When metals operations best practices meet the right IIoT architecture

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By Tarun Mathur  LinkedIn
ABB Metals Digital Lead
About the author
Tarun Mathur graduated from the Indian Institute of Technology, Mumbai and holds a Master’s degree in mathematical modeling and process control.

He has held several positions in R&D focusing on the development of advanced model-based solutions for process industries.

Tarun is currently focused on projects applying new digital technologies to optimize metals plant performance, processes and quality.

Following on from my description of Industry 4.0, this article evaluates the close link between operational practices and integration with information technology, digitalization. No move to digitalization is complete without on-ground processes being adapted to integrate seamlessly with it. The IIoT architecture must be implemented in its entirety for manufacturing enterprises to realize benefits from the move to digitalized environments. 

The close integration of operational technology with information technology

I don’t believe that a smart metals plant in the modern era can exist in isolation - in Industry 4.0, operations are closely integrated with IT, enabling a smart factory where interconnected devices and systems work together without boundaries. The need for this convergence has led to open systems where communication protocols are no longer a barrier, even when data formats vary between automation platforms. IIoT frameworks are increasingly supporting interoperability and enabling big data analytics for diverse uses. 

Smart IIoT architecture is the effective and seamless integration of sensors, gateways, smart devices and data centres
Best practice architecture blueprint

IIoT architecture and deployment

To simplify, there are four main parts to the IIoT architecture:  

  • Sensors to collect data at the plant level, with choice of sensor driven by specific data collection requirements and plant operating parameters. Such sensors can be placed across the plant at every stage, including stockyards, primary metals areas, hot rolling equipment as examples. Where multiple data sources result in the requirement of several sensors, I suggest data aggregation being a key area to focus on. Once collected in analog form, data is digitized for further use  

  • The gateway, which transports data by linking sensors to data processing tools and systems, with additional requirement for data management and malware protection 

  • Smart devices, which are important at plants to process data and reduce data clogging, thereby optimizing efficiency. With the need for big data comes the risk of pressure on networks; devices help reduce that pressure. While sensors are responsible for communicating input data, devices have the additional ability to accept instructions and respond to them 

  • Data centers, either localized or on cloud, are where data is aggregated, analysed and used for diverse applications – monitoring & predictive insights, resolution tracking, building algorithms and enhancing machine learning capabilities 

A path towards autonomous decentralized plant

Smart deployment of IIoT

Get started with IIoT implementation

Knowledge of correct data points, data structuring and analysis, and designing algorithms that drive artificial intelligence can only be done through a deep understanding of the metals industry

Sneak peek

The one key outcome of digitalization is end-to-end integration and interoperability. My next blog will cover the approach to and power of this approach...

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Learn more

Introduction to Industry 4.0 in metals
Digital transformation in metals and steel industries
The future of metals and steel industry - as seen by the generation Z
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