Sunday 8th Dec, 2019

Increasing plant efficiency with thyssenkrupp’s digital twin technology

Matthias Göing and Martin Krex from thyssenkrupp Industrial Solutions speak to ABHR about three megatrends emerging in the mining industry, and how its digital twin technology is helping achieve them.

Matthias Göing and Martin Krex from thyssenkrupp Industrial Solutions speak to ABHR about three megatrends emerging in the mining industry, and how its digital twin technology is helping achieve them.

As a global mining corporation, thyssenkrupp Industrial Solutions has kept its finger on the pulse of the industry, identifying three megatrends occurring across the world.

Matthias Göing, Head of Product Management and Martin Krex, Global Product Manager Automation and Digitisation at thyssenkrupp Industrial Solutions explain the trends are sustainability, increasing plant efficiency and digitisation.

“Specifically, digitisation is playing an ever-important role as it touches on the other two megatrends,” Krex says.

“The use of digital solutions is a key lever in increasing process efficiencies as well as ensuring sustainability for our customers. thyssenkrupp Business Unit Mining is presently busy with transferring its knowledge of machinery into digital expertise.”

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The company offers a range of digital services, which use technology such as drones to collect data from its customer’s plants, machinery and equipment for analysis. By looking at the gathered information, thyssenkrupp is then able to make informed decisions that can evaluate, predict, automate and optimise mining equipment.

One digital solution the company employs is the creation of a digital twin. These are virtual representations of a physical object or system, which can use data to simulate outcomes and provide a better understanding about the machine as a whole.

Göing says that thyssenkrupp’s digital twin process goes even further than just a dynamic virtual representation.

“The digital twin consists of two parts: one part is the 3D visualisation of the machine or plant to get a better grasp on what is currently installed, understanding dimensions and relation between plant portions,” he says.

“The second part is a dynamic mathematical model, which reflects the machine behaviour under specific input conditions. Both parts are interconnected to create a useful link between design and operations of a machine along the whole product lifecycle.”

“The design of the machine is continually evaluated and updated to find the right operational concept, required architecture and optimised detail design.”

One piece of machinery that can benefit from the use of a digital twin is a conveyor. thyssenkrupp can create a digital twin for any conveyor, which includes a visualisation of the mechanical components and their operational behaviour using mathematical modelling.

This mathematical model can be fed live or recorded data to improve the accuracy of its predictions. It can then be used to understand critical start/stop processes of the conveyor and visualise complex mechanical behaviours such as belt tension of belt velocity at any point on the conveyor.

Additionally, a digital twin can simulate variable belt speeds to find the best operating point for existing components and identify unforeseen power oscillations caused by drive slip.

While in operation, a digital twin can help to create a concrete understanding of how each change in a machine’s design can lead to a change in the operating characteristics of the equipment by analysing operational and maintenance tasks.

Göing says thyssenkrupp can also provide its customers with a comprehensive view of the components and their behaviour, which could result in less downtime.

“On the basis of anomaly detection (the comparison between real actual states and ideal target states), thyssenkrupp can even generate the planning reliability of the machine’s maintenance intervals for the customer,” he says.

“Future states can be simulated on the basis of current states, which leads to a reduction of unplanned downtimes as well as plant failures and thus to increase production output. Furthermore, the prediction of anomalies reduces required maintenance and optimised spare and wear parts inventory.

“The digital twin offers us new possibilities. We are able to simulate the whole system during the project feasibility phase to optimise engineering and evaluate new plant parameters before a change on the live system is even executed.”