FEATURE AI & Machine Learningļ¹
AI paves the way to net zero
Aaron Yeardley, Tunley Engineering’s Carbon- Reduction Engineer, discusses the uses of AI in improving manufacturing processes but also how it can reduce carbon footprint
I
ndustry 4.0 is a product of the digital era as automation and data exchange in manufacturing technologies shift central industrial control systems to a smart setup that bridges the physical and digital world, enabled by the Internet of Things. The main factors driving this so- called “Fourth Industrial Revolution” are advances in artifi cial intelligence (AI) and machine learning. The complex algorithms involved in AI use data collected from cyber-physical systems, resulting in smart manufacturing, with focus on enabling value creation and real-time optimisation. The impact that Industry 4.0 will have on manufacturing will be astronomical, since operations can be automatically optimised to produce increased profi t margins. However, AI and smart manufacturing can also benefi t the environment. The technologies used to optimise profi ts can also be used to produce insights into a company’s carbon footprint and accelerate its sustainability. Some of these methods are available to help companies reduce their greenhouse gas (GHG) emissions now. Other methods have the potential to reduce global GHG emissions in the future.
Scope 3 identification Scope 3 emissions are generated by upstream and downstream activities from a company’s supply chain; in eff ect, the scope covers all of a company’s GHG emission sources except those directly created by the company and from using electricity. It comes as no surprise that on average Scope 3 emissions are 5.5 times greater than the combined amount from Scope 1 and Scope 2. Therefore, companies should ensure all three scopes are covered in their GHG emissions baseline. However, in comparison to Scope 1 and Scope 2 emissions, Scope 3 emissions are diffi cult to measure and calculate, mainly because of lack of transparency in supply chains, lack of connections with suppliers, and complex industrial standards that
12 October 2022 | Automation
provide misleading information. The major issues concerning Scope 3 emissions are as follows:
• Reliability of data – including the variability in data quality between supply chains and the uncertainty in carbon emission factors used to calculate GHG emissions. • Double counting – emissions can easily be double counted as supply chains of companies become interconnected. For example, transportation of a product for one company is also transportation of material for another.
• Fair attribution of total supply chains – given the total GHG emissions for a supply chain have been successfully counted, what is the fair responsibility of each actor in the supply chain?
AI-based tools can help establish baseline Scope 3 emissions for companies since they are used to model an entire supply chain. The tools can quickly and effi ciently sort through large volumes of data collected from sensors. If a company uses enough sensors across all its operations, it can identify sources of emissions and even detect methane plumes.
Optimisation and maintenance A digital twin is an AI model that works as a digital representation of a physical piece of equipment or an entire system. Digital twins can help the industry optimise energy management by using the AI surrogate models to better monitor and distribute energy resources and provide forecasts for better preparation. Digital twins can be used as virtual replicas of building systems, industrial processes, vehicles, and many others. The virtual environment enables more testing and iterations so that
everything can be optimised to its best performance. This means digital twins can be used to optimise building management making smart strategies that are based on carbon reduction. A digital twin will optimise many sources of data and bring them onto a dashboard so that users can visualise it in real time. Predictive maintenance of machines and equipment used in industry is now becoming common practice because it saves companies scheduled maintenance costs, or those in fi xing broken equipment. The AI-based tool uses machine learning to map out historical sensor data to maintenance records. Once a machine-learning algorithm is trained using the historical data, it can successfully predict when maintenance is required based on live sensor readings in a plant. Predictive maintenance accurately models the wear and tear of machinery in use. Hence, combined with other AI- based methods it can create an optimal maintenance workfl ow for industrial processes. In addition, carbon savings can be made via controlled deployment of spare parts and less travel for technicians to go to a site, but also save on electricity usage, effi ciency (by preventing declining performance) and human labour.
CONTACT:
Tunley Engineering
www.tunley-engineering.com
automationmagazine.co.uk
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