Mustafa Kumral’s research while affiliated with McGill University and other places

Capacity planning is a particular problem in the minerals industry because it depends on the heterogeneity and accessibility of the ore in the deposit, as well as on demand. If the ore is uniformly distributed within the deposit, capacity planning will be a function of project financing and demand. However, in many deposits, the ore is unevenly distributed, and it is not possible to access the ore as desired. Overlying ore and waste, considering slope angles, should be extracted earlier to access specific ore locations. Also, measuring the degree of EoS, the ratio of variable costs to total cost, called the “capacity factor,” also affects capacity planning. This research explores the critical relationship capacity planning, block sequencing, and economies of scale (EoS). By integrating EoS into a comprehensive cost-capacity relationship framework, a novel model that enhances traditional mine planning methodologies is proposed. A case study of an old gold deposit demonstrates the proposed model’s practical applicability. Results show that lower capacity factors (i.e., high fixed and low variable operating costs) for mining systems associated with large-capacity mining equipment significantly enhance net present value (NPV) compared to small-capacity equipment. On the other hand, the capacity factor for mineral processing systems has a less significant effect on NPV maximization when compared with the mining capacity factor. This emphasizes that the importance of EoS in mining systems is higher than in mineral processing systems. Even though the analysis suggests that the optimum capacity and the related NPV vary with different degrees of EoS, NPV is maximized with lower capacity factors for the mining system and moderate capacity factors for the mineral processing system. This observation highlights the critical need for accurate estimation of fixed and variable costs, as well as the degree of EoS, for a project.

Understanding the nature of connections and causal relationships between economic variables is crucial, especially in industries with significant uncertainties, such as the mineral industry. More accurately capturing causal relationships provides greater insight into engineering and economic phenomena. This paper aims to demonstrate the causal relationships of some phenomena in mineral economies through novel approaches. The proposed approach has a twofold focus: (1) wavelet analysis, a robust and adaptable technique for investigating the time–frequency domain, and (2) the connectedness method, which detects interconnected relationships between variables. Wavelet analysis is presented by the wavelet coherence methodology, which is used to determine the degree of dynamic correlation or causal links between two time series across different time scales. In other words, it unlocks the interactions of the variables across low, medium, and high time scales. Furthermore, the research expands the connectedness method to consider the interconnections between elements in a system, recognizing that modifications in one aspect could have a cascading effect on the entire system rather than individual components. Case studies are conducted on a dataset that includes nickel prices and various financial variables such as Shanghai indices, the ratios of currencies, and the 6-month bonds of the USA and Canada. The analysis using both methods identifies nickel as a key shock sender, particularly influencing the Shanghai indices, Canadian bonds, and the Canadian dollar and United States dollar exchange rate. In some cases, the results of the two methods presented conflicting outcomes. These findings highlight nickel’s significant but varying impact on global financial markets, emphasizing the need for multiple methods to capture its complex nature. The results show wavelet coherence detects associations and causal links in short-, medium-, and long-term cases. The connectedness approach demonstrates how chosen variables are interconnected within the chosen sample by detecting the shock transmission between variables. The variables are transmitting or receiving the shocks, and the transmitters influence variables. The findings suggest that the wavelet coherence and connectedness approach can be valuable tools for decision-making and risk management in the mineral industry.

Dig-limits optimization is one of the most important steps in the grade control process at open-pit mines. It aims to send blasted materials to their optimal destinations to maximize the profitability of mining projects. Grade and blast movement are key uncertainties that affect the optimal determination of dig-limits. This paper presents an integrated workflow for optimizing dig-limits under grade and blast movement uncertainties. The proposed methodology incorporates these uncertainties into the grade control process to enhance material classification and destination optimization, thereby minimizing ore loss and dilution. A multivariate geostatistical simulation workflow is developed to capture spatial uncertainties in grade distribution and blast movement distance and direction. By applying projection pursuit multivariate transformation and sequential Gaussian simulation for modeling blast movement distances at all locations and flitches within the bench section, the anticipated D-like shape from blasting is reproduced, and uncertainty is quantified. The maximum expected profit method effectively determines optimal material destinations under uncertainty improving overall mining profitability. The proposed risk-based dig-limits optimization model accounts for mining equipment selectivity, irregular bench shapes, and varying orebody orientations, resulting in operational and economically viable dig-limits. A case study on a porphyry copper deposit demonstrated the significant impact of blast movement on ore loss and dilution, emphasizing the need for accurate blast movement modeling and its integration into grade control procedures. By accounting for differential blast movement, the proposed workflow ensures reliable post-blast material classifications, reducing suboptimal decisions, thus improving project profitability and operational efficiency.

Mining machinery constitutes essential assets for a mining corporation. Due to economies of scale, technological innovations and stringent quality and safety requirements, the size, complexity, functionality and diversity of industrial machinery have expanded markedly over the last two decades. This growth has increased sensitivity to machine availability and reliability. Mining operations install comprehensive maintenance units tasked with inspection, repair, replacement and inventory management for the machines in use. Leveraging the proliferation of sensor technologies integrated within the machines, maintenance units obtain rich data streams synchronously disclosing machine health and performance metrics, which enables a predictive maintenance programme. This programme performs prognostic detections of anomalies and permits timely intervention to avert catastrophic breakdowns. However, such sensor-driven predictive maintenance scheme for machinery in the mining sector is limited. The present paper utilises the Gaussian process, a powerful predictive modelling technique, to show its potential in addressing this challenge. The efficacy of this approach is validated through three case studies. Each case study is equipped with sensor data and represents a typical predictive maintenance task for mining assets. The developed Gaussian process models successfully capture meaningful temporal patterns in sensor data and generate credible predictions across all three tasks: temporal prediction of sensor data degradation trends, remaining useful lifespan prediction and simultaneous monitoring and prediction of multiple machine conditions. Furthermore, the models offer uncertainty estimates to the prediction outcomes, potentially facilitating maintenance decision-making process.

Iron ore has had a highly global market since setting a new pricing mechanism in 2008. With current dollar values, iron ore concentrate for sale price, which was $39 per tonne (62$218 per tonne (62% Fe) in mid-2021. It is hovering around $120 in October 2023 (cf. https://tradingeconomics.com/commodity/iron-ore). The uncertainty associated with these fluctuations creates hardship for iron ore mine operators and steelmakers in planning mine development and making future sale agreements. Therefore, iron ore price forecasting is of special importance. This paper proposes a cutting-edge multi-echelon tandem learning (METL) model to forecast iron ore prices. This model comprises variational mode decomposition (VMD), multi-head convolutional neural network (MCNN), stacked long short-term-memory (SLSTM) network, and attention mechanism (AT). In the proposed METL (i.e., the combination of VMD, MCNN, SLSTM, AT) model, the VMD decomposes the time series data into sub-sequential modes for better measuring volatility. Then, the MCNN is applied as an encoder to extract spatial features from the decomposed sub-sequential modes. The SLSTM network is adopted as a decoder to extract temporal features. Finally, the AT is employed to capture spatial–temporal features to obtain the complete forecasting process. Extensive computational experiments are conducted based on daily-based and weekly-based iron ore price datasets with different time scales. It was validated that the proposed METL model outperformed its single-echelon and other categorized models by 10–65% in range. The proposed METL model can improve the prediction accuracy of iron ore prices and thus help mining and steelmaking enterprises to determine their sale or purchase strategies.

Purpose This study aims to address the critical issue of machine breakdowns in industrial settings, which jeopardize operation economy, worker safety, productivity and environmental compliance. It explores the efficacy of a predictive maintenance program in mitigating these risks by proactively identifying and minimizing failures, thereby optimizing maintenance activities for higher efficiency. Design/methodology/approach The article implements Logical Analysis of Data (LAD) as a predictive maintenance approach on an industrial machine maintenance dataset. The aim is to (1) detect failure presence and (2) determine specific failure modes. Data resampling is applied to address asymmetrical class distribution. Findings LAD demonstrates its interpretability by extracting patterns facilitating the failure diagnosis. Results indicate that, in the first case study, LAD exhibits a high recall value for failure records within a balanced dataset. In the second case study involving smaller-scale datasets, enhancement across all evaluation metrics is observed when data is balanced and remains robust in the presence of imbalance, albeit with nuanced differences in between. Originality/value This research highlights the importance of transparency in predictive maintenance programs. The research shows the effectiveness of LAD in detecting failures and identifying specific failure modes from diagnostic sensor data. This maintenance strategy exhibits its distinction by offering explainable failure patterns for maintenance teams. The patterns facilitate the failure cause-effect analysis and serve as the core for failure prediction. Hence, this program has the potential to enhance machine reliability, availability and maintainability in industrial environments.

In 2024, Resources Policy reaches its 50th anniversary as a journal. Fifty years leading the field of mineral and fossil fuel policies and economic research worldwide. Considering this special milestone, we provide a forward-looking view in this paper, highlighting seven areas we believe are critical for robust research that Resources Policy should publish in the future. Leveraging our research expertise and knowledge with the journal, these seven areas of future research include implications of post-mining and energy transitions, the dark side of critical minerals, the increasing substitution of local labour by alternative inputs, the role of the resource curse in resilience considerations, the cleaner production role of mining, macroeconomic frameworks, and the future of mining beyond mines (deep-sea and space mining). We believe more research is needed in these seven research areas, which can enhance our understanding of critical aspects, reduce uncertainty, and provide novel ways to address societal, environmental, economic and policy challenges related to the extraction and use of minerals and fossil fuels. Resources Policy is … devoted to the economics and policy issues related to mineral and fossil fuel extraction, production and use. The journal content … analyses issues of public policy, economics, social science, geography, and finance in the areas of mining, minerals, fossil fuels and metals. (Resources Policy Aims and Scope, 2024)

Strategic mine planning centers on solving cut-off grade selection, capacity planning, and block sequencing. Even though significant knowledge has been accumulated on mine planning over the last decades, there is still potential to add value to mineral sourcing by addressing various aspects. To this end, this paper addresses two issues. The effects of (1) grade/metal distribution within a mineral deposit and (2) the economies of scale (EoS) are explored in cut-off grade selection and capacity planning. In doing so, the interdependency between cut-off grade selection and capacity planning is also considered. A case study is implemented on a metallic deposit whose grade distribution exhibits lognormal distribution to detect if grade/metal distribution influences cut-off grade selection. Also, based on the same ore tonnage and metal quantity, six different datasets are generated with different shape and scale factors. The research outcomes indicate that deposits with lower shape and scale factors of lognormal distribution are more sensitive to metal price and discount rate changes because slight cut-off grade variations significantly change net present value (NPV). While the NPV of the deposit with the largest shape factor is $3,208,112,841 with a cut-off grade of 0.058 oz/tonne, the NPV of the deposit with the smallest shape factor is$93,617,240 with a cut-off grade of 0.027 oz/tonne. Furthermore, the case study is directed to investigate the effect of EoS on a project’s value, with a specific emphasis on the ratio of variable cost to total cost (capacity factor). Two different regression analyses are conducted based on the proposed model for optimal capacity planning and cut-off grade selection, respectively. In the first one, the absolute standardized beta values for EoS of mining and mineral processing costs are 0.736 and 0.425, meaning that capacity planning is highly sensitive to the EoS of mining and mineral processing operating costs. Meanwhile, the absolute standardized beta value for grade variability is 0.054 which means that the effects of grade variability and metal distribution are almost negligible for capacity planning. However, EoS is the most critical variable for capacity optimization. In the second regression analysis, the standardized beta values for grade variability and EoS of mineral processing operating cost are 0.573 and 0.522, so their effects on cut-off grade selection become vital.

The three main optimisation components of sublevel stoping methods are stope layout, production schedule (or stope sequencing) and access networks. The joint optimisation of these components could further add value to an underground mining project. This potential has not been considered in the literature due to computational difficulties, and the problem was solved sequentially. This paper proposes a new joint optimisation model to integrate these components. In addition, the proposed optimisation model incorporates stochastic simulations to capture uncertainty and variability associated with the grades of the related mineral deposits mined. The optimisation model is based on a two-stage stochastic integer programming (SIP) formulation that maximises the project's net present value (NPV) and minimises the planned dilution. Applying the proposed method at a small copper deposit shows that the SIP outperforms the results obtained from mixed integer programming. For a seven-year mine life, the SIP model generated ∼20% more NPV, demonstrating the importance of developing a joint optimisation formulation and accounting for grade uncertainty and variability.

In the minerals industry, inadequately addressing technical, economic, social, environmental, and geological uncertainties can lead to poor decisions and unexpected outcomes, such as financial losses, accidents, and liabilities. Correlation analysis is widely used in minerals-related research to estimate variables, but erroneous inferences can be made about causal relationships between variables, leading to higher risk, for example, relationships between discount rate and commodity price, interest rate and inflation, energy costs and gold price, vibration and component wear in mining equipment, and abrasive mineral characteristics and drill bit wear. Therefore, mine valuation and risk analysis in the minerals industry require a strong understanding of the nature of associations between variables. The present paper demonstrates how causality could be used in the mining industry. Four tests were implemented and compared through two case studies. The cointegration test revealed the presence of a long-term connection between cointegrated variables. The Granger, variable-lag Granger, and Toda-Yamamoto causality tests analyzed the nature, lag, and direction of causal relationships between variables. Due to its dynamic time-warping algorithm, the variable-lag Granger causality test showed a robust causal association without any attachments to the possible lag or direction. Two case studies showed that causality tests best facilitate decision-making in the minerals industry by improving understanding of associations between variables.