Developing coal burst propensity index method for Australian coal mines

Forecasting Rock Burst in Coal Mines Based on Neural Network

In view of disasters caused by rock burst becoming more and more serious in coal mine production, three models are established for evaluation and prediction the rock burst risk based on artificial neural network. First, ten indicators are determined which have a larger influence on rock burst. Then two back propagation network models are trained using the original data and the processed data re...

Fossil hunting in coal mines.

At the northern tip of Colombia, Jonathan Bloch hunts for fossils in the midst of spontaneous combustion fires, noxious gases laced with foul-smelling sulfur, rock-shattering explosions, and some of the largest machines in the world. “It is a weird place to work,” Bloch admits, an associate curator of vertebrate paleontology at the FloridaMuseum of Natural History, although he says he feels saf...

Pneumoconiosis in Central Indian coal-mines.

Extensive surveys carried out in Europe and America have shown the incidence of coalworkers' pneumoconiosis to be widespread (Flinn, Seifert, Brinton, Jones, and Franks, 1941; Fletcher, 1955). In India, however, the prevalence of this disease has never been investigated, and a report published by the government of India in 1946 (Deshpande, 1946) stated that there was no conclusive evidence that...

Chinese coal mines--the industrial death trap.

Knowledge-Enhanced CO-monitoring in Coal-Mines

Detection of underground res is an important security task in hard-coal mining. Automated re detection systems are usually based on the monitoring of carbon monoxide (CO). Systems using conventional technology based on threshold and tendency observations, however, generate a large number of false alarms. We show how CO-concentrations can be forecast by appropriate models of the physical and che...