Towards Industry 4.0: A Roadmap for the South African Heavy Industry Sector

Prinsloo, J. G.; Vosloo, J. C.; Mathews, E.H.

doi:10.7166/30-3-2237

Cited by 13 publications

(12 citation statements)

References 41 publications

(58 reference statements)

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“…Industry 4.0 demands further employee skills and competencies, such as ICT know-how, interdisciplinary competencies, and special personality traits. Besides industry 4.0 digital basis, knowledge and skills in ICT are compulsory as they paved way for this digital transformation [203][204][205][206][207]. ICT has a profusion of new technologies including cloud computing, Big data, and Internet of things (IoT) that are heightening automation and accelerating digitalization, networking, and connectivity; resulting in increased levels of industrial intelligence [208,209].…”

Section: Industry 40 Potentiality In Eacmentioning

confidence: 99%

Sustainable and Dynamic Competitiveness towards Technological Leadership of Industry 4.0: Implications for East African Community

Bongomin

Nganyi

Abswaidi

et al. 2020

Journal of Engineering

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The war to technology and economic powers has been the driver for industrialization in most developed countries. The first industrial revolution (industry 1.0) earned millions for textile mill owners, while the second industrial revolution (industry 2.0) opened the way for tycoons and captains of industry such as Henry Ford, John D. Rockefeller, and J.P. Morgan. The third industrial revolution (industry 3.0) engendered technology giants such as Apple and Microsoft and made magnates of men such as Bill Gates and Steve Jobs. Now, the race for the fourth industrial revolution (industry 4.0) is on and there is no option, and every country whether developed or developing must participate. Many countries have positively responded to industry 4.0 by developing strategic initiatives to strengthen industry 4.0 implementation. Unlocking the country’s potential to industry 4.0 has been of interest to researchers in the recent past. However, the extent to which industry 4.0 initiatives are being launched globally has never been divulged. Therefore, the present study aimed at exploring industry 4.0 initiatives through a comprehensive electronic survey of the literature to estimate the extent of their launching in different regions. Inferences were drawn from industry 4.0 initiatives in developed nations to be used as the recommendations for the East African Community. Results of the survey revealed that 117 industry 4.0 initiatives have been launched in 56 countries worldwide consisting of five regions: Europe (37%), North America (28%), Asia and Oceania (17%), Latin America and the Caribbean (10%), and Middle East and Africa (8%). The worldwide percentage was estimated as 25%. This revealed that there is a big gap existing between countries in the race for industry 4.0.

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Section: Industry 40 Potentiality In Eacmentioning

confidence: 99%

Sustainable and Dynamic Competitiveness towards Technological Leadership of Industry 4.0: Implications for East African Community

Bongomin

Nganyi

Abswaidi

et al. 2020

Journal of Engineering

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“…Industrial information systems will fall short of their objectives if no input data is received from industrial sites. The existing network infrastructure used for real-time data transmission is therefore considered to be a critical component in the overall information system architecture, as well as in other IoT-related systems [39][40].…”

Section: State Of the Artmentioning

confidence: 99%

The Value of a Telemetry Monitoring System in Sustaining the Operational Performance of Industrial Information Systems

Prinsloo

Mathews

Vosloo

2020

SAJIE

Self Cite

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Revolutionary advances in the information and communication technology field pave the way for automation and data exchange in the heavy industry sector. Currently, Industry 4.0 solutions are more readily accepted in factories and in more advanced economies, where good infrastructure is in place to facilitate these solutions. In the context of these heavy industries, wide-area wireless telemetry networks play an important role in facilitating accessibility and allowing for real-time monitoring and data transmission over wide geographical distances. This paper emphasises the value of a new wireless telemetry monitoring and diagnostic system that allows for the development of a detailed troubleshooting methodology when communication disruptions occur. The system was implemented on an existing telemetry system that is used by an energy services company to maintain the operational status of various remote information systems. The network is also used to facilitate various demand-side management projects throughout the Republic of South Africa. The results obtained show that the proposed system can enhance the robustness of existing Industrial Internet of Things infrastructure, and this promotes the adoption of Industry 4.0 concepts in the heavy industry sector.

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“…The characteristic system can then be used as a predictive model in a dynamic control philosophy (Hasan et al, 2013 ; Chu et al, 2014 ). This has not been done in the deep-level mining industry due to the lack of sensor infrastructure and the large and complex integrated nature of the systems in these mines (Prinsloo et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…The lack of sensor infrastructure in mining prevents the implementation of semi-empirical and mathematical-based control and predictive philosophies (Nell et al, 2019 ; Prinsloo et al, 2019 ). The lack of sensors result in data points that lack features (a measureable piece of data i.e., a column in a dataset).…”

Section: Introductionmentioning

confidence: 99%

Integration of machine learning with complex industrial mining systems for reduced energy consumption

Harmse

Laar

Pelser

et al. 2022

Front. Artif. Intell.

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The deep-level mining industry is experiencing narrowing profit margins due to increasing operating costs and decreasing production. The industry is known for its lack of dynamic control across complex integrated systems running deep underground, making IoT technologies difficult to implement. An important integrated system in a typical underground mine is the refrigeration-ventilation system. In practice, the two systems are still controlled independently, often due to a lack of continuous measurements. However, their integrated effects ultimately affect energy usage and production. This study develops and compares various machine learning prediction techniques to predict the integrated behavior of a key component operating on the boundary of the refrigeration-ventilation system, while also addressing the lack of continuous measurements. The component lacks sensors and the developed industrial machine learning models negate the effect thereof using integrated control. The predictive models are compared based on accuracy, prediction time, as well as the amount of data required to obtain the required level of accuracy. The “Support Vector Machines” method achieved the lowest average error (1.97%), but the “Artificial Neural Network” method is more robust (with a maximum percentage error of 12.90%). A potential energy saving of 215 kW or 2.9% of the ventilation and refrigeration system, equivalent to R1.33-million per annum ($82 9001) is achievable using the “Support Vector Machines” method.

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Towards Industry 4.0: A Roadmap for the South African Heavy Industry Sector

Cited by 13 publications

References 41 publications

Sustainable and Dynamic Competitiveness towards Technological Leadership of Industry 4.0: Implications for East African Community

Sustainable and Dynamic Competitiveness towards Technological Leadership of Industry 4.0: Implications for East African Community

The Value of a Telemetry Monitoring System in Sustaining the Operational Performance of Industrial Information Systems

Integration of machine learning with complex industrial mining systems for reduced energy consumption

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