Abstract
Currently, Kenya supplies its energy demand predominantly through hydroelectric power, which fluctuates due to poor and unpredictable rainfall in particular years. Geothermal energy is proposed as a clean and reliable energy source in meeting Kenya’s increasing energy demand. During geothermal drilling operations, disruptions due to tool wear and breakages increases the cost of operation significantly. Some of these causes can be mitigated by real-time monitoring of the tool head during operations. This paper presents the design and implementation of a digital twin model of a drilling tool head, represented as a section of a mechatronic assembly system. The system was modelled in Siemens NX and programmed via the TIA portal using S7 1200 PLC. The digital model was programmed to exactly match the operations of the physical system using OPC (open platform communications) standards. These operations were verified through the motion study by simultaneous running of the assembly system and digital twin model. The study results substantiate that a digital twin model of a geothermal drilling operation can closely mimic the physical operation.
Highlights
The fourth industrial revolution referred to as industrie 4.0 is the current trend in automation technology [1]
This research aims to simulate a geothermal drilling operation where the virtual model is linked to its physical counterpart and real-time data obtained for drilling optimization
A real-time monitoring and control method through the use of a digital twin was proposed for use in geothermal drilling
Summary
The fourth industrial revolution referred to as industrie 4.0 is the current trend in automation technology [1]. It encompasses digitalization, networking technologies, internet of things, cloud computing and cyber-physical systems. It involves the creation of a virtual representation of an existing physical system in cyberspace [2]. As discussed by Fei et al [3], such a representation acts as an effective test-bed for implementing data fusion, machine learning and artificial intelligence to achieve real time control, monitoring and optimization. Digital twins are gaining popularity in research and industry as they can accurately represent the status, position or working conditions of their physical counterparts
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