Arduino-Based Low-Cost Device for the Measurement of Detonation Times in Blasting Caps.

The use of equipment such as oscilloscopes, high-speed cameras or acoustic sensors is quite common to measure detonation times from surface connectors and detonators. However, these solutions are expensive and, sometimes, not adequate to use in field conditions, such as mining or civil works. In this regard, a low-cost portable device is designed and tested using the Arduino platform, achieving a simple, robust and precise system to carry out field measurements. This study describes the characteristics and working principles of the designed device, as well as the verifications carried out to check the accuracy of the Arduino ceramic oscillator. Additionally, a field test was carried out using 100 actual detonators and surface connectors to verify the correct operation of the designed equipment. We have designed a device, and a methodology, to measure detonation instants with a minimum accuracy of 0.1 ms, being sufficient to carry out subsequent studies of detonation time dispersion for non-electric detonators.

A GIS-based approach: Influence of the ventilation layout to the environmental conditions in an underground mine.

Gases such as CO, CO2 or NOx are constantly generated by the equipment in any underground mine and the ventilation layout can play an important role in keeping low concentrations in the working faces. Hence, a method able to control the workplace environment is crucial. This paper proposes a geographical information system (GIS) for such goal. The system created provides the necessary tools to manage and analyse an underground environment, connecting pollutants and temperatures with the ventilation characteristics over time. Data concerning the ventilation system, in a case study, has been taken every month since 2009 and integrated into the management system, which has quantified the gasses concentration throughout the mine due to the characteristics and evolution of the ventilation layout. Three different zones concerning CO, CO2, NOx and effective temperature have been found as well as some variations among workplaces within the same zone that suggest local airflow recirculations. The system proposed could be a useful tool to improve the workplace conditions and efficiency levels.

Data mining of accidents in Spanish underground mines in the period 2003-2021 caused by a collision with a moving object.

Underground mining is currently one of the Spanish economic sectors with the worst accident rates. Besides, the most frequent type of accident, and with the most serious consequences, is the one in which the injured worker is hit by a moving object. For this reason, this study focuses on the analysis of this type of accident, divided into 3 subgroups to better understand the behavioural patterns. Data mining techniques were applied using the Apriori algorithm to extract as much information as possible about the genesis of these accidents. Similarly, each subset of accidents was processed in two different ways to improve the data analysis, depending on the causal variables used in each case, so that a study of six different scenarios was carried out. The five best association rules or behaviour patterns for each of the six scenarios are shown as a function of their frequency for each rule with 1-4 causal variables.

Analysis of Occupational Accidents in the Spanish Mining Sector in the Period 2009-2018.

Occupational accidents in the Spanish mining industry have been substantially reduced over the last decades. However, the incidence rate shows higher values than other leading mining countries. In this regard, the research carried out reveals the factors influencing the high incidence rates of the Spanish mining sector, based on three scenarios: underground mining (UG), quarries and open pit mining (OP) and mineral processing plants (PP). The three most common types of accident for each scenario have been determined, considering the accidents in Spain between 2009 and 2018. The analysis also includes the main deviations, and physical activities that the injured worker was carrying out at the time of the accident. Besides, a model to predict the number of accidents based on the lost working days is also presented together with the incidence and severity risk index adjusted by the number of employees and their worked hours, respectively, in each scenario. These finding can be relevant to define the most effective measures and policies to reduce the number of accidents in the mining sector.

Analysis of a Historical Accident in a Spanish Coal Mine.

There has been a long history of coal mine accidents and these, usually, involve serious injuries, fatalities, and the destruction of facilities. In the seventies, an explosion killed 28 miners in a Spanish coal mine. This paper gives insight into the main factors of the accident by means of the causation mode, using two well-known alternatives: (1) the method from the Spanish Instituto Nacional de Seguridad y Salud en el Trabajo (INSST), where the causes and circumstances of the accident are classified into immediate causes and basic causes, and (2) the Feyer and Williamson method, where the classification is done using precursor events and contributing factors. The analysis identifies the lessons to be learned from the disaster. Both methods have given very similar results, verifying the goodness of the analysis. Methane emissions due to a variation in the exploitation method, the electrical installation, and a lack of safety procedures and training were the main causes of the accident. These findings explain the real causes of this accident and can be very valuable for the prevention of future accidents.

Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques.

An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector-either surface or underground mining-based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents.