Efficient Graphical Algorithm of Sensor Distribution and Air Volume Reconstruction for a Smart Mine Ventilation Network.

The accurate and reliable monitoring of ventilation parameters is key to intelligent ventilation systems. In order to realize the visualization of airflow, it is essential to solve the airflow reconstruction problem using few sensors. In this study, a new concept called independent cut set that depends on the structure of the underlying graph is presented to determine the minimum number and location of sensors. We evaluated its effectiveness in a coal mine owned by Jinmei Corporation Limited (Jinmei Co., Ltd., Shanghai, China). Our results indicated that fewer than 30% of tunnels needed to have wind speed sensors set up to reconstruct the well-posed airflow of all the tunnels (>200 in some mines). The results showed that the algorithm was feasible. The reconstructed air volume of the ventilation network using this algorithm was the same as the actual air volume. The algorithm provides theoretical support for flow reconstruction

An improved energy management methodology for the mining industry.

The focus for this work was the development of an improved energy management methodology tailored for the mining sector. Motivation for this research was driven by perception of slow progress in adoption of energy management practices to improve energy performance within the mining sector. Energy audits conducted for an underground mine, a mineral processing facility, and a pyrometallurgical process were reviewed and recommendations for improved data gathering, reporting and interpretation were identified. An obstacle for conducting energy audits in mines without extensive sub-metering is a lack of disaggregated data indicating end use. Thus a novel method was developed using signal processing techniques to disaggregate the end-use electricity consumption, exemplified through isolation of a mine hoist signal from the main electricity meter data. Further refinements to the method may lead to its widespread adoption, which may lower energy auditing costs via a reduced number of meters and infrastructure, as well as lower data storage requirements. Mine ventilation systems correspond to the largest energy demand center for underground mines. Thus a detailed analysis ensued with the development of a techno-economic model that could be used to assess various fan and duct options. Furthermore, the need for a standardized methodology for determination of duct friction factors from ventilation surveys was proposed, which included a method to verify the validity of the resulting value from asperity height measurements. A method was also suggested for determination of leakage and duct friction factor values from ventilation survey data. Dissemination of best practice is a strategy that could be employed to improve energy performance throughout the mining sector, thus a Best Practice database was developed to iv improve communication and provide a standardized reporting framework for sharing of energy conservation initiatives. Demonstration of continuous improvement is an underpinning element of the ISO 50001 energy management standard but as mines extract ore from deeper levels energy use increases. Thus ensued the development of a benchmarking metric, with the use of appropriate support variables that included mine depth, production, and climate data, that demonstrated the benefit of implemented energy conservation measures for an underground mine. The development of an ultimate energy management methodology for all stages of mineral processing from ‘Mine to Bullion’ is beyond the scope of this work. However, this research has resulted in several recommendations for improvement and identified areas for further improvements.Doctor of Philosophy (PhD) in Natural Resources Engineerin

Dynamic atmospheric signal analysis for improving mine safety and health

There are a number of contaminants generated from strata and equipment usage in underground mines including poisonous and combustible gases, as well as heat. Mine ventilation is utilized to dilute the gases and cool the mine to provide conducive environment for mine workers. In order to ensure that contaminant levels are within acceptable regulatory limits, various sensors are installed in strategic places in the mine for monitoring.Continuous atmospheric monitoring is one of the tools used to achieve health and safety limit compliance and to ensure the quality of air conditions in underground mines. It is challenging to interpret monitoring sensor signal for accident prevention due to different contributing factors. The possibility of contaminant accumulation can be dangerously high as the concentration pulse traveling in the air moves from one location to another. This can be attributed to the inherent delay processes associated with the concentration pulse as it travels with the air velocity. As such, the identification of the delay hazard processes is of prime importance in predicting and preventing any future contaminant concentration increase in the traveling front.An increase of hazardous contaminant concentrations can be predicted by signal pattern recognition, root-cause analysis of rapid changes toward deterioration and forward prediction in time using algorithms and numerical models. This study focuses on analyzing signal patterns to recognize dangerous trends due to delayed processes by predicting contaminant concentrations for safety checking in underground mines. Efficient numerical ventilation model with contaminant simulation components is needed for the analysis of real-time atmospheric monitoring data. Examples of signal analysis and forward prediction of concentration are demonstrated in mine examples and the new results are presented for the application to improve mine safety and health

Mining Safety and Sustainability I

Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry

Managing and Controlling the Thermal Environment in Underground Metal Mines

The main aim of this research work was to discuss the methods of identifying and control heat in underground mine environments. The research contains three main sections as follow:1. Selecting an appropriate heat stress index for underground mining applicationMethods: The aim of this research study was to discuss the challenges in identifying and selecting an appropriate heat stress index for thermal planning and management purposes in underground mines. A method was proposed coupled to a defined strategy for selecting and recommending heat stress indices to be used in underground metal mines in the US and worldwide based on a thermal comfort model. Results: The performance of current heat stress indices used in underground mines varies based on the climatic conditions and the level of activities. Therefore, by carefully selecting or establishing an appropriate heat stress index is of paramount importance to ensure the safety, health and increasing productivity of the underground workers.Conclusions: This method presents an important tool to assess and select the most appropriate index for certain climatic conditions in order to protect the underground workers from heat related illnesses. Although complex, the method presents results that are easy to interpret and understand than any of the currently available evaluation methods.2. Best practices in use of continuous climatic monitoring systems for assessment of underground mine climatic condition:Methods: Major heat sources in an underground metal mine in Nevada was quantified using over one year of climatic data collection in both primary and auxiliary ventilation systems. Furthermore, auxiliary ventilation systems were examined in a development heading and a production area at our partner mine. Climatic models were developed and validated to simulate the climatic conditions based on intake airflow conditions and the heat load along the ducting system. Considerations were also given to the fact that arsenic concentrations may be present at the face. Different scenarios were studied to design and optimize the auxiliary ventilation systems in order to minimize the heat generated by multiple auxiliary fans and minimize arsenic concentration in the production workings.Results: The results show that the heat generated by different major heat sources can change throughout the mine as a function of surface temperature. Furthermore, current auxiliary ventilation design cannot maintain the comfort limits of the underground workers. In some cases, some type of cooling system must be utilized to retain the thermal comfort in production workings. Conclusions: In many instances, by simply adjusting or upgrading the auxiliary ventilation system in a problem area of a mine will effectively dilute the pollutants that are generated during production operations and provide adequate climatic conditions to the mine workers. This can be achieved through various methods such as: (1) extending the auxiliary duct towards the face, (2) installing an additional auxiliary fan to overcome the added pressure losses in the system, (3) changing the size of the fan, (4) switching from an “exhausting” arrangement to a “forcing” arrangement, and (5) installing an “overlap” auxiliary ventilation system.3. Quantifying the thermal damping effect in underground vertical openings using artificial neural network:Method: A nonlinear autoregressive time series with external input (NARX) algorithm was used as a novel method to predict the dry-bulb temperature (Td) at the bottom of the shaft as a function of surface air temperature. Furthermore, an attempt was made to quantify typical “damping coefficient” for both production and ventilation shafts through simple linear regression models.Results: The performance of the model was examined using climatic data collected at two underground mines during summer and winter. Analyses demonstrated that the artificial neural network (ANN) model could accurately predict the temperature at the bottom of a shaft. Comparisons between the collected climatic data and the regression-based predictions show that a simple linear regression model provides an acceptable prediction of the Td at the bottom of intake shafts. The same approach can be used to predict the thermal damping effect on the wet-bulb temperature (Tw) at the bottom of production and ventilation shafts.Conclusions: A comparison between collected data and the climatic modeling demonstrates that the ventilation or climatic modeling software packages do not have the ability take into account the “thermal damping effect (TDE)” (also known as thermal flywheel effect) when modeling the thermal environment in deep and hot underground mines. The major difficulty in incorporating TDE comes from a large number of variables interacting with each other plus the time-dependent heat and mass transport processes that control the flow of strata heat into/from the mine airways

Proceedings of the 2009 Coal Operators\u27 Conference

Proceedings of the 2009 Coal Operators\u27 Conference. All papers in these proceedings are peer reviewed. ISBN: 978 1 920806 95 8

Demand Reduction and Responsive Strategies for Underground Mining

This thesis presents a demand reduction and responsive strategy for underground mining operations. The thesis starts with a literature review and background research on global energy, coal mining and the energy related issues that the mining industry face everyday. The thesis then goes on to discuss underground mine electrical power systems, data acquisition, load profiling, priority ranking, load shedding and demand side management in mining. Other areas presented in this thesis are existing energy reduction techniques, including: high efficiency motors, motor speed reduction and low energy lighting. During the thesis a data acquisition system was designed and installed at a UK Coal colliery and integrated into the mines existing supervisory control and data acquisition (SCADA) system. Design and installation problems were overcome with the construction of a test meter and lab installation and testing. A detailed explanation of the system design and installation along with the data analysis of the data from the installed system. A comprehensive load profile and load characterisation system was developed by the author. The load profiling system is comprehensive allows the definition of any type of load profile. These load profiles are fixed, variable and transient load types. The loads output and electrical demand are all taken into consideration. The load characterisation system developed is also very comprehensive. The LC MATRIX is used with the load profiles and the load characteristics to define off-line schedules. A set of unique real-time decision algorithms are also developed by the author to operate the off-line schedules within the desired objective function. MATLAB Simulation is used to developed and test the systems. Results from these test are presented. Application of the developed load profiling and scheduling systems are applied to the data collected from the mine, the results of this and the cost savings are also presented

A new signal processing method for acoustic emission/microseismic data analysis

The acoustic emission/microseismic technique (AE/MS) has emerged as one of the most important techniques in recent decades and has found wide applications in different fields. Extraction of seismic event with precise timing is the first step and also the foundation for processing AE/MS signals. However, this process remains a challenging task for most AE/MS applications. The process has generally been performed by human analysts. However, manual processing is time consuming and subjective. These challenges continue to provide motivation for the search for new and innovative ways to improve the signal processing needs of the AE/MS technique. This research has developed a highly efficient method to resolve the problems of background noise and outburst activities characteristic of AE/MS data to enhance the picking of P-phase onset time. The method is a hybrid technique, comprising the characteristic function (CF), high order statistics, stationary discrete wavelet transform (SDWT), and a phase association theory. The performance of the algorithm has been evaluated with data from a coal mine and a 3-D concrete pile laboratory experiment. The accuracy of picking was found to be highly dependent on the choice of wavelet function, the decomposition scale, CF, and window size. The performance of the algorithm has been compared with that of a human expert and the following pickers: the short-term average to long-term average (STA/LTA), the Baer and Kradolfer, the modified energy ratio, and the short-term to long-term kurtosis. The results show that the proposed method has better picking accuracy (84% to 78% based on data from a coal mine) than the STA/LTA. The introduction of the phase association theory and the SDWT method in this research provided a novelty, which has not been seen in any of the previous algorithms --Abstract, page iii

Qualitative and Quantitative Approaches for Evaluation of Safety Risks in Coal Mines

The safety in underground coal mines continues to be a major problem in the Indian mining industry. Despite significant measures taken by the Directorate General of Mines Safety (DGMS) to reduce the number of mining accidents in underground coal mines, the number remains high. To improve the safety conditions, it has become a prerequisite to performing risk assessment for various operations in Indian mines. It is noted that many research studies conducted in the past are limited to either statistical analysis of accidents or study of single equipment or operation using qualitative and quantitative techniques. Limited work has been done to identify, analyse, and evaluate the safety risks of a complete underground coal mine in India. The present study attempts to determine the appropriate qualitative and quantitative risk assessment approaches for the evaluation of safety risks in Indian underground coal mines. This thesis addresses several important objectives as (i) to identify the type of safety risk analysis techniques suitable for evaluating various mining scenarios (ii) to identify and analyse the hazard factors and hazardous events that affects the safety in underground coal using the qualitative and quantitative approaches (iii) to evaluate the risk level (RL) of the hazardous factors/groups, hazardous events, and the overall mine using the proposed methodology. In this research work, the qualitative techniques, i.e. Failure Mode and Effects Analysis (FMEA), Workplace Risk Assessment and Control (WRAC), and the quantitative techniques, i.e. Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) were applied in an underground coal mine to identify and analyse the hazard factors and hazard events. The analysis of FMEA and WRAC results concluded that the qualitative risk assessment is easy to execute and practical as they are not dependent on the historical data; rather they need experience and close examination. On the other hand, they may yield subjective results due to instinctive human assessment. The analysis of the FTA and ETA results concluded that the quantitative risk assessment could not be performed in Indian underground coal mines due to lack of probability, exposure, and consequence data. To overcome the mentioned problems in qualitative and quantitative techniques, a methodology was proposed for evaluation of the safety risks of hazard events, hazard groups, and overall mine. The proposed methodology is the unification of fuzzy logic, VIKOR (In Serbia: VIseKriterijumska Optimizacija I Kompromisno Resenje, that means: Multi-criteria Optimization and Compromise Solution), and Analytic Hierarchy Process (AHP) techniques. Because of the imprecise nature of the information available in the mining industry, fuzzy logic was employed to evaluate the risk of each hazardous event in terms of consequence, exposure, and probability. VIKOR as was used to rank the evaluated risk of hazardous events. AHP technique helps to determine the relative importance of the risk factors. Therefore, AHP technique was integrated into the risk model so that the risk evaluation can progress from hazardous event level to hazard factor level and finally to overall mine level. To reduce the calculation time significantly and to increase the speed of the proposed risk assessment process, a user-friendly Graphical User Interface (TRAM) was developed using the C# language through Microsoft Visual Studio 2015 and .Net libraries. The proposed methodology developed in this thesis was applied to six underground coal mines. The results presented the risk level of hazard events, hazards groups and overall mine of six mines. The mine-5 has the highest risk level among the evaluated mines. The ranking order of the mines observed based on the overall risk level is mine-5> mine-1 > mine-2 > mine-3 > mine-6 > mine-4. The results of the proposed methodology were compared with DGMS proposed rapid ranking method. This is observed that the proposed methodology presents better evaluation than other approaches. This study could help the mine management to prepare safety measures based on the risk rankings obtained. It may also aid to evaluate accurate risk levels with identified hazards while preparing risk management plans