Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
3 results
Search Results
Item Explosive energy is the most commonly used form of energy to fragment rock mass/overburden and mineral deposits in the mines. Fragmentation obtained in the blasting process influences the downstream costs like loading cost, transportation cost, processing cost, etc. Among the various factors which influence the rock fragmentation, initiation system is one of the most important because presently much research is going on in this area of rock blasting. Some field studies were taken up with conventional detonating cord initiation and shock-tube-based NONEL initiation systems to study the influence of initiation systems on rock fragmentation. Fragmentation analysis was done using the boulder count method and image analysis. It was found that the shock-tube initiation gives 33% less boulders and 31% lesser K50 value compared to detonating cord initiation. © 2004 Taylor & Francis Ltd.(Taylor and Francis Ltd., Shock tube initiation for better fragmentation: A case study) Sastry, V.R.; Ram Chandar, K.2004Item Prediction of Bond's work index from field measurable rock properties(Elsevier B.V., 2016) Ram Chandar, K.; Deo, S.N.; Baliga, A.J.In mineral beneficiation, grinding is the final stage in the process of size reduction. The power consumed in this stage is higher when compared to other stages, owing to increased size reduction ratio. The primary purpose of grinding is to reduce the particle size to optimum so that mineral particles can be extracted more economically. Decision making plays an important role here, as it involves determining and comparing the energy that is required to perform the grinding process and also determining the amount of minerals lost as the coarser size particles are arrived at in mineral beneficiation. In general, Bond's work index is used to determine the grinding efficiency and also to calculate the power requirement. The process is very time consuming and it requires skilled labor and specialized mill. A systematic investigation was carried out to predict Bond's work index using simple field measurable properties of rocks. Tests were conducted on Basalt, Slate and Granite using a laboratory scale ball mill and rock properties namely density, Protodyakonov's strength index and rebound hardness number were determined. The results were analyzed using artificial neural networks and regression analysis. Mathematical equations were developed to predict Bond's work index based on rock properties using regression analysis, which resulted a very good correlation co-efficient values. © 2016 Elsevier B.V.Item A Critical Comparison of Regression Models and Artificial Neural Networks to Predict Ground Vibrations(Springer International Publishing, 2017) Ram Chandar, K.; Sastry, V.R.; Hegde, C.Blasting is important and an essential prerequisite in any opencast mine for fragmenting hard deposits. Blasting always produces unwanted effects like ground vibrations, noise and fly rock; among which ground vibrations effect is more on surrounding structures. Propagation of ground vibrations can lead to destruction of surrounding structures. Prediction of ground vibrations especially in terms of peak particle velocity is beneficial as opposed to conventional data monitoring techniques which can be expensive as well as time consuming. This paper uses predictors to estimate the intensity of ground vibrations and compares different methods of prediction methods like linear regression, multiple linear regression, non linear regression (NLR) and artificial neural networks. Intensity of ground vibrations generated from blasting operations was monitored in three different mines of limestone, dolomite and coal; obtaining about 168 ground vibration recordings in total. The statistical modelling or data-driven modeling has shown promise in the prediction of blast vibrations. Proposed a system of introducing site specific rock parameters like poison’s ratio, uniaxial compressive strength of rock and Young’s modulus to improve the correlation coefficient using statistical modelling (commonly called feature engineering in machine learning circles). © 2016, Springer International Publishing Switzerland.