Guest contributor |
Industrial emissions account for 30 per cent of global greenhouse gas emissions. Digital solutions that merge operations technology (OT) with IT are driving both factory efficiency and sustainability efforts.
Microsoft and Rockwell Automation are helping organisations converge their OT data from the factory floor with IT data in the cloud to enable data-driven decision-making and move closer to sustainable factories of the future. However, OT data is often unstructured, fragmented and challenging to integrate with IT platforms, requiring scalable real-time management and analytics. The integration should also support robust security protocols.
Integrating IT and OT allows firms to contextualise energy data alongside production and business information. This empowers companies to benchmark sustainability performance, leverage analytics to identify savings opportunities, and optimise energy efficiency, emissions reduction, production output and product quality.
Microsoft and Rockwell Automation have worked on several joint customer projects to improve sustainability in manufacturing. For example, they helped a global tyre producer to use a machine learning control (MLC) model to optimise energy-intensive processes. An MLC model is a control system that uses machine learning algorithms to generate models that are more complex than a traditional linear system. The company wanted to balance production and quality variables across the entire line to see where the overall process – from raw materials to the final tyre – could be optimised, whilst also achieving energy savings. This led to a 5-10 per cent improvement in material retention, a 15-20 per cent increase in scrap recovery, and reduced energy per tyre.
OT/ IT convergence can also provide end-to-end visibility and traceability across the supply chain. Microsoft and Rockwell Automation also worked with an organisation managing wastewater removal from oil and gas extraction. They operate more than 1,100 miles (1,770 kilometres) of pipelines that move nearly two million barrels of hazardous wastewater daily. Control room operators spent at least 30 per cent of their shift viewing continuous video feed streams to monitor the numerous pipelines for potential leaks.
The company wanted to implement fast, accurate models that could detect anomalies down to the difference between rainwater and wastewater across the pipeline and trigger real-time action alerts. They deployed a solution based on high-accuracy vision artificial intelligence models using data gathered via camera-motion detection abilities. By automating the detection and notification process, control room operators now efficiently respond to more high-value leaks instead of manually viewing video feeds. This not only saved time but also mitigated serious environmental damage and regulatory fines that could harm the business’s reputation.
Learn more about Rockwell Automation’s sustainability strategy.
Austin Locke is principal of AI and data science practice, and Rodrigo Alves is manager, at Kalypso, a Rockwell Automaton business
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