An open pit multistage mine production scheduling model for drilling, blasting and excavating operations

This paper proposes a new multi-resource multi-stage scheduling problem for optimising the open-pit drilling, blasting and excavating operations under equipment capacity constraints. The flow process is analysed based on the real-life data from an Australian iron ore mine site. The objective of the model is to maximise the throughput and minimise the total idle times of equipment at each stage. The following comprehensive mining attributes and constraints have been considered: types of equipment; operating capacities of equipment; ready times of equipment; speeds of equipment; block-sequence-dependent movement times of equipment; equipment-assignment-dependent operation times of blocks; distances between each pair of blocks; due windows of blocks; material properties of blocks; swell factors of blocks; and slope requirements of blocks. It is formulated by mixed integer programming and solved by ILOG-CPLEX optimiser. The proposed model is validated with extensive computational experiments to improve mine production efficiency at the operational level. The model also provides an intelligent decision support tool to account for the availability and usage of equipment units for drilling, blasting and excavating stages.

New graph-based algorithms to efficiently solve large scale open pit mining optimisation problems

In the mining optimisation literature, most researchers focused on two strategic-level and tactical-level open-pit mine optimisation problems, which are respectively termed ultimate pit limit (UPIT) or constrained pit limit (CPIT). However, many researchers indicate that the substantial numbers of variables and constraints in real-world instances (e.g., with 50-1000 thousand blocks) make the CPIT’s mixed integer programming (MIP) model intractable for use. Thus, it becomes a considerable challenge to solve the large scale CPIT instances without relying on exact MIP optimiser as well as the complicated MIP relaxation/decomposition methods. To take this challenge, two new graph-based algorithms based on network flow graph and conjunctive graph theory are developed by taking advantage of problem properties. The performance of our proposed algorithms is validated by testing recent large scale benchmark UPIT and CPIT instances’ datasets of MineLib in 2013. In comparison to best known results from MineLib, it is shown that the proposed algorithms outperform other CPIT solution approaches existing in the literature. The proposed graph-based algorithms leads to a more competent mine scheduling optimisation expert system because the third-party MIP optimiser is no longer indispensable and random neighbourhood search is not necessary.

A new open-pit multi-stage mine production timetabling model for drilling, blasting and excavating operations

This paper proposes a new multi-resource multi-stage mine production timetabling problem for optimising the open-pit drilling, blasting and excavating operations under equipment capacity constraints. The flow process is analysed based on the real-life data from an Australian iron ore mine site. The objective of the model is to maximise the throughput and minimise the total idle times of equipment at each stage. The following comprehensive mining attributes and constraints are considered: types of equipment; operating capacities of equipment; ready times of equipment; speeds of equipment; block-sequence-dependent movement times; equipment-assignment-dependent operational times; etc. The model also provides the availability and usage of equipment units at multiple operational stages such as drilling, blasting and excavating stages. The problem is formulated by mixed integer programming and solved by ILOG-CPLEX optimiser. The proposed model is validated with extensive computational experiments to improve mine production efficiency at the operational level.

Data mining for telecommunications network log analysis

Telecommunications network management is based on huge amounts of data that are continuously collected from elements and devices from all around the network. The data is monitored and analysed to provide information for decision making in all operation functions. Knowledge discovery and data mining methods can support fast-pace decision making in network operations. In this thesis, I analyse decision making on different levels of network operations. I identify the requirements decision-making sets for knowledge discovery and data mining tools and methods, and I study resources that are available to them. I then propose two methods for augmenting and applying frequent sets to support everyday decision making. The proposed methods are Comprehensive Log Compression for log data summarisation and Queryable Log Compression for semantic compression of log data. Finally I suggest a model for a continuous knowledge discovery process and outline how it can be implemented and integrated to the existing network operations infrastructure.

An operational-level multi-stage mine production timetabling model for optimally synchronising drilling, blasting and excavating operations

This paper proposes a new multi-stage mine production timetabling (MMPT) model to optimise open-pit mine production operations including drilling, blasting and excavating under real-time mining constraints. The MMPT problem is formulated as a mixed integer programming model and can be optimally solved for small-size MMPT instances by IBM ILOG-CPLEX. Due to NP-hardness, an improved shifting-bottleneck-procedure algorithm based on the extended disjunctive graph is developed to solve large-size MMPT instances in an effective and efficient way. Extensive computational experiments are presented to validate the proposed algorithm that is able to efficiently obtain the near-optimal operational timetable of mining equipment units. The advantages are indicated by sensitivity analysis under various real-life scenarios. The proposed MMPT methodology is promising to be implemented as a tool for mining industry because it is straightforwardly modelled as a standard scheduling model, efficiently solved by the heuristic algorithm, and flexibly expanded by adopting additional industrial constraints.

Incorporating the interrelationship between primary and support processes in monitoring and optimising longwall coal mining efficiency

This thesis increased the researchers understanding of the relationship between operations and maintenance in underground longwall coal mines, using data from a Queensland underground coal mine. The thesis explores various relationships between recorded variables. Issues with human recorded data was uncovered, and results emphasised the significance of variables associated with conveyor operation to explain production.

Australian fly-in, fly-out operations: Impacts on communities, safety workers and their families

Background Australia’s mineral, resource and infrastructure sectors continues to expand as operations in rural and remote locations increasingly rely on fly-in, fly-out or drive-in, drive-out workforces in order to become economically competitive. The issues in employing these workforces are becoming more apparent and include a range of physical, mental, psychosocial, safety and community challenges. Objectives This review aims to consolidate a range of research conducted to communicate potential challenges for industry in relation to a wide variety of issues when engaging and using FIFO/DIDO workforces which includes roster design, working hours, fatigue, safety performance, employee wellbeing, turnover, psychosocial relationships and community concerns. Methods A wide literature review was performed using EBSCOhost and Google Scholar, with a focus on FIFO or DIDO workforces engaged within the resources sector. Results A number of existing gaps in the management of FIFO workforces and potential for future research were identified. This included the identification of various roster designs and hours worked across the resources industry and how to best understand the influences of roster swings, and work hours on fatigue, safety, psychological wellbeing and job satisfaction. Fatigue management, particularly in relation to travelling after extended work shifts can increase the risk for road safety and influence safety performance while at work due to a culmination of long hours, roster cycle and accumulated sleep debt. Further challenges associated with the engagement of this workforce include feelings of isolation, physiological and general health and lifestyle concerns. Conclusions FIFO workforces appear to be at an increased risk physically and mentally due to a wide range of influences of this unique lifestyle, particularly in relation to rosters, length of shift and feelings of community disengagement. Research and data collected has been limited in understanding the influences on employee engagement, satisfaction, retention and safety. Ensuring the challenges associated with FIFO employment are understood, addressed and communicated to workers and their families may assist.

Root cause diagnostics using temporal data mining

A method, system, and computer program product for fault data correlation in a diagnostic system are provided. The method includes receiving the fault data including a plurality of faults collected over a period of time, and identifying a plurality of episodes within the fault data, where each episode includes a sequence of the faults. The method further includes calculating a frequency of the episodes within the fault data, calculating a correlation confidence of the faults relative to the episodes as a function of the frequency of the episodes, and outputting a report of the faults with the correlation confidence.

System and Method for mining of Temporal Data

A system for temporal data mining includes a computer readable medium having an application configured to receive at an input module a temporal data series having events with start times and end times, a set of allowed dwelling times and a threshold frequency. The system is further configured to identify, using a candidate identification and tracking module, one or more occurrences in the temporal data series of a candidate episode and increment a count for each identified occurrence. The system is also configured to produce at an output module an output for those episodes whose count of occurrences results in a frequency exceeding the threshold frequency.

Isotopic imprints of mountaintop mining contaminants.

Mountaintop mining (MTM) is the primary procedure for surface coal exploration within the central Appalachian region of the eastern United States, and it is known to contaminate streams in local watersheds. In this study, we measured the chemical and isotopic compositions of water samples from MTM-impacted tributaries and streams in the Mud River watershed in West Virginia. We systematically document the isotopic compositions of three major constituents: sulfur isotopes in sulfate (δ(34)SSO4), carbon isotopes in dissolved inorganic carbon (δ(13)CDIC), and strontium isotopes ((87)Sr/(86)Sr). The data show that δ(34)SSO4, δ(13)CDIC, Sr/Ca, and (87)Sr/(86)Sr measured in saline- and selenium-rich MTM impacted tributaries are distinguishable from those of the surface water upstream of mining impacts. These tracers can therefore be used to delineate and quantify the impact of MTM in watersheds. High Sr/Ca and low (87)Sr/(86)Sr characterize tributaries that originated from active MTM areas, while tributaries from reclaimed MTM areas had low Sr/Ca and high (87)Sr/(86)Sr. Leaching experiments of rocks from the watershed show that pyrite oxidation and carbonate dissolution control the solute chemistry with distinct (87)Sr/(86)Sr ratios characterizing different rock sources. We propose that MTM operations that access the deeper Kanawha Formation generate residual mined rocks in valley fills from which effluents with distinctive (87)Sr/(86)Sr and Sr/Ca imprints affect the quality of the Appalachian watersheds.

Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning Techniques

Die zunehmende Vernetzung der Informations- und Kommunikationssysteme führt zu einer weiteren Erhöhung der Komplexität und damit auch zu einer weiteren Zunahme von Sicherheitslücken. Klassische Schutzmechanismen wie Firewall-Systeme und Anti-Malware-Lösungen bieten schon lange keinen Schutz mehr vor Eindringversuchen in IT-Infrastrukturen. Als ein sehr wirkungsvolles Instrument zum Schutz gegenüber Cyber-Attacken haben sich hierbei die Intrusion Detection Systeme (IDS) etabliert. Solche Systeme sammeln und analysieren Informationen von Netzwerkkomponenten und Rechnern, um ungewöhnliches Verhalten und Sicherheitsverletzungen automatisiert festzustellen. Während signatur-basierte Ansätze nur bereits bekannte Angriffsmuster detektieren können, sind anomalie-basierte IDS auch in der Lage, neue bisher unbekannte Angriffe (Zero-Day-Attacks) frühzeitig zu erkennen. Das Kernproblem von Intrusion Detection Systeme besteht jedoch in der optimalen Verarbeitung der gewaltigen Netzdaten und der Entwicklung eines in Echtzeit arbeitenden adaptiven Erkennungsmodells. Um diese Herausforderungen lösen zu können, stellt diese Dissertation ein Framework bereit, das aus zwei Hauptteilen besteht. Der erste Teil, OptiFilter genannt, verwendet ein dynamisches "Queuing Concept", um die zahlreich anfallenden Netzdaten weiter zu verarbeiten, baut fortlaufend Netzverbindungen auf, und exportiert strukturierte Input-Daten für das IDS. Den zweiten Teil stellt ein adaptiver Klassifikator dar, der ein Klassifikator-Modell basierend auf "Enhanced Growing Hierarchical Self Organizing Map" (EGHSOM), ein Modell für Netzwerk Normalzustand (NNB) und ein "Update Model" umfasst. In dem OptiFilter werden Tcpdump und SNMP traps benutzt, um die Netzwerkpakete und Hostereignisse fortlaufend zu aggregieren. Diese aggregierten Netzwerkpackete und Hostereignisse werden weiter analysiert und in Verbindungsvektoren umgewandelt. Zur Verbesserung der Erkennungsrate des adaptiven Klassifikators wird das künstliche neuronale Netz GHSOM intensiv untersucht und wesentlich weiterentwickelt. In dieser Dissertation werden unterschiedliche Ansätze vorgeschlagen und diskutiert. So wird eine classification-confidence margin threshold definiert, um die unbekannten bösartigen Verbindungen aufzudecken, die Stabilität der Wachstumstopologie durch neuartige Ansätze für die Initialisierung der Gewichtvektoren und durch die Stärkung der Winner Neuronen erhöht, und ein selbst-adaptives Verfahren eingeführt, um das Modell ständig aktualisieren zu können. Darüber hinaus besteht die Hauptaufgabe des NNB-Modells in der weiteren Untersuchung der erkannten unbekannten Verbindungen von der EGHSOM und der Überprüfung, ob sie normal sind. Jedoch, ändern sich die Netzverkehrsdaten wegen des Concept drif Phänomens ständig, was in Echtzeit zur Erzeugung nicht stationärer Netzdaten führt. Dieses Phänomen wird von dem Update-Modell besser kontrolliert. Das EGHSOM-Modell kann die neuen Anomalien effektiv erkennen und das NNB-Model passt die Änderungen in Netzdaten optimal an. Bei den experimentellen Untersuchungen hat das Framework erfolgversprechende Ergebnisse gezeigt. Im ersten Experiment wurde das Framework in Offline-Betriebsmodus evaluiert. Der OptiFilter wurde mit offline-, synthetischen- und realistischen Daten ausgewertet. Der adaptive Klassifikator wurde mit dem 10-Fold Cross Validation Verfahren evaluiert, um dessen Genauigkeit abzuschätzen. Im zweiten Experiment wurde das Framework auf einer 1 bis 10 GB Netzwerkstrecke installiert und im Online-Betriebsmodus in Echtzeit ausgewertet. Der OptiFilter hat erfolgreich die gewaltige Menge von Netzdaten in die strukturierten Verbindungsvektoren umgewandelt und der adaptive Klassifikator hat sie präzise klassifiziert. Die Vergleichsstudie zwischen dem entwickelten Framework und anderen bekannten IDS-Ansätzen zeigt, dass der vorgeschlagene IDSFramework alle anderen Ansätze übertrifft. Dies lässt sich auf folgende Kernpunkte zurückführen: Bearbeitung der gesammelten Netzdaten, Erreichung der besten Performanz (wie die Gesamtgenauigkeit), Detektieren unbekannter Verbindungen und Entwicklung des in Echtzeit arbeitenden Erkennungsmodells von Eindringversuchen.

Challenges with minimising mercury pollution in the small-scale gold mining sector: Experiences from the Guianas

This paper examines the barriers to mitigating mercury pollution at small-scale gold mines in the Guianas (Guyana, French Guiana and Suriname), and prescribes recommendations for overcoming these obstacles. Whilst considerable attention has been paid to analysing the environmental impacts of operations in the region, minimal research has been undertaken to identify appropriate policy and educational initiatives for addressing the mounting mercury problem. Findings from recent fieldwork and selected interviews with operators from Guyanese and Surinamese gold mining regions reveal that legislative incapacity, the region's varied industry policy stances, various technological problems, and low environmental awareness on the part of communities are impeding efforts to facilitate improved mercury management at small-scale gold mines in the Guianas. Marked improvements can be achieved, however, if legislation, particularly that pertaining to mercury, is harmonised in the region; educational seminars continue to be held in important mining districts; and additional outlets for disseminating environmental equipment and mercury-free technologies are provided.

Livelihood diversification and the expansion of artisanal mining in rural Tanzania: drivers and policy implications

This paper provides an extended analysis of livelihood diversification in rural Tanzania, with special emphasis on artisanal and small-scale mining (ASM). Over the past decade, this sector of industry, which is labour-intensive and comprises an array of rudimentary and semi-mechanized operations, has become an indispensable economic activity throughout Sub-Saharan Africa, providing employment to a host of redundant public sector workers, retrenched large-scale mine labourers and poor farmers. In many of the region’s rural areas, it is overtaking subsistence agriculture as the primary industry. Such a pattern appears to be unfolding within the Morogoro and Mbeya regions of southern Tanzania, where findings from recent research suggest that a growing number of smallholder farmers are turning to ASM for employment and financial support. It is imperative that national rural development programmes take this trend into account and provide support to these people.

'Agricultural poverty' and the expansion of artisanal mining in sub-Saharan Africa: Experiences from southwest Mali and southeast Ghana

Why do people engage in artisanal and small-scale mining (ASM) – labour-intensive mineral extraction and processing activity – across sub-Saharan Africa? This paper argues that ‘agricultural poverty’, or hardship induced by an over-dependency on farming for survival, has fuelled the recent rapid expansion of ASM operations throughout the region. The diminished viability of smallholder farming in an era of globalization and overreliance on rain-fed crop production restricted by seasonality has led hundreds of thousands of rural African families to ‘branch out’ into ASM, a move made to secure supplementary incomes. Experiences from Komana West in Southwest Mali and East Akim District in Southeast Ghana are drawn upon to illustrate how a movement into the ASM economy has impacted farm families, economically, in many rural stretches of sub-Saharan Africa.

Winners and losers from industry reforms in the developing world: experiences from the electricity and mining sectors

The reform of previously state-owned and operated industries in many Less Developed Countries (LDCs) provide contrary experiences to those in the developed world, which have generally had more equitable distributional impacts. The economic reform policies proposed by the so-called 'Washington Consensus' state that privatisation provides governments with opportunities to raise revenues through the sale of under-performing and indebted state industries, thereby reducing significant fiscal burdens, and, at the same time, facilitating influxes of foreign capital, skills and technology, with the aim of improving operations and a "trickle-down" of benefits. However, experiences in many LDCs over the last 15-20 years suggest that reform has not solved the problem of chronic public-sector debt, and that poverty and socio-economic inequalities have increased during this period of 'neo-liberal' economics. This paper does not seek to challenge the policies themselves, but rather argues that the context in which reform has often taken place is of fundamental significance. The industry-centric policy advice provided by the IFIs typically causes a 'lock-in' of inequitably distributed 'efficiency gains', providing minimal, if any, benefits to impoverished groups. These arguments are made using case study analysis from the electricity and mining sectors.