Strategic Mine Research Articles

An application of simultaneous stochastic optimisation on an open-pit copper mining complex with supply, recovery, and market uncertainties

ABSTRACT A mining complex is an integrated value chain that consists of multiple mines, stockpiles, processors, waste dumps, and tailing facilities, and where the in-situ materials mined are transformed into products sold to customers. Simultaneous stochastic optimisation provides a strategic mine planning framework that incorporates all components of a mining complex into a single mathematical representation, capitalising on the synergies between components while considering uncertainty. An application of this optimisation framework at a coper mining complex demonstrates that the incorporation of material supply, market demand, and processing recovery uncertainties into the modelling leads to production schedules that yield a higher NPV and metal recovery.

A Systems Engineering Approach to Incorporate ESG Risks and Opportunities in Early-Stage Mine Design and Planning

This study examines how ESG risks and opportunities can be systematically identified, assessed, and incorporated into the early-stage design and planning of natural resources projects. The focus for this study will be on the mining activities required to source the resources for the global decarbonization effort. The need for a framework to incorporate ESG risks and opportunities into the strategic mine planning process was first identified in the de Beers Sustainability Valuation Approach. The Social Value Capital Decision Model advanced by BHP represents an advance on the de Beers model. This is the first example of a structured methodology for systematically considering stakeholder values and incorporating these into the capital decision framework. To test the applicability of a new approach to mine design by using Quality Function Deployment (QFD), a case study involving a copper mine located in South America was developed. This case study demonstrates how QFD can provide clear line-of-sight to connect design decisions with priority stakeholder concerns. The framework provides a communications tool for aligning the ESG design process across functional silos within complex organizations. The development of appropriate software tools could assist in managing the inherent complexity associated with integrating stakeholder value concerns into early stage design decisions.

Knowledge synthesis on the mine life cycle and the mining value chain to address climate change

The escalating demands for mineral commodities driven by population growth and energy transition are prompting the mining industry to intensify extraction efforts and respond to the imperatives of climate change. Concurrently, the pressure of climate change-related concerns necessitates mining operations to enhance their environmental, economic, and social sustainability. This review aims to synthesize the overlapping knowledge from the mine life cycle (MLC) and the mining value chain (MVC) to distill the practices and strategies for mining's climate action as an extended producer responsibility. The first focus of this study was to examine the concepts of MLC and MVC and their interconnections. Subsequently, an in-depth literature review was conducted to map each stage of the MLC to the value chain regarding mining's climate action and future requirements. Our work pinpointed specific climate-related challenges and opportunities at every juncture, underscoring the importance of strategic mine planning with climate objectives, design for closure, renewables deployment, green mining technologies, agile and lean operations, risk mitigation for premature mine closure, and climate information transparency.

A novel Hybrid XGBoost Methodology in Predicting Penetration Rate of Rotary Based on Rock-Mass and Material Properties

Predicting the drill penetration rate is a fundamental requirement in mining operations, profoundly impacting both the cost-effectiveness of mining activities and strategic mine planning. Given the intricate web of factors influencing rotary drilling performance, the necessity for advanced modeling techniques becomes evident. To this end, the hybrid extreme gradient boosting (XGBoost) was utilized to gauge the penetration rate of rotary drilling machines, utilizing random search, grid search, Harris Hawk optimization (HHO), and the dragonfly algorithm (DA) as metaheuristic algorithms. Our research draws from extensive data collected in copper mine case studies, encompassing both field and investigational data. This dataset incorporates critical material properties, such as tensile strength (TS), uniaxial compressive strength (UCS), as well as vital rock-mass characteristics including joint direction (JD), joint spacing (JS), and bit diameter (D). Our investigation evaluates the reliability of these prediction methods using various performance indicators, including mean absolute error (MAE), root mean square error (RMSE), average absolute relative error (AARE), and coefficient of determination (R2). The multivariate analysis reveals that the HHO-XGB model stands out, demonstrating superior prediction accuracy (MAE: 0.457; RMSE: 2.19; AARE: 2.29; R2: 0.993) compared to alternative models. Furthermore, our sensitivity analysis emphasizes the substantial impact of uniaxial compressive strength and tensile strength on the drill penetration rate. This underlines the importance of considering these material properties in mining operations. In conclusion, our research offers robust models for forecasting the penetration rate of similar rock formations, providing invaluable insights that can significantly enhance mining operations and planning processes.

Geometallurgical Detailing of Plant Operation within Open-Pit Strategic Mine Planning

Mineral and metallurgical processing are crucial within the mineral value chain. These processes involve several stages wherein comminution is arguably the most important due to its high energy consumption, and its impact on subsequent extractive processes. Several geological properties of the orebody impact the efficiency of mineral processing and extractive metallurgy; scholars have therefore proposed to deal with the uncertain ore feed in terms of grades and rock types, incorporating operational modes that represent different plant configurations that provide coordinated system-wide responses. Even though these studies offer insights into how mine planning impacts the ore fed into the plant, the simultaneous optimization of mine plan and metallurgical plant design has been limited by the existing stochastic mine planning algorithms, which have only limited support for detailing operational modes. The present work offers to fill this gap for open-pit mines through a computationally efficient adaptation of a strategic mine planning algorithm. The adaptation incorporates a linear programming representation of the operational modes which forms a Dantzig-Wolfe decomposition, nested within a high-performing stochastic mine planning algorithm based on a variable neighborhood descent metaheuristic. Sample calculations are presented, loosely based on the Mount Isa deposit in Australia, in which a metallurgical plant upgrade is evaluated, showing that the upgraded design significantly decreases the requirement on the mining equipment, without significantly affecting the NPV.

Establishing the quantitative and qualitative limits of the applicability of the multi-stage dumping sequence in open pit mining

ABSTRACT Environmental issues and sustainability are currently being discussed and studied in several areas of mining technology; combining these issues with improved mining operation and management is part of strategic mine planning. Waste management plays an important role in this process, as an alternative way of minimising environmental impacts and achieving more attractive economic scenarios, such as backfilling the pit. This study aimed to evaluate the application of the multi-stage dumping sequence (MSDS) in conjunction with in-pit deposition strategy for a phosphate mine providing general orientations and limits of applicability of the method. The MSDS makes use of temporary waste dumps which gives operational flexibility regarding waste management while reducing the Net Present Cost (NPC) (up to 8% in this study) compared with the traditional and sometimes untouchable rule of no re-handling approach during waste disposal.

A systematic review of artificial intelligence and data-driven approaches in strategic open-pit mine planning

The significant increase in data availability and high-computing power and innovations in real-time monitoring systems enable the technological transformation of the mining industry. Artificial Intelligence (AI) and data-driven methods are becoming appealing solutions to tackle different challenges in mining operations where an increasingly larger body of research is being published. Strategic mine planning is one of the areas that can be greatly enhanced with the adaptation of AI techniques to make intelligent data-driven decisions. This paper presents a systematic literature review to identify research trends in this field both in the specific area of application and the AI technique used. Papers from popular scientific databases were compiled and categorized into three main identified research areas in this field: Production Planning and Scheduling, Equipment Management and Grade Control, and individual AI techniques were catalogued. The results indicated an exponential growth in the general number of publications, where the most consolidated techniques across all applications were Genetic Algorithms and Discrete Simulation.

Inclusion of the geometallurgical variable specific energy in the mine planning using direct block scheduling

In mining projects, it is necessary to have a wide knowledge of the main variables of the mineral deposit before strategic mine planning takes effect. In the meantime, the application of geometallurgy has allowed the modeling of parameters related to the lithologies present in the deposit, such as the specific energy in comminution. This work intends to carry out a mine planning case study with the Direct Block Scheduling (DBS) methodology implemented in the MiningMath software and using the Marvin block model. The results indicate that the processing time of each block required more complex decision-making from the DBS algorithm to fulfill the objectives of mine planning. It is also noticed that the algorithms prioritize the extraction of blocks more released in the first years of the mine, anticipating profits and leaving, for the second half of the life of the project, the intensification of development, aiming to release more blocks for mining.

Methodology to include the comminution specific energy into open-pit strategy mine planning using global optimization

Strategic Mine Planning demands an accurate knowledge of the mineral deposit, being very important the use of block models supported by Geometallurgy. Currently, the methodology used considers the mine planning divided into parts (ultimate open pit, pushbacks, scheduling). Global optimization can guarantee the optimal solution once all steps are solved together. In the present study the database Marvin was used and the geometallurgical variable comminution specific energy was included. In the first scenario, called GeoMet1, a ramp-up was included in the first three years. In the GeoMet2, different maximum movement ranges were defined, as well as the implementation of ore stockpiles and their subsequent rehandling. Gains of 9.66% in NPV and 5.18% in ore production were found for GeoMet2. The second scenario presented ore production adhering to the programmed, in addition to greater stability in the stripping ratio. In addition, GeoMet2 was more efficient in controlling the comminution specific energy. Therefore, the implementation of mass movement limitations allied to the use of stockpiles favored the optimization of the mine scheduling results. It was possible to verify the importance of including the variable comminution specific energy in the block model, to provide more realistic and reliable results

Integration of strategic open-pit mine planning into hierarchical artificial intelligence

The mine production scheduling problem (MPSP) has been studied since the 1960s, and remains an active area of computational research. In extending the concepts of the MPSP, the automated mine may now be regarded as a hierarchical intelligent agent in which the bottom layer consists of distributed robotic equipment, while strategic functionality occupies the higher layers. Here we present a disambiguation of artificial intelligence, machine learning, computational optimization, and automation within the mining context. Specifically, the Q-learning algorithm has been adapted to generate the initial solutions for a high-performing strategic mine planning algorithm, originally developed by Lamghari, Dimitrakopoulos and Ferland, based on the variable neighbourhood descent (VND) metaheuristic. The hierarchical intelligent agent is presented as an integrative conceptual platform, defining the interaction between our new Q-learning adaptation and Lamghari's VND, and potentially other hierarchically controlled components of an artificially intelligent mine, having various degrees of automation. Sample computations involving Q-learning and VND are presented.

Beyond the social license to operate: Whole system approaches for a socially responsible mining industry

The demand for minerals, metals and rare-earth elements is rapidly growing to support the transition to low-carbon energies, and the mining industry must increase its supply while facing complex Environmental, Social and Governance (ESG) risks. Traditionally relying on its engineering expertise to maximize ore extraction, the sector must now find ways to sustain its production while facing increased scrutiny from the public, civil societies and shareholders alike. The paper reviews current practice in sustainability assessment to highlight sector-specific characteristics and the notion of trust as central to effective project developments. Because the social interface of extractive operations is complex, dynamic and non-linear in nature, we recommend going beyond the aim of obtaining a social license to operate and use Systems Thinking to fully embed Corporate Social Responsibility (CSR) at the core of strategic mine planning. System Dynamics can foster interdisciplinary collaborations by bridging together social and technical flows within simulation models to identify sustainable levers of change. We present the development of a stock and flow model quantifying causal mechanisms between the use of cyanide, the environment, communities and public trust, and operational productivity. Industry practitioners, researchers and facilitators can use the model as an adaptable framework to engage with systems modelling in mining. We recommend its use in conjunction with meaningful stakeholder’s engagement to ensure shared understanding, reduced uncertainty and long-term benefits for all.

Evaluating Mine Design Alternatives for Social Risks Using Discrete Choice Analysis

As with other engineering design tasks, mine design involves setting design objectives and constraints (the feasible solution space) and finding the optimal design alternative. Mine engineers often struggle to incorporate the preferences of local community members into their evaluation of mine design alternatives because the mining literature lacks tools to quantify such risks during mine planning. This paper presents an approach to evaluate community acceptance (i.e., community preferences for the alternatives) using discrete choice models and decision-based design during mine planning. Using discrete choice models and a rigorous framework, engineers can estimate the cost of social risks as a function of the probability that individuals in the host community will prefer a particular design alternative. They can then estimate the overall utility of a particular design alternative to the project proponents. This paper illustrates the proposed approach with a strategic mine planning exercise for a gold mine. The framework can be a useful tool for designing mines for sustainability, if combined with effective community engagement and management’s commitment to creating shared value.

Pattern search method for accelerating Stope boundary optimization problem in underground mining operations

The Stope Boundary Optimization (SBO) problem refers to the layout design of production areas (stopes) in an underground mining operation. It falls within the domain of strategic mine planning and a model that addresses this problem aims to maximize profitability, offers optimal management of mineral resources and facilitates decision making around capital investments. The SBO problem is computationally complex and a method that offers solution within a reasonable computation time does not exist. In this article, a combination of a new greedy algorithm and a pattern search technique with an integer programming model is proposed. An implementation of the proposed method on three realistic case studies confirms its efficiency in generating robust solutions with a better optimality gap and considerably less computational effort.

A Review of Models and Algorithms for Surface-Underground Mining Options and Transitions Optimization: Some Lessons Learnt and the Way Forward

It is important that the strategic mine plan makes optimum use of available resources and provides continuous quality ore to drive sustainable mining and profitability. This requires the development of a well-integrated strategy of mining options for surface and/or underground mining and their interactions. Understanding the current tools and methodologies used in the mining industry for surface and underground mining options and transitions planning are essential to dealing with complex and deep-seated deposits that are amenable to both open pit and underground mining. In this study, extensive literature review and a gap analysis matrix are used to identify the limitations and opportunities for further research in surface-underground mining options and transitions optimization for comprehensive resource development planning.

An Application of Simultaneous Stochastic Optimization at a Large Open-Pit Gold Mining Complex under Supply Uncertainty

The simultaneous stochastic optimization of mining complexes optimizes various components of the related mineral value chain jointly while considering material supply (geological) uncertainty. As a result, the optimization process capitalizes on the synergies between the components of the system while not only quantifying and considering geological uncertainty, but also producing strategic mine plans, maximizing the net present value. This paper presents an application of simultaneous stochastic optimization at a large gold mining complex. The complex contains three open-pit mines, three stockpiles, a waste dump, and a processing facility. Material hardness management is integrated at the processing facility. The case study generated production schedules for each mineral deposit considered, as well as an overall assessment of the project and related forecasts. It resulted in an 18 year life-of-asset and identified the semi-autogenous grinder (SAG) mill as the bottleneck of the operation.

Application of Gaussian Mixture Model and Geostatistical Co-simulation for Resource Modeling of Geometallurgical Variables

This work addresses the practice of resource calculation for geometallurgical variables. Similar to mineral resource modeling, estimation domains for geometallurgical variables should be identified first. Then, the geometallurgical variables that are deemed homogeneous need to be modeled separately in each domain. A difficulty for this is related to the geometallurgical variables that can rarely be in agreement with the geological interpretation of a deposit. To circumvent this difficulty, a machine learning approach, namely Gaussian mixture model technique, is employed in an Fe ore deposit to obtain clusters that can turn out the geometallurgical domains. After corroborating that the obtained domains are reasonable from a geometallurgical perspective, a technique is provided to co-simulate the geometallurgical variables within the attained geometallurgical domains following a probabilistic cascade approach. The latter allows incorporation of cross-dependency among the variables that usually are neglected in the modeling process. The algorithm showed that the proposed technique is statistically valid and can be applied for optimum ore processing plant and strategic mine design, where defining the grade alone may not be enough for deciding on further optimization of a mining project. It is also showed that as an instruction, how the proposed algorithm can provide the recovery functions of the geometallurgical variables for resource calculation.

II. ABDÜLHAMİD DÖNEMİNDE TEKİRDAĞ VE ÇEVRESİNDE PETROL VE KÖMÜR ARAMA ÇALIŞMALARI

II. Abdülhamid dönemi, petrolün stratejik bir maden olarak değerlendirilmeye başlandığı bir dönem olma özelliğini taşır. Zira bu dönem Batı’da sanayi devriminin en önemli enerji kaynağı olan kömürün yerini yavaş yavaş petrolün aldığı dönemdir. Bu yüzden, Osmanlı Devleti sınırları dahilinde özellikle petrol ve kömür üretimi için üst düzey devlet adamlarına imtiyazlar verilmeye başlanmıştır. Bu önemli iki stratejik kaynağın araştırılması, tespit edilen kaynakların işletilmesine yönelik olarak pek çok alanda çalışmalar yapılmıştır. Bu çalışmalarda öne çıkan yerler arasında, Osmanlı payitahtına yakınlığının yanı sıra bir liman ve ziraî ürün merkezi konumunda olan Tekirdağ ve çevresi de bulunmaktadır. Bu tebliğde sınaî kalkınma çabalarının baş gösterdiği II. Abdülhamid döneminde, Tekirdağ ve çevresinde kömür ve petrol kaynaklarına yönelik arama çalışmalarına yer verilecektir. Bu çalışmaların olumlu ve olumsuz sonuçlarına dair bilgiler aktarılacaktır.

Method of Selecting Opening Cut Location Using Multi-Criteria Analysis of Decision Variant Mapping

The aim of the article is to present a solution to the research problem that addresses the selection of the opening cut location in the surface mining method. Selecting the opening cut location is a strategic mine planning matter and has a key impact on the overall processes occurring during mining operations. This choice is a complex, and at the same time, mathematically ill-defined issue. The selection procedure should take into account many, often opposing, perspectives of the interveners, who represent the criteria laid down by the technical and organisational, economic, as well as social and environmental groups. In order to be able to compare criteria of a different nature, the authors implemented a multi-criteria method as a solution, derived from operational research. The mathematical tool best suited to the characteristics of the issue of selecting the opening cut location is a method from the ELECTRE family, which was used to create the final solution ranking. The main achievement of the method presented is the specification of a complementary group of assessment criteria and the application of a method allowing a solution to be created, which results in the selection of the most favourable decision variant. The developed method supports decision-makers responsible for making investment decisions in the implementation of mining projects.

Assessing the Impact of Geologic Contact Dilution in Ore/Waste Classification in the Gol-Gohar Iron Ore Mine, Southeastern Iran

Since the Gol-Gohar iron ore mine (GGIOM), which is located in southeastern Iran, is currently one of the biggest iron mines in this region, increasing the accuracy of its mineral resources model has become a challenge for geologists, metallurgists and mining engineers. Given that an accurate classification of the mining blocks into ore or waste is highly significant in strategic mine planning, three approaches for simulating the iron grades were compared against the true grades obtained from production data. The comparison was done by calculating the ratio between the total number of blocks correctly classified as ore and waste and the total number of misclassified blocks, and it was conducted for each approach in three mined benches at the GGIOM. The results reveal that the grade simulation that ignores the geological boundaries and the grade simulation based on a deterministic geological interpretation are much less accurate than the hierarchical approach, which consists of simulating both the geological boundaries and the grades.

A Stochastic Mixed Integer Programming Framework for Underground Mining Production Scheduling Optimization Considering Grade Uncertainty

Conventional mine planning approaches use an estimated orebody model as input to generate optimal production schedules. The smoothing effect of some geostatistical estimation methods cause most of the mine plans and production forecasts to be unrealistic and incomplete. With the development of simulation methods, the risks from grade uncertainty in ore reserves can be measured and managed through a set of equally probable orebody realizations. In order to incorporate grade uncertainty into the strategic mine plan, a stochastic mixed integer programming (SMIP) formulation is presented to optimize an underground cut-and-fill mining production schedule. The objective function of the SMIP model is to maximize the net present value (NPV) of the mining project and minimize the risk of deviation from the production targets. To demonstrate the applicability of the SMIP model, a case study on a cut-and-fill underground gold mining operation is implemented.