Interpretation of variability of rock mass rating by geostatistical analysis: a case study in Western Turkey

Geotechnical interpretation of drillholes for rock mass characterization is commonly based on the engineering judgment. This might result in misrepresentation of the rock mass at unsampled locations. This paper investigates the reproducibility of the spatial variability of rock mass quality (rock mass rating, RMR) using geostatistics. Geotechnical data from an exploratory phase mining project in Western Turkey that covers 2.7 km depth with a total of 14 drillholes composed of 700 samples was used to perform a geostatistical analysis. RMR values of positions, where geotechnical logging was missing, were estimated by applying inverse distance weighting (IDW) and the ordinary kriging (OK) methods. The validation process showed that the OK method is consistently estimating the RMR values better than IDW. The spatial variability of RMR values by geostatistical analysis could provide valuable information in the design and development of underground or surface structures.     

Source Based Characterization and Pollution Profile of a Baker's Yeast Industry

Baker's yeast industry has significant effects on environment due to significant water consumption and high strength wastewater production. Effluents from baker's yeast industry are characterized by high concentrations of biological oxygen demand (BOD₅), chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), total dissolved solids (TDS), sulfate, and dark brown color. The aim of this study is to provide a detailed quantitative and qualitative identification and characterization of the wastewater sources from different processes of a baker's yeast industry. It is identified that the most polluted wastewater components of the facility are coming from rotary drum filter and evaporation process. However, the highest pollution loads is identified as tank and equipment cleaning effluent due to very high flow rate from this source. The efforts should be on the way to minimize the water usage for the tank and equipment cleaning process, and to recycle less polluted waste streams back to the process.     

Prediction of the Cut Depth of Granitic Rocks Machined by Abrasive Waterjet (AWJ)

In this paper, an experimental study on the cut depth, which is an important cutting performance indicator in the abrasive waterjet (AWJ) cutting of rock, was presented. Taguchi experimental design of an orthogonal array was employed to conduct the experiments. A variety of nine types of granitic rocks were used in the cutting experiments. The experimental data were used to assess the influence of AWJ operating variables on the cut depth. Using regression analysis, models for prediction of the cut depth from the operating variables and rock properties in AWJ machining of granitic rocks were then developed and verified. The results indicated that the cut depths decreased with increasing traverse speed and decreasing abrasive size. On the other hand, increase of the abrasive mass flow rate and water pressure led to increases in the cut depths. Additionally, it was observed that the standoff distance had no discernible effects on the cut depths. Furthermore, from the statistical analysis, it was found that the predictive models developed for the rock types had potential for practical applications. Verification of the models for using them as a practical guideline revealed a high applicability of the models within the experimental range used.     

Operation analysis of Eleviyan irrigation reservoir dam by optimization and stochastic simulation

Rainfall distributions in Iran are spatially and temporally heterogeneous, a fact probably linked to the mostly arid and semi-arid climate of the country. On the other hand, water demand is increasing with increasing population and improving life style. At present, the optimal utilization of water resources and irrigation dams is the primary concern of water resource managers. The Eleviyan dam (with a capacity of 60 hm³) was constructed to meet the irrigation and municipal water needs of the Maraghan region (Northwestern Iran). In this study, the efficiency of the Eleviyan irrigation dam system was investigated in three phases by setting up the optimization model that maximized the water release for irrigation purposes after municipal water need were met. In the first phase, the inflows measured in the 21 years prior to the construction of the reservoir, and in the second, the inflows generated by the Monte Carlo simulation method, and in the third phase, the inflows after the construction of the reservoir were used. The results demonstrate that the capacity determined during the preliminary studies was accurate and the operation carried out in the recent periods of operation life was up to a satisfactory standard.     

Development of Predictive Models for the Specific Energy of Circular Diamond Sawblades in the Sawing of Granitic Rocks

This paper presents an experimental study on the sawing of granitic rocks by circular diamond sawblades. The influence of the operating variables and rock properties on the specific energy were initially investigated and analyzed. To determine the most significant operating variables and rock properties influencing the specific energy, statistical analyses were then employed and the models were built for the estimation of specific energy depending on the operating variables and the rock properties. Moreover, the derived models were validated through statistical tests such as the determination coefficient, t-test, F-test, and residuals. The results indicated that the specific energy decreased with the decreasing of peripheral speed and the increasing of traverse speed, cutting depth, and flow rate of cooling fluid, respectively. It was concluded that, rather than the physico-mechanical properties, the mineralogical properties were the dominant rock properties affecting the specific energy. Additionally, the peripheral speed was statistically determined as the most significant operating variable affecting the specific energy. The peripheral speed was followed by the cutting depth, traverse speed, and flow rate of cooling fluid with respect to their level of significance on the specific energy. Furthermore, the model results revealed that the developed models have high potentials as a guidance for practical applications.     

Flow estimations for the Sohu Stream using artificial neural networks

In this study, daily rainfall–runoff relationships for Sohu Stream were modelled using an artificial neural network (ANN) method by including the feed-forward back-propagation method. The ANN part was divided into two stages. During the first stage, current flows were estimated by using previously measured flow data. The best network architecture was found to utilise two neurons in the input layer (the delayed flows from the first and second days), two hidden layers, and one output layer (the current flow). The coefficient of determination (R ²) in this architecture was 81.4%. During the second stage, the current flows were estimated by using a combination of previously measured values for precipitation, temperature, and flows. The best architecture consisted of an input layer of 2 days of delayed precipitation, 3 days of delayed flows, and temperature of the current. The R ² in this architecture was calculated to be 85.5%. The results of the second stage best reflected the real-world situation because they accounted for more input variables. In all models, the variables with the highest R ² ranked as the previous flow (81.4%), previous precipitation (21.7%), and temperature.     

Wear Performance of Saw Blades in Processing of Granitic Rocks and Development of Models for Wear Estimation

This study investigates the wear performance of diamond circular saw blades in cutting of granitic rocks. An alternative wear measuring method is developed to measure the reduced blade radius without taking the blade off the machine. The effect on and contribution to the specific wear rate (SWR) of each operating variable are determined, and the SWR is correlated with rock properties. Morphologies of wearing surfaces of segments and rocks are also evaluated using scanning electron microscopy (SEM). Depending on both operating variables and rock properties, prediction models are developed for estimation of the SWR. Results show that the SWR increases with an increase in the peripheral speed and the traverse speed, while it decreases with an increase in the cutting depth and the flow rate of the cooling fluid. The peripheral speed, and the microhardness and proportions of minerals such as quartz, plagioclase, and feldspar are statistically determined as the significant variables affecting the SWR. Finally, it is disclosed that models developed for estimation of SWR have great potential for practical applications.     

M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey

This study investigate the potential of M5 model tree in predicting daily stream flows in Sohu river located within the municipal borders of Ankara, Turkey. The results of the M5 model tree was compared with support vector machines. Both modelling approaches were used to forecast up to 7-day ahead stream flow. A comparison of correlation coefficient and root mean square value indicates that M5 model tree approach works equally well to the SVM for same day discharge prediction. The M5 model tree also works well up to 7-day ahead discharge forecasting in comparison of SVM with this data set. An advantage of using M5 model tree approach is the availability of simple linear models to predict the discharge as well use of less computational time.