ore tailings as a replacement of natural fine aggregates. As natural sand reserves are depleting day by day, there is a need for substitution for sand in bricks. A comprehensive overview of the published literature on the use of iron ore tailings and other industrial waste is being presented. The effects of various properties such as compressive strength, thermal conductivity and durability of bricks have been presented in this paper.
Keywords: Iron ore tailings; Bricks; Compressive strength; Thermal Conductivity; Greener development.
The classification and mechanisms of coal-gas compound dynamic disasters: A preliminary discussion
by Kai Wang, Feng Du
Abstract: With the development of deep mining in recent years, there appears a new form of dynamic disaster, which is known as coal-gas outburst and rockburst compound dynamic disaster. It is also called as coal-gas compound dynamic disaster for academia. In this paper, we propose a new classification system of coal-gas compound dynamic disasters. The coal-gas compound dynamic disasters are divided into three types named rockburst-induced outburst dynamic disaster, outburst-induced rockburst dynamic disaster, outburst and rockburst coupling dynamic disaster. And the occurrence mechanism and energy criterion of each kind of compound dynamic disaster are discussed. We suggest that integrated prevention and control strategies of coal-gas compound dynamic disasters should be adopted by eliminating the gas internal energy and releasing the elastic energy. We also believe that using big data to improve the prediction and control level of coal-gas compound dynamic disasters is the key research direction in the future.
Keywords: deep mining; coal and gas outburst; rockburst; compound dynamic disaster; classification; mechanism; energy criterion; gas internal energy; elastic energy prevention and control strategies; big data;.
Implementation of Industry 4.0 technologies in the mining industry a case study
by Arnesh Telukdarie
Abstract: In modern mining, it is imperative to have a real-time flow of information between enterprise level and shop floor systems. The gaps that exist between these spheres make it difficult for managers to have timely information for optimum decision making. A mining company needs instantaneous visibility on production, quality, cycle times, machine status, and other important operational variables to achieve optimum and effective operations. With the implementation of Industry 4.0 technologies at a Mine, the integration of fragmented shop floor and the enterprise level systems enables seamless communication in delivering optimum operations. This research demonstrates Industry 4.0 technologies as the mechanisms for integrating business systems, manufacturing systems and processes. The Industry 4.0 methods researched are deployed using Software Development Lifecycle (SDLC) process at a mining company to integrate systems such as manufacturing, plant, business partners, and SAP ERP. The results introduce a semi-smart Mine with real-time visibility of the overall mining status.
Keywords: Industry 4.0; Internet of Things; Industrial Internet of Things; Cyber Physical Systems; Big Data; ERP.