Digging Deeper than Platinum: South Africa's Participation in an Emergent Fuel Cell Industry

South’s Africa’s position as global platinum supplier provides a unique opportunity for an emergent fuel cell industry. The innovative technology’s reliance on platinum has sparked interest in the mining sector, promoting the clean energy-producing devices in their own operations. This research focuses upon contemporary structures of racial oppression within the industry, to analyse how these dynamics influence the development and implementation of innovative technology. It also challenges the sustainability discourse associated with fuel cell technology in South Africa. The study follows a qualitative research approach, incorporating a political ecology focus to highlight the politicized nature of these interactions. The methodology incorporates a literature review, key informant interviews, fieldwork observations and document analysis. Findings indicate that the implementation of fuel cell technology in South Africa’s platinum mines will disproportionately burden historically disadvantaged South Africans, with the lack in technical knowledge-base considered a major challenge. Additionally, it was found that sustainability claims surrounding fuel cell technology are largely based on environmental characteristics. This has resulted in an oversimplification and a depoliticised account of the impacts of the technology. This study looked critically at the convergence of history and innovation, placing emphasis on context, power relations and knowledge to provide a more holistic account of the research problem. Opportunities exist for making a meaningful and viable contribution towards development and sustainability by means of investing in a South African fuel cell industry. The challenge will be in deliberately seeking pathways which address the more complex components of sustainability, benefitting all stakeholders and paying particular attention to the historical, political and social contexts from which the technology emerges. It is this particular context which allows for a questioning and perhaps even a re-evaluation of the sustainability narratives broadly applied to fuel cell technology.

Admittance Control for Robotic Loading: Design and Experiments with a 1-Tonne Loader and a 14-Tonne LHD

This paper describes the design, tuning, and extensive field testing of an admittance-based Autonomous Loading Controller (ALC) for robotic excavation. Several iterations of the ALC were tuned and tested in fragmented rock piles—similar to those found in operating mines—by using both a robotic 1-tonne capacity Kubota R520S diesel-hydraulic surface loader and a 14-tonne capacity Atlas Copco ST14 underground load-haul-dump (LHD) machine. On the R520S loader, the ALC increased payload by 18 % with greater consistency, although with more energy expended and longer dig times when compared with digging at maximum actuator velocity. On the ST14 LHD, the ALC took 61 % less time to load 39 % more payload when compared to a single manual operator. The manual operator made 28 dig attempts by using three different digging strategies, and had one failed dig. The tuned ALC made 26 dig attempts at 10 and 11 MN target force levels. All 10 11 MN digs succeeded while 6 of the 16 10 MN digs failed. The results presented in this paper suggest that the admittance-based ALC is more productive and consistent than manual operators, but that care should be taken when detecting entry into the muck pile

Automatic Identification of Large Fragments in a Pile of Broken Rock Using a Time-of-Flight Camera

This paper presents a solution to part of the problem of making robotic or semi-robotic digging equipment less dependant on human supervision. A method is described for identifying rocks of a certain size that may affect digging efficiency or require special handling. The process involves three main steps. First, by using range and intensity data from a time-of-flight (TOF) camera, a feature descriptor is used to rank points and separate regions surrounding high scoring points. This allows a wide range of rocks to be recognized because features can represent a whole or just part of a rock. Second, these points are filtered to extract only points thought to belong to the large object. Finally, a check is carried out to verify that the resultant point cloud actually represents a rock. Results are presented from field testing on piles of fragmented rock. Note to Practitioners—This paper presents an algorithm to identify large boulders in a pile of broken rock as a step towards an autonomous mining dig planner. In mining, piles of broken rock can contain large fragments that may need to be specially handled. To assess rock piles for excavation, we make use of a TOF camera that does not rely on external lighting to generate a point cloud of the rock pile. We then segment large boulders from its surface by using a novel feature descriptor and distinguish between real and false boulder candidates. Preliminary field experiments show promising results with the algorithm performing nearly as well as human test subjects.