Estimating the continuous risk of accidents occurring in the South African mining industry

Van den Honert, Andrew (2014-12)

Thesis (MEng)--Stellenbosch University, 2014.

Thesis

ENGLISH ABSTRACT: Statistics from mining accidents expose that the potential for injury or death to employees from occupational accidents is relatively high. This study attempts to contribute to the on-going efforts to improve occupational safety in the mining industry by creating a model capable of predicting the continuous risk of occupational accidents occurring. Model inputs include the time of day, time into shift, temperatures, humidity, rainfall and production rate. The approach includes using an Artificial Neural Network (ANN) to identify patterns between the input attributes and to predict the continuous risk of accidents occurring. As a predecessor to the development of the model, a comprehensive literature study was conducted. The objectives of the study were to understand occupational safety, explore various forecasting techniques and identify contributing factors that influence the occurrence of accidents and in so doing recognise any gaps in the current knowledge. Another objective was to quantify the contributing factors identified, as well as detect the sensitivity amongst these factors and in so doing deliver a groundwork for the present model. After the literature was studied, the model design and construction was performed as well as the model training and validation. The training and validation took the form of a case study with data from a platinum mine near Rustenburg in South Africa. The data was split into three sections, namely, underground, engineering and other. Then the model was trained and validated separately for the three sections on a yearly basis. This resulted in meaningful correlation between the predicted continuous risk and actual accidents as well as the majority of the actual accidents only occurring while the continuous risk was estimated to be above 80%. However, the underground section has so many accidents, that the risk is permanently very high. Yet, the engineering and other sections produced results useful for managerial decisions.

AFRIKAANSE OPSOMMING: Mynbou ongeluk statistieke dui aan dat die potensiaal vir besering of dood as gevolg van beroepsongelukke relatief hoog is. Die studie poog om by te dra tot die voortdurende verbetering van beroepsveiligheid in die mynbedryf deur middel van ’n model wat die risiko van beroepsongelukke voorspel. Die model vereis die tyd, tyd verstreke in die skof, temperatuur, humiditeit, reënval en produksie tydens die ongeluk as inset. Die benadering tot hierdie model maak gebruik van ’n Kunsmatige Neurale Netwerk (KNN) om patrone tussen die insette te erken en om die risiko van ’n voorval te beraam. As ’n voorloper tot die model ontwikkeling, is ’n omvattende literatuurstudie onderneem. Die doelwitte van die literatuur studie was om beroepsveiligheid beter te verstaan, verskeie voorspellings tegnieke te ondersoek en kennis van bydraende faktore wat lei tot voorvalle te ondersoek. Nog ’n doelwit sluit die kwantifisering in van geidentifiseerde bydraende faktore, asook die opsporing van die sensitiwiteit tussen hierdie faktore en hierdeur ’n fondasie vir die voorgestelde model te skep. Na afloop van die literatuurstudie is die model ontwikkel, opgelei en gevalideer. Die opleiding en validasie is deur middel van ’n gevallestudie in ’n platinummyn naby Rustenburg in Suid Afrika gedoen. Die data is verdeel in drie afdelings, d.i. ondergronds, ingenieurswese en ander. Die model is vir elke afdeling apart opgelei en gevalideer op ’n jaarlikse basis. Hierdie het gelei tot ’n betekenisvolle korrelasie tussen die voorspelde risiko en die werklike ongelukke met die meerderheid van die werklike ongevalle wat voorgekom het terwyl die risiko 80% oorskry het. In die ondergrondse afdeling is so baie voorvalle waarneem dat die risiko permanent hoog is. Die ander afdelings het wel resultate verskaf wat sinvol gebruik kan word in bestuursbesluite.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/96072
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