Afrodita Zendelska; Adrijana Trajanova; Mirjana Golomeova; Blagoj Golomeov; Dejan Mirakovski; Nikolinka Doneva; Marija Hadzi-Nikolova
Abstract
The treatment of acid mine drainages is usually based on two basic technologies, active and passive treatment technologies. Whichever acid mine drainage (AMD) treatment method is employed, a neutralizing procedure that raises the water's pH over 7.0 using alkaline agents is required prior to discharge. ...
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The treatment of acid mine drainages is usually based on two basic technologies, active and passive treatment technologies. Whichever acid mine drainage (AMD) treatment method is employed, a neutralizing procedure that raises the water's pH over 7.0 using alkaline agents is required prior to discharge. A comparison of eight different agents (BaCO3, Na2CO3, NaOH, KOH, K2CO3, MgO, CaCO3, and Ba(OH)2) was performed in order to choose the most effective neutralizing agent for acid mine drainage treatment. The experiments were performed using a multi-component synthetic aqueous solution with an initial concentration of 10 mg/L of the Cu, Mn, Zn, Fe, and Pb ions and an initial pH value of 1.9. According to the research, the most effective neutralizing agent for the removal of heavy metals from a multi-component aqueous solution is MgO, while the least effective agent was Na2CO3. The obtained series of effective neutralizing agents for the removal of heavy metals from a multi-component aqueous solution are presented in the work. The effect of the studied concentration of neutralizing agents depends on the neutralizing agents and heavy metals that are used. The percentage of heavy metals removed from aqueous solutions increases along with rising pH values. The consumption of the neutralizing agent decreases as the concentration of the neutralizing agent is increased. In addition, the time taken to achieve pH depends on the agent concentration. In particular, as the concentration of the neutralizing agent increases, the time to reach the pH decreases.
F. Khorram; H. Memarian; B. Tokhmechi; H. Soltanian-zadeh
Abstract
In this study based on image analysis, an ore grade estimation model was developed. The study was performed at a limestone mine in central Iran. The samples were collected from different parts of the mine and crushed in size from 2.58 cm down to 15 cm. The images of the samples were taken in appropriate ...
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In this study based on image analysis, an ore grade estimation model was developed. The study was performed at a limestone mine in central Iran. The samples were collected from different parts of the mine and crushed in size from 2.58 cm down to 15 cm. The images of the samples were taken in appropriate environment and processed. A total of 76 features were extracted from the identified rock samples in all images. Neural network used as an intelligent tool for ore grade estimation and the features of every image were combined with weighted average method. In order to feature dimensional decrease, principal component analysis method was used. Six principal components, which were extracted from the feature vectors, captured 90.661% of the total feature variance. Components were used as the input to neural network and four grade attributes of limestone (CaCO3, Al2O3, Fe2O3 and MgCO3) were used as the output. The root of mean squared error between the observed values and the model estimated values for the test data set are 6.378, 4.847, 0.1513 and 0.0284, the R2 values are 0.7852, 0.8663, 0.7591and 0.8094 for the mentioned chemical composition respectively. The magnitude of R2 indicates the correlation between actual and estimated data. Therefore, it can be inferred that the model can successfully estimate the limestone chemical compositions percentage.
