Production Scheduling and Waste Disposal Planning for Oil Sands Mining Using Goal Programming

In oil sands mining, timely provisions of ore and tailings containment with less environmental footprints are the main drivers of profitability and sustainability. The recent Alberta Energy Resources Conservation Board Directive 074 requires oil sands waste disposal planning to be an integral part of mine planning. This requires the development of a well integrated strategy of directional mining and tailings dyke construction for in-pit and ex-pit tailings storage management. The objectives of this paper are to: 1) determine the order and time of extraction of ore, dyke material and waste that maximizes the net present value; 2) determine the destination of dyke material that minimizes construction cost; and 3) minimize deviations from the production goals of the mining operation. We have developed, implemented, and verified a theoretical optimization framework based on mixed integer linear goal programming (MILGP) to address these objectives. This study presents an integration of mixed integer linear programming and goal programming in solving large scale mine planning optimization problems using clustering and pushback techniques. Application of the MILGP model was presented with an oil sands mining case. The MILGP model generated a smooth and uniform mining schedule that generates value and provides a robust framework for effective waste disposal planning. The results show that mining progresses with an ore to waste ratio of 1:1.5 throughout the mine life, generating an overall net present value of $14,237M. This approach improves the sustainable development of oil sands through better waste management

Effects of spatial resolution,land-cover heterogeneity and different classification methods on accuracy of land-cover mapping

Despite improved spatial and spectral characteristics of satellite and aerial imaging systems, land-cover classification is still challenged by a continuously evolving and complex rural and urban landscape conditions resulting from diverse land-use scenarios. Sizes and material composition of impervious surfaces changes greatly from urban to rural areas, leading to varying spectral signatures and ultimately misclassification. This creates a challenge in choosing suitable classification algorithms and image processing methods. In this study, the influence of spatial resolution and land-cover spectral and spatial heterogeneity on accuracy of land-cover classification at a rural-urban interface was examined alongside comparison of Random Forest (RF) and Support Vector Machine (SVM) classification algorithms. Further, the performance of spectral unmixing strategies was tested against standard feature extraction methods, namely, NAPCA and PCA. The results showed a 10 % improvement in classification accuracy from 30 m to 10 m spatial resolution for both overall accuracy and Kappa coefficients, however, relatively high per-pixel class disagreement (39 %) was recorded between the different resolution maps, pointing to the fact that overall accuracy or Kappa coefficients may not capture the spatial resolution effects on classification accuracy results in its entirety. SVM classifier proved superior to the RF classifier with even a relatively bigger margin at the coarser spatial resolution (i.e. 4.9 % and 5.7 % higher accuracy at 10 m and 30 m spatial resolution respectively). Higher classification accuracies were observed for partial unmixing and sum-to-unity unmixing feature extraction strategies at both spatial resolutions relative to the results from PCA, NAPCA and original image data (i.e. 62 %, 61 %, 51 %, 61 % and 59 % respectively for 30 m resolution, and, 67 %, 67 %, 62 %, 65 % and 66 % respectively for 10 m resolution image). It was found that the dominance of unmixing-based feature extraction methods reduced while the standard dimensionality reduction approaches (NAPCA and PCA) made a zero contribution to improving classification accuracy at finer spatial resolution (i.e. 10 m). According to the results of land-cover heterogeneity assessment, more fragmented and spatially diverse landscapes were comparably more spectrally diverse along the rural-urban gradient. A high degree of landscape heterogeneity and lowest classification accuracy was observed in the peri-urban region at approximately 11 kilometers from the very urban area. The findings indicate that landscapes with high PD, LSI, SHDI and low CONTAG have lower accuracy while homogeneous and less fragmented landscapes have higher accuracy. The findings from the study will provide a basis for more accurate time series analysis of land-use dynamics at the rural-urban interface

Responsiveness of Mining Community Acceptance Model to Key Parameter Changes

The mining industry has difficulties predicting changes in the level of community acceptance of its projects over time. These changes are due to changes in the society and individual perceptions around these mines as a result of the mines\u27 environmental and social impacts. Agent-based modeling can be used to facilitate better understanding of how community acceptance changes with changing mine environmental impacts. This work investigates the sensitivity of an agent-based model (ABM) for predicting changes in community acceptance of a mining project due to information diffusion to key input parameters. Specifically, this study investigates the responsiveness of the ABM to average degree (total number of friends) of the social network, close neighbour ratio (a measure of homophily in the social network) and number of early adopters ( innovators ). A two-level full factorial experiment was used to investigate the sensitivity of the model to these parameters. The primary (main), secondary and tertiary effects of each parameter were estimated to assess the model\u27s sensitivity. The results show that the model is more responsive to close neighbour ratio and number of early adopters than average degree. Consequently, uncertainty surrounding the inferences drawn from simulation experiments using the agent-based model will be minimized by obtaining more reliable estimates of close neighbour ratio and number of early adopters. While it is possible to reliably estimate the level of early adopters from the literature, the degree of homophily (close neighbour ratio) has to be estimated from surveys that can be expensive and unreliable. Further, work is required to find economic ways to document relevant degrees of homophily in social networks in mining communities

Identifying the Presence of AMD-Derived Soil CO₂ in Field Investigations Using Isotope Ratios

Recent incidents of hazardous accumulations of CO2 in homes on or adjacent to reclaimed mine land have been shown to be linked to neutralization reactions between acidic mine drainage and carbonate material. An efficient and economic method is necessary to identify the presence of acid mine drainage- (AMD-) derived CO2 on reclaimed mine land, prior to construction. One approach to identify the presence of AMD-derived CO2 is to characterize stable carbon isotope ratios of soil CO2. To do so, a viable method is necessary to acquire soil gas samples for isotope ratio analysis. This paper presents preliminary investigations of the effectiveness of two methods of acquiring gas samples (sampling during soil flux measurements and using slam bar) for isotope analysis. The results indicate that direct soil gas sampling is cheaper and provides better results. Neither method is adequate without accounting for temporal effects due to changing gas transport mechanisms. These results have significant implications for safe post-mining land uses and future investigations of leakages from geologic carbon sequestration sites

Eliciting Drivers of Community Perceptions of Mining Projects through Effective Community Engagement

Sustainable mining has received much attention in recent years as a consequence of the negative impacts of mining and public awareness. The aim of this paper is to provide mining companies guidance on improving the sustainability of their sites through effective community engagement based on recent advances in the literature. It begins with a review of the literature on sustainable development and its relationship to stakeholder engagement. It then uses the literature to determine the dominant factors that affect community perceptions of mining projects. These factors are classified into five categories: environmental, economic, social, governance and demographic factors. Then, we propose a new two-stage method based on discrete choice theory and the classification that can improve stakeholder engagement and be cost-effective. Further work is required to validate the proposed method, although it shows potential to overcome some of the challenges plaguing current approaches

Corporate Social Responsibility: Understanding the Mining Stakeholder with a Case Study

The social responsibility of corporate mining has been challenged by a significant socio-political risk from local communities. These issues reduce shareholder value by increasing costs and decreasing the market perception of corporate social responsibility. Community engagement is the process of understanding the behavior and interests of a group of targeted mining communities through surveys and data analysis, with the purpose of incorporating mining community acceptance into the mining sustainability. While mining organizations have discussed community engagement to varying degrees, there are three main shortcomings in current studies, as concluded in the authors\u27 previous research. This paper presents a framework to apply discrete choice theory to improve mining community engagement and corporate mining social responsibility. In addition, this paper establishes the main technical challenges to implement the developed framework, and presents methods to overcome the challenges for future research with a case study. The contribution of this research will transform mine sustainability in a fundamental way by facilitating the incorporation of effective community engagement. This will lead to more sustainable mines that local communities support

Reduction in the Energy Cost of Minerals through at-the-face Comminution and Separation of Mineral and Waste [abstract]

Only abstract of poster available.Track III: Energy InfrastructureHigh-pressure waterjets penetrate into material through the pressurization and growth of small cracks within the target surface. In mineral ores the individual grains of the constituent components are defined by the grain boundaries and these provide such surface cracks. Eroding the ore by a stream of high-pressure water can thus exploit the cracks so that they grow, inter-connect and remove the ore on a grain by grain basis. This separates out the individual components of the ore, as the ore is mined. Because the properties of the different mineral grains differ, either in size, density or shape they can be separated, often quite easily, at the mining machine, as the grains are collected after being removed from the face. Thus, at the point of mining, the valuable components of the ore can be separated and collected. The remaining waste minerals can then be left adjacent to the mining face, potentially being re-cemented to provide support to the ongoing excavation. This joint mining and separation process saves the cost of transporting the waste rock out of the mine, and the costs of conventional separation of the valuable material at the surface. In current practice, all the ore mined is crushed, at the surface, to a very fine powder in order to achieve liberation of the valuable mineral. As well as requiring considerably more energy this also produces a very fine waste product, which is more expensive to dispose of, often behind large tailings dams at the surface, at an environmental cost. The use of pressurized cavitation to enhance the process, and reduce energy needs and process time is a part of this work. This new process is anticipated to drop the energy cost of mineral production by up to 60% and has been validated in laboratory and some field tests

The Status of the Local Community in Mining Sustainable Development beyond the Triple Bottom Line

Mineral products provide essential fuels and raw materials for industrialization and our daily life, but their influences on other aspects of life need to be taken into consideration. While the whole world benefits from mining\u27s contributions, most of the resulting detrimental impacts on the environment and society fall on the local communities. The participation of the local community is one solution to decrease the risks from community-related problems. Subsequently, the requirements of mining sustainable development can be met. A literature review was conducted on mining sustainability and stakeholder participation, and the shortcomings of existing research and difficulties of further study were discussed in detail. This study covers a broad understanding of mining sustainability from a mining community\u27s perspective. In addition, it offers a new mining sustainability scope based on the literature review. Besides the balance of economic, environmental, and social aspects, the mine owner and local community have to be engaged in the new mining sustainability scope. This literature review could improve community engagement and help mining companies to better understand local mining communities

Probabilistic Mapping and Spatial Pattern Analysis of Grazing Lawns in Southern African Savannahs Using WorldView-3 Imagery and Machine Learning Techniques

Savannah grazing lawns are a key food resource for large herbivores such as blue wildebeest (Connochaetes taurinus), hippopotamus (Hippopotamus amphibius) and white rhino (Ceratotherium simum), and impact herbivore densities, movement and recruitment rates. They also exert a strong influence on fire behaviour including frequency, intensity and spread. Thus, variation in grazing lawn cover can have a profound impact on broader savannah ecosystem dynamics. However, knowledge of their present cover and distribution is limited. Importantly, we lack a robust, broad-scale approach for detecting and monitoring grazing lawns, which is critical to enhancing understanding of the ecology of these vital grassland systems. We selected two sites in the Lower Sabie and Satara regions of Kruger National Park, South Africa with mesic and semiarid conditions, respectively. Using spectral and texture features derived from WorldView-3 imagery, we (i) parameterised and assessed the quality of Random Forest (RF), Support Vector Machines (SVM), Classification and Regression Trees (CART) and Multilayer Perceptron (MLP) models for general discrimination of plant functional types (PFTs) within a sub-area of the Lower Sabie landscape, and (ii) compared model performance for probabilistic mapping of grazing lawns in the broader Lower Sabie and Satara landscapes. Further, we used spatial metrics to analyse spatial patterns in grazing lawn distribution in both landscapes along a gradient of distance from waterbodies. All machine learning models achieved high F-scores (F1) and overall accuracy (OA) scores in general savannah PFTs classification, with RF (F1 = 95.73±0.004%, OA = 94.16±0.004%), SVM (F1 = 95.64±0.002%, OA = 94.02±0.002%) and MLP (F1 = 95.71±0.003%, OA = 94.27±0.003%) forming a cluster of the better performing models and marginally outperforming CART (F1 = 92.74±0.006%, OA = 90.93±0.003%). Grazing lawn detection accuracy followed a similar trend within the Lower Sabie landscape, with RF, SVM, MLP and CART achieving F-scores of 0.89, 0.93, 0.94 and 0.81, respectively. Transferring models to the Satara landscape however resulted in relatively lower but high grazing lawn detection accuracies across models (RF = 0.87, SVM = 0.88, MLP = 0.85 and CART = 0.75). Results from spatial pattern analysis revealed a relatively higher proportion of grazing lawn cover under semiarid savannah conditions (Satara) compared to the mesic savannah landscape (Lower Sabie). Additionally, the results show strong negative correlation between grazing lawn spatial structure (fractional cover, patch size and connectivity) and distance from waterbodies, with larger and contiguous grazing lawn patches occurring in close proximity to waterbodies in both landscapes. The proposed machine learning approach provides a novel and robust workflow for accurate and consistent landscape-scale monitoring of grazing lawns, while our findings and research outputs provide timely information critical for understanding habitat heterogeneity in southern African savannah