Optical sorting technology for waste management from the Boukhadra iron ore mine (NE Algeria)

Knowledge of tailings characteristics is required for utilisation and management purposes in the mining and construction industry. Tailings from the mine waste dumps at Itakpe iron ore mine were collected and analysed in the laboratory to determine their chemical and physical characteristics and these include; permeability, porosity, specific gravity, particle size distribution, chemical composition and bioavailability factor of element. Geochemical speciation with quantitative X-ray powder diffraction was used to evaluate the chemical and mineral composition of Itakpe iron ore tailings. The aim is to offer base line data necessary to assess metal mobility and bioavailability. The distribution of heavy metals such as Cu, Ni, Cd, Cr, Zn and Fe was determined using multi- step sequential extraction. The results obtained indicate that the permeability is 6.24 x 10-3 cm/sec; porosity is 35%; and specific gravity is 3.58. The tailings is well graded and is sand gravel. Nickel and Zinc was found to be considerably high in exchangeable and bound to carbonates fraction which are mobile region and is bound to Fe – Mn oxides which is slightly mobile region but the higher concentration of Ni found in residual fraction. The implication of this result is that Nickel and Zinc partially enter into the food chain. Chromium and Cadmium concentration result indicated that these metals can easily enter into the food chain because of their presence in the mobile region and their higher mobility percentage.

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Effects of Moderate Intensity Interval Training in workers suffering from Restrictive Lung Diseases involved in Polluted environment at Iron Ore Mine

Iran Occupational Health Journal ◽

10.52547/ioh.18.1.32 ◽

2021 ◽

Vol 18 (1) ◽

pp. 32-44

Author(s):

Abdolhakim Batajrobeh Rudi ◽

Mohsen Mohammadnia Ahmadi ◽

Mehdi Mogharnasi ◽

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Keyword(s):

Lung Diseases ◽

Iron Ore ◽

Interval Training ◽

Moderate Intensity ◽

Polluted Environment ◽

Iron Ore Mine

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The Forecasting of Groundwater Fluctuations using Time Series Analysis and Combination of Data-Driven Models

10.22541/au.161698878.88793891/v1 ◽

2021 ◽

Author(s):

Amirhossein Najafabadipour ◽

Gholamreza Kamali ◽

Hossein Nezamabadi-pour

Keyword(s):

Iron Ore ◽

Least Squares Method ◽

Moving Average ◽

Groundwater Resources ◽

Mining Area ◽

Data Driven ◽

Short Term ◽

Iron Ore Mine ◽

Groundwater Fluctuations ◽

Groundwater Drainage

The Forecasting of Groundwater Fluctuations is a useful tool for managing groundwater resources in the mining area. Water resources management requires identifying potential periods for groundwater drainage to prevent groundwater from entering the mine pit and imposing high costs. In this research, Auto-Regressive Integrated Moving Average (ARIMA) and Holt-Winters Exponential Smoothing (HWES) data-driven models were used for short-term modeling of the groundwater fluctuations in a piezometer around the Gohar Zamin Iron Ore Mine. For this purpose, 250 non-seasonal groundwater fluctuations data in the period 22-Nov-2018 to 29-Jul-2019, 200 data for modeling, and 50 data for prediction were used. To take advantage of all the features of the two developed models, the predictions are combined with different methods and specific weights. The results show better accuracy for the ARIMA method between the two short-term forecasts, while the HWES method requires less time for modeling. Also, among all the predictions made, the highest accuracy for the combined least-squares method is for forecasting the groundwater fluctuations in the short-term. All the forecasts show a decrease in the groundwater fluctuations, indicating pumping wells around the Gohar Zamin Iron Ore Mine area.

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