Correlation between Cerchar abrasivity index,rock properties,and drill bit lifetime

The Cerchar abrasivity index (CAI) is one of the most widely known index method for identification of rock abrasivity. It is a simple and fast testing method providing reliable information on rock abrasiveness. In this study, the relationships between the CAI and some rock properties such as uniaxial compressive strength (UCS), point load strength, Brazilian tensile strength and Schmidt rebound hardness, and equivalent quartz content (EQC) are examined. The relationships between the CAI and drill bit lifetime is also investigated and the type of drill bit wear observed is mentioned. Additionally, the CAI is modeled using simple and multiple linear regression analysis based on the rock properties. Drill bit lifetime is also modeled based on the CAI. The results show that the CAI increases with the increase of the UCS, point load strength, Brazilian tensile strength, L-type and N-type Schmidt rebound hardness, and the EQC. It is concluded that the higher and the lower bit lifetime are obtained for marl and andesitic-basaltic formation, respectively. Moreover, flushing holes, inserted button, button removal, and failures of button on the bits are determined as the type of drill bit wear. The modeling results show that the models based on the UCS and the EQC give the better forecasting performances for the CAI.     

Prediction of soil temperature using regression and artificial neural network models

In this study, monthly soil temperature was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. The soil temperature and other meteorological parameters, which have been taken from Adana meteorological station, were observed between the years of 2000 and 2007 by the Turkish State Meteorological Service (TSMS). The soil temperatures were measured at depths of 5, 10, 20, 50 and 100 cm below the ground level. A three-layer feed-forward ANN structure was constructed and a back-propagation algorithm was used for the training of ANNs. In order to get a successful simulation, the correlation coefficients between all of the meteorological variables (soil temperature, atmospheric temperature, atmospheric pressure, relative humidity, wind speed, rainfall, global solar radiation and sunshine duration) were calculated taking them two by two. First, all independent variables were split into two time periods such as cold and warm seasons. They were added to the enter regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and they were also used in the input layer of the ANN method. Results of these methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods.     

Applications on agricultural and forest waste adsorbents for the removal of lead (II) from contaminated waters

At present, there is growing interest in using low cost, commercially available materials for the adsorption of heavy metals. The major advantages of adsorption technologies are its effectiveness in reducing the concentration of heavy metal ions to very low levels and the use of inexpensive adsorbent materials. In this review, agricultural and forest waste adsorbents were used to remove Pb²⁺ ions in wastewater treatment, and their technical feasibilities were reviewed in studies mainly from 2000 to 2010. They all were compared with each other by metal binding capacities, metal removal performances, sorbent dose, optimum pH, temperature, initial concentration and contact time. Although commercial activated carbon is widely used in wastewater treatment applications, it has high costs. The use of agricultural by-products as adsorbent material to purify heavy metal contaminated water has become increasingly popular through the past decade because they are less expensive, biodegradable, abundant and efficient. Instead of activated carbon, this study was focused on the inexpensive materials such as agricultural and forest waste. It was shown that these alternative adsorbents had sufficient binding capacity to remove Pb²⁺ ions from wastewater.     

K-Ar dating,whole-rock and Sr-Nd isotope geochemistry of calc-alkaline volcanic rocks around the Gümüşhane area: implications for post-collisional volcanism in the Eastern Pontides,Northeast Turkey

Volcanic rocks from the Gümü?hane area in the southern part of the Eastern Pontides (NE Turkey) consist mainly of andesitic lava flows associated with tuffs, and rare basaltic dykes. The K-Ar whole-rock dating of these rocks range from 37.62?±?3.33 Ma (Middle Eocene) to 30.02?±?2.84 Ma (Early Oligocene) for the andesitic lava flows, but are 15.80?±?1.71 Ma (Middle Miocene) for the basaltic dykes. Petrochemically, the volcanic rocks are dominantly medium-K calc-alkaline in composition and show enrichment of large ion lithophile elements, as well as depletion of high field strength elements, thus revealing that volcanic rocks evolved from a parental magmas derived from an enriched mantle source. Chondrite-normalized rare-earth element patterns of the volcanic rocks are concave upwards with low- to-medium enrichment (La_CN/Lu_CN?=?3.39 to 12.56), thereby revealing clinopyroxene- and hornblende-dominated fractionations for andesitic-basaltic rocks and tuffs, respectively. The volcanic rocks have low initial ⁸⁷Sr/⁸⁶Sr ratios (0.70464 to 0.70494) and εNd_(i) values (+1.11 to +3.08), with Nd-model ages (T_DM) of 0.68 to 1.02 Ga, suggesting an enriched lithospheric mantle source of Proterozoic age. Trace element and isotopic data, as well as the modelling results, show that fractional crystallization and minor assimilation played an important role in the evolution of the volcanic rocks studied. The Eocene to Miocene volcanism in the region has resulted from lithospheric delamination and the associated convective thinning of the mantle, which led to the partial melting of the subduction-metasomatized lithospheric mantle.