Development of a drill energy utilization index for aiding selection of drill machines in surface mines

Drill machines used in surface mines, particularly in coal, is characterized by a very poor utilization （around 40%） and low availability （around 60%）. The main purpose of this study is to develop a drill selec- tion methodology and simultaneously a performance evaluation technique based on drill cuttings produced and drilling rate achieved. In all 28 blast drilled through were investigated. The drilling was accomplished by 5 different drill machines of Ingersoll-Rand and Revathi working in coal mines of Sonepur Bazari （SECL） and Block-II （BCCL）. The drills are Rotary and Rotary Percussive type using tri- cone rock roller bits. Drill cuttings were collected and sieve analysis was done in the laboratory. Using Rosin Ramler Diagram, coarseness index （CI）, mean chip size （d）, specific-st trace area （SSA） and charac- teristic particle size distribution curves for all the holes drilled were plotted. The predictor equation for drill penetration rate established through multiple regressions was found to have a very good correlation with an index of determination of 0.85. A comparative analysis of particle size distribution curves was used to evaluate the drill efficiency. The suggested approach utilises the area under the curve, after the point of trend reversal and brittleness ratio of the respective bench to arrive at drill energy utilization index （DEUI）, for mapping of drill machine to bench, The developed DEU1 can aid in selecting or mapping a right machine to right bench for achieving higher penetration rate and utilizations.

Ecological risk assessment of heavy metals in surface seawater and sediment near the outlet of a zinc factory in Huludao City, Liaoning Province, China

At present, the methods widely applied to assess ecological risk of heavy metals are essentially single-point estimates in which exposure and toxicity data cannot be fully used and probabilities of adverse biological effects cannot be achieved. In this study, based on investigation of concentrations of six heavy metals(As, Hg, Pb, Cd, Cu, and Zn) in the surface seawater and sediment near the outlet of a zinc factory, located in Huludao City, Liaoning Province, China, a tiered approach consisting of several probabilistic options was used to refi ne ecological risk assessment for the individuals. A mixture of various heavy metals was detected in the surface seawater, and potential ecological risk index(PERI) was adopted to assess the potential ecological risk of heavy metals in the surface sediment. The results from all levels of aquatic ecological risk assessment in the tiered framework, ranging from comparison of single effects and exposure values to the use of distribution-based Hazard Quotient obtained through Monte Carlo simulation, are consistent with each other. Briefl y, aquatic Zn and Cu posed a clear ecological risk, while Cd, Pb, Hg, and As in the water column posed potential risk. As expected, combined ecological risk of heavy metal mixture in the surface seawater was proved signifi cantly higher than the risk caused by any individual heavy metal, calculated using the concept of total equivalent concentration. According to PERI, the severity of pollution by the six heavy metals in the surface sediment decreased in the following sequence: Cd>Hg>As>Pb>Cu>Zn, and the total heavy metals in the sediment posed a very high risk to the marine environment. This study provides a useful mathematical framework for ecological risk assessment of heavy metals.

Asymmetric GARCH type models for asymmetric volatility characteristics analysis and wind power forecasting

Wind power forecasting is of great significance to the safety, reliability and stability of power grid. In this study, the GARCH type models are employed to explore the asymmetric features of wind power time series and improved forecasting precision. Benchmark Symmetric Curve (BSC) and Asymmetric Curve Index (ACI) are proposed as new asymmetric volatility analytical tool, and several generalized applications are presented. In the case study, the utility of the GARCH-type models in depicting time-varying volatility of wind power time series is demonstrated with the asymmetry effect, verified by the asymmetric parameter estimation. With benefit of the enhanced News Impact Curve (NIC) analysis, the responses in volatility to the magnitude and the sign of shocks are emphasized. The results are all confirmed to be consistent despite varied model specifications. The case study verifies that the models considering the asymmetric effect of volatility benefit the wind power forecasting performance.