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Open AccessFeature PaperArticle

Sociotechnical Undergraduate Education for the Future of Natural Resource Production

by

Jessica Smith

,

Carrie McClelland

and

Oscar Jaime Restrepo

Mining 2023, 3(2), 387-398; https://doi.org/10.3390/mining3020023 - 18 Jun 2023

Viewed by 596

Abstract

The greatest challenges for contemporary and future natural resource production are sociotechnical by nature, from public perceptions of mining to responsible mineral supply chains. The term sociotechnical signals that engineered systems have inherent social dimensions that require careful analysis. Sociotechnical thinking is a [...] Read more.

The greatest challenges for contemporary and future natural resource production are sociotechnical by nature, from public perceptions of mining to responsible mineral supply chains. The term sociotechnical signals that engineered systems have inherent social dimensions that require careful analysis. Sociotechnical thinking is a prerequisite for understanding and promoting social justice and sustainability through one’s professional practices. This article investigates whether and how two different projects enhanced sociotechnical learning in mining and petroleum engineering students. Assessment surveys suggest that most students ended the projects with greater appreciation for sociotechnical perspectives on the interconnection of engineering and corporate social responsibility (CSR). This suggests that undergraduate engineering education can be a generative place to prepare future professionals to see how engineering can promote social and environmental wellbeing. Comparing the different groups of students points to the power of authentic learning experiences with industry engineers and interdisciplinary teaching by faculty. Full article

(This article belongs to the Special Issue Envisioning the Future of Mining)

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Open AccessArticle

Inclusive Urban Mining: An Opportunity for Engineering Education

by

Sofia L. Schlezak

and

Jaime E. Styer

Mining 2023, 3(2), 284-303; https://doi.org/10.3390/mining3020018 - 18 May 2023

Viewed by 1127

Abstract

With the understanding that the mining industry is an important and necessary part of the production chain, we argue that the future of mining must be sustainable and responsible when responding to the increasing material demands of the current and next generations. In [...] Read more.

With the understanding that the mining industry is an important and necessary part of the production chain, we argue that the future of mining must be sustainable and responsible when responding to the increasing material demands of the current and next generations. In this paper, we illustrate how concepts, such as inclusiveness and the circular economy, can come together in new forms of mining—what we call inclusive urban mining—that could be beneficial for not only the mining industry, but for the environmental and social justice efforts as well. Based on case studies in the construction and demolition waste and WEEE (or e-waste) sectors in Colombia and Argentina, we demonstrate that inclusive urban mining could present an opportunity to benefit society across multiple echelons, including empowering vulnerable communities and decreasing environmental degradation associated with extractive mining and improper waste management. Then, recognizing that most engineering curricula in this field do not include urban mining, especially from a community-based perspective, we show examples of the integration of this form of mining in engineering education in first-, third- and fourth-year design courses. We conclude by providing recommendations on how to make inclusive urban mining visible and relevant to engineering education. Full article

(This article belongs to the Special Issue Envisioning the Future of Mining)

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Open AccessArticle

Environmental and Work Factors That Drive Fatigue of Individual Haul Truck Drivers

by

Elaheh Talebi

,

W. Pratt Rogers

and

Frank A. Drews

Mining 2022, 2(3), 542-565; https://doi.org/10.3390/mining2030029 - 26 Aug 2022

Cited by 1 | Viewed by 1402

Abstract

Many factors influence the fatigue state of human beings, and fatigue has a significant adverse effect on the health and safety of the haulage operators in the mine. Among various fatigue monitoring systems in mine operations, currently, the Percentage of Eye Closure (PERCLOS) [...] Read more.

Many factors influence the fatigue state of human beings, and fatigue has a significant adverse effect on the health and safety of the haulage operators in the mine. Among various fatigue monitoring systems in mine operations, currently, the Percentage of Eye Closure (PERCLOS) is common. However, work and other environmental factors influence the fatigue state of haul truck drivers; PERCLOS systems do not consider these factors in their modeling of fatigue. Therefore, modeling work and environmental factors’ impact on individual operations fatigue state could yield interesting insights into managing fatigue. This study provides an approach of using operational data sets to find the leading indicators of the operators’ fatigue. A machine learning algorithm is used to model the fatigue of the individual. eXtreme Gradient Boosting (XGBoost) algorithm is chosen for this model because of its efficiency, accuracy, and feasibility, which integrates multiple tree models and has stronger interpretability. A significant number of negative and positive samples are created from the available data to increase the number of datasets. Then, the results are compared with other existing models. A selected algorithm, along with a big data set was able to create a comprehensive model. The model was able to find the importance of the individual factors along with work and environmental factors among operational data sets. Full article

(This article belongs to the Special Issue Envisioning the Future of Mining)

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Open AccessArticle

Development of a Smart Computational Tool for the Evaluation of Co- and By-Products in Mining Projects Using Chovdar Gold Ore Deposit in Azerbaijan as a Case Study

by

,

,

,

and

Mining 2022, 2(3), 487-510; https://doi.org/10.3390/mining2030026 - 30 Jul 2022

Cited by 4 | Viewed by 2342

Abstract

Despite their significance in numerous applications, many critical minerals and metals are still considered minor. Since most of them are not found alone in mineral deposits, their co- or by-production depends on the production of base metals and other major commodities. In many [...] Read more.

Despite their significance in numerous applications, many critical minerals and metals are still considered minor. Since most of them are not found alone in mineral deposits, their co- or by-production depends on the production of base metals and other major commodities. In many cases, the concentration of the minor metals is low enough not to be considered part of the production. Hence, their supply is not always secured, their availability decreases, and their criticality increases. Many researchers have addressed this issue, but no one has set actual impact factors other than economic ones that should determine the production of these minor commodities. This study identified several parameters, the number and diversity of which gave birth to developing a computational tool using a multi-criteria-decision analysis model based on the Analytical Hierarchical Process (AHP) and Python. This unprecedented methodology was applied to evaluate the production status of different commodities in a polymetallic deposit located in Chovdar, Azerbaijan. The evaluation outcomes indicated in quantifiable terms the production potentials for several commodities in the deposit and justified the great perspectives of this tool to evaluate all kinds of polymetallic deposits concerning the co- and by-production of several minor critical raw materials. Full article

(This article belongs to the Special Issue Envisioning the Future of Mining)

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Open AccessArticle

Rock Fragmentation Prediction Using an Artificial Neural Network and Support Vector Regression Hybrid Approach

by

Richard Amoako

,

Ankit Jha

and

Shuo Zhong

Mining 2022, 2(2), 233-247; https://doi.org/10.3390/mining2020013 - 24 Apr 2022

Cited by 11 | Viewed by 3065

Abstract

While empirical rock fragmentation models are easy to parameterize for blast design, they are usually prone to errors, resulting in less accurate fragment size prediction. Among other shortfalls, these models may be unable to accurately account for the nonlinear relationship that exists between [...] Read more.

While empirical rock fragmentation models are easy to parameterize for blast design, they are usually prone to errors, resulting in less accurate fragment size prediction. Among other shortfalls, these models may be unable to accurately account for the nonlinear relationship that exists between fragmentation input and output parameters. Machine learning (ML) algorithms are potentially able to better account for the nonlinear relationship. To this end, we assess the potential of the multilayered artificial neural network (ANN) and support vector regression (SVR) ML techniques in rock fragmentation prediction. Using geometric, explosives, and rock parameters, we build ANN and SVR models to predict mean rock fragment size. Both models yield satisfactory results and show higher performance when compared with the conventional Kuznetsov model. We further demonstrate an automated means of analyzing a varied number of hidden layers for an ANN using Bayesian optimization in the Keras Python library. Full article

(This article belongs to the Special Issue Envisioning the Future of Mining)

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Open AccessArticle

A High-Fidelity Modelling Method for Mine Haul Truck Dumping Process

by

Aaron Young

and

William Pratt Rogers

Mining 2022, 2(1), 86-102; https://doi.org/10.3390/mining2010006 - 11 Feb 2022

Cited by 1 | Viewed by 2434

Abstract

Dumping is one of the main unit operations of mining. Notwithstanding a long history of using large rear dump trucks in mining, little knowledge exists on the cascading behavior of the run-of-mine material during and after dumping. In order to better investigate this [...] Read more.

Dumping is one of the main unit operations of mining. Notwithstanding a long history of using large rear dump trucks in mining, little knowledge exists on the cascading behavior of the run-of-mine material during and after dumping. In order to better investigate this behavior, a method for generating high fidelity models (HFMs) of dump profiles was devised and investigated. This method involved using unmanned aerial vehicles with mounted cameras to generate photogrammetric models of dumps. Twenty-eight dump profiles were created from twenty-three drone flights. Their characteristics were presented and summarized. Four types of dump profiles were observed to exist. Factors that influence the determination of these profiles include the location of the truck relative to the dump crest, the movement of the underlying dump material during the dumping process and the differences in the dump profile prior to dumping. The HFMs created in this study could possibly be used for calibrating computer simulations of dumps to better match reality. Full article

(This article belongs to the Special Issue Envisioning the Future of Mining)

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Open AccessArticle

Safe Mining Assessment of Artisanal Barite Mining Activities in Nigeria

by

David Oluwasegun Afolayan

,

Azikiwe Peter Onwualu

,

Carrick McAfee Eggleston

,

Adelana Rasak Adetunji

,

Mingjiang Tao

and

Richard Kwasi Amankwah

Mining 2021, 1(2), 224-240; https://doi.org/10.3390/mining1020015 - 18 Sep 2021

Cited by 3 | Viewed by 5414

Abstract

Barite, used in mud formulation, is mined in several places to support the industry. However, there is insufficient literature on the downside of mining and associated hazards, especially in the artisanal barite mining sector. This paper contains three parts. The initial section reviews [...] Read more.

Barite, used in mud formulation, is mined in several places to support the industry. However, there is insufficient literature on the downside of mining and associated hazards, especially in the artisanal barite mining sector. This paper contains three parts. The initial section reviews major causes of mining accidents and health hazards in Nigeria. The second section examines existing but weak institutional frameworks and policies for artisanal and small-scale mining (ASM) in Nigeria. In the third part, data from questionnaires and heavy metal contamination assessment are compared with health and environmental standards to identify and characterize hazards. It was observed that 54% had health challenges traceable to illicit drugs, and 54% were ignorant about the use of safety kits. The UV-Vis, AAS, and ICP-MS analyses confirmed lead, barium, zinc, copper, and iron in the water samples. Index of geoaccumulation (Igeo) and contamination factor (CF) show that water samples are moderate to highly polluted by Pb²⁺, Ba²⁺, and highly contaminated. The chronic daily intake assessment and health quotient analysis revealed that the accumulation of lead and barium is possible and can initiate chronic diseases in humans over a long time. Certain safe mining protocols and controls are recommended. Full article

(This article belongs to the Special Issue Envisioning the Future of Mining)

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Open AccessArticle

Optimising Productivity and Safety of the Open Pit Loading and Haulage System with a Surge Loader

by

Ignacio Andrés Osses Aguayo

,

Micah Nehring

and

G. M. Wali Ullah

Mining 2021, 1(2), 167-179; https://doi.org/10.3390/mining1020011 - 02 Aug 2021

Cited by 5 | Viewed by 7375

Abstract

The open pit mining load and haul system has been a mainstay of the mining industry for many years. While machines have increased in size and scale and automation has become an important development, there have been few innovations to the actual load [...] Read more.

The open pit mining load and haul system has been a mainstay of the mining industry for many years. While machines have increased in size and scale and automation has become an important development, there have been few innovations to the actual load and haul process itself in recent times. This research highlights some of the potential productivity and safety benefits that the incorporation of a surge loader may bring to the load and haul system through an analysis of the system, discussion of component characteristics, and mine planning aspects. The incorporation of the surge loader into open pit loading and haulage operations also enables improved safety. This is a result of a reduction in shovel–truck interactions and the reduced likelihood of truck overfilling and uneven loading. This paper details the number of mine worker deaths that a surge loader may have prevented within the Peruvian and Chilean mining industries. Full article

(This article belongs to the Special Issue Envisioning the Future of Mining)

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Review

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Open AccessReview

Electrification Alternatives for Open Pit Mine Haulage

by

,

,

and

Mining 2023, 3(1), 1-25; https://doi.org/10.3390/mining3010001 - 01 Jan 2023

Cited by 2 | Viewed by 3344

Abstract

Truck-Shovel (TS) systems are the most common mining system currently used in large surface mines. They offer high productivity combined with the flexibility to be rapidly relocated and to adjust load/haul capacity and capital expenditure according to market conditions. As the world moves [...] Read more.

Truck-Shovel (TS) systems are the most common mining system currently used in large surface mines. They offer high productivity combined with the flexibility to be rapidly relocated and to adjust load/haul capacity and capital expenditure according to market conditions. As the world moves to decarbonise as part of the transition to net zero emission targets, it is relevant to examine options for decarbonising the haulage systems in large surface mines. In-Pit Crushing and Conveying (IPCC) systems offer a smaller environmental footprint regarding emissions, but they are associated with a number of limitations related to high initial capital expenditure, capacity limits, mine planning and inflexibility during mine operation. Among the emerging technological options, innovative Trolley Assist (TA) technology promises to reduce energy consumption for lower carbon footprint mining systems. TA systems have demonstrated outstanding potential for emission reduction from their application cases. Battery and energy recovery technology advancements are shaping the evolution of TAs from diesel-electric truck-based patterns toward purely electrified BT ones. Battery Trolley (BT) systems combined with autonomous battery-electric trucks and Energy Recovery Systems (ERSs) are novel and capable of achieving further significant emission cuts for surface mining operations associated with safety, energy saving and operational improvements. This article reviews and compares electrification alternatives for large surface mines, including IPCC, TA and BT systems. These emerging technologies provide opportunities for mining companies and associated industries to adopt zero-emission solutions and help transition to an intelligent electric mining future. Full article

(This article belongs to the Special Issue Envisioning the Future of Mining)

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