AI driving zero waste

Artificial intelligence drives the way to net zero and waste in manufacturing.

Aaron Yeardley works as a Carbon Reduction Engineer for Tunley Engineering. He combines his role with completing his PhD in Chemical Engineering at the University of Sheffield. Yeardley specialises in gathering data from clients and performing carbon calculations to present carbon footprints. He then works with the client providing solutions to help reduce their carbon footprint. He utilises his expertise in data analytics, machine learning and python coding to achieve these goals. The fourth industrial revolution (Industry 4.0) is already happening, and it’s transforming the way manufacturing operations are carried out. Industry 4.0 is a product of the digital era as automation and data exchange in manufacturing technologies shift the central industrial control system to a smart setup that bridges the physical and digital world, addressed via the Internet of Things (IoT). Industry 4.0 is creating cyber-physical systems that can network a production process enabling value creation and real-time optimisation. The main factor driving the revolution is the advances in artificial intelligence (AI) and machine learning. The complex algorithms involved in AI use the data collected from cyber-physical systems, resulting in “smart manufacturing”. The impact that Industry 4.0 will have on manufacturing will be astronomical as operations can be automatically optimised to produce increased profit margins. However, the use of AI and smart manufacturing can also benefit the environment. The technologies used to optimise profits 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 the emissions from a company’s supply chain, both upstream and downstream activities. This means scope 3 covers all of a company’s GHG emission sources except those that are directly created by the company and those created 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 quantitated in their GHG emissions baseline. However, in comparison to Scope 1 and Scope 2 emissions, Scope 3 emissions are difficult to measure and calculate. This is because of a lack of transparency in supply chains, lack of connections with suppliers, and complex industrial standards that provide misleading information. The major issues concerning Scope 3 emissions are as follows: • Reliability of data – This includes 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 company. • 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 as they are used to model an entire supply chain. The tools can quickly and efficiently sort through large volumes of data collected from sensors. If a company deploys enough sensors across the whole area of operations, it can identify sources of emissions and even detect methane plumes.

Aaron Yeardley

Digital twin optimisation

A digital twin is an AI model that works as a digital representation of a physical piece of equipment or an entire system. A digital twin can help the industry optimise energy management by using the AI surrogate models to better monitor and distribute energy resources and provide forecasts to allow for better preparation. A digital twin will optimise many sources of data and bring them onto a dashboard so that users can visualise it in real-time. For example, a case study in the Nanyang Technological University used digital twins across 200 campus buildings over five years and managed to save 31% in energy and 9,600 tCO2e. The research used IES’ ICL technology to plan, operate, and manage campus facilities to minimise energy consumption. Digital twins can be used as virtual replicas of building systems, industrial processes, vehicles, and many other opportunities. 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. Predictive maintenance

Predictive maintenance of machines and equipment used in industry is now becoming common practice because it saves companies costs in performing scheduled maintenance, or costs in fixing broken equipment. The AI-based tool uses machine learning to learn how historical sensor data maps to historical 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 that is currently in use. The best part of predictive maintenance is that it does not require additional costs for extra monitoring. Algorithms have been created that provide accurate predictions based on operational telemetry data that is already available. Predictive maintenance combined with other AI-based methods such as maintenance time estimation and maintenance task scheduling can be used to create an optimal maintenance workflow for industrial processes. Conversely, improving current maintenance regimes which often contribute to unplanned downtime, quality defects and accidents is appealing for everybody.

An optimal maintenance schedule produced from predictive maintenance prevents work that often is not required. Carbon savings will be made via the controlled deployment of spare parts, less travel for people to come to the site, and less hot shooting of spare parts. Intervening with maintenance only when required and not a moment too late will save on the use of electricity, efficiency (by preventing declining performance) and human labour. Additionally, systems can employ predictive maintenance on pipes that are liable to spring leaks, to minimise the direct release of GHGs such as HFCs and natural gas. Thus, it has huge potential for carbon savings. Research has shown that underpinning the scheduling of maintenance activities on predictive maintenance and maintenance time estimation can produce an optimal maintenance scheduling. The work optimised the scheduling by minimising costs based on plant layout, downtime, and labour constraints. However, scheduling can also be planned by optimising the schedule concerning carbon emissions. In this situation, maintenance activities can be performed so that fewer journeys are made and GHG emissions are saved. IoT circular economy

The internet of things (IoT) is the digital industrial control system, a network of physical objects that are connected over the internet by sensors, software and other technologies that exchange data with each thing. In time, the implementation of the IoT will be worldwide and every single production process and supply chain will be available as a virtual image. Open access to a worldwide implementation of the IoT has the potential to provide a truly circular economy. Product designers can use the information available from the IoT and create value from other people’s waste. Theoretically, we could establish a work where manufacturing processes are all linked so that there is zero extracted raw materials, zero waste disposed and net-zero emissions. Currently, the world has developed manufacturing processes one at a time, not interconnected value chains across industries. It may be a long time until the IoT creates the worldwide virtual image required, but once it has, the technology will be powerful enough to address losses from each process and exchange material between connected companies. Both materials and energy consumption can be shared to lower CO2 emissions drastically. It may take decades, but the IoT provides the technology to create a circular economy. Conclusion

AI has enormous potential to benefit the environment and drive the world to net-zero. The current portfolio of research being conducted at the Alan Turning Institute (UK’s national centre for data science) includes projects that explore how machine learning can be part of the solution to climate change. For example, an electricity control room algorithm is being developed to provide decision support and ensure energy security for a decarbonised system. The national grid’s electricity planning is improved by forecasting the electricity demand and optimising the schedule. Further, Industry 4.0 can plan for the impact that global warming and decarbonisation strategies have on our lives. tunley-engineering.com

5G garbage trucks driving the future of smart cities

A new 5G project will use high-resolution cameras and GPS sensors attached to waste trucks to rapidly detect road and roadside assets that require maintenance.

An interconnected network of garbage trucks could be the new frontline in repairing local roads, thanks to a research collaboration between Swinburne University of Technology and Brimbank City Council that utilises the 5G network and the Internet of Things (IoT). The project will see high-resolution cameras and GPS sensors attached to Brimbank’s waste trucks. The rich data captured from these connected devices will be sent in real-time to a cloud-based system that can create an easy-to-use map of assets that require maintenance, such as road signs, bus shelters or damaged roads. This will drastically reduce the time it takes to identify, document and fix issues, removing the need for costly manual reporting and auditing, and saving up to 50% of asset auditing costs. Supported by $1.18m in funding from the Federal Government’s Australian 5G Innovation Initiative and working with Optus, the project will also help demonstrate how 5G can reliably support the needs of smart cities around Australia. Bringing the Internet of Things to life

Director of Swinburne’s Factory of the Future and Digital Innovation Lab, Associate Professor Prem Prakash Jayaraman, says the project presents unique challenges that 5G and IoT technology can help solve. “Swinburne is bringing together researchers, government and industry to co-create safe, resilient solutions for smart and sustainable cities. Together, we are enhancing access to services, places and economic opportunities, and improving quality of life,” Jayaraman said. “We are delighted to be working with the forwardthinking Brimbank Council, and utilising Swinburne’s leading capabilities and world-renowned expertise in Internet of Things and digital innovation to demonstrate a solution that can be used in cities across Australia and around the world,” he added. “Residents have told us via the Community Survey that improving the appearance of Brimbank’s roads, road signs, bus stop shelters and roadside spaces is a high priority,” Brimbank Mayor Cr Jasmine Nguyen said, warmly welcoming the partnership. “This innovative 5G-based project offers us a quicker and more efficient way to identify which assets need maintenance, and to get the information to the work crews. Simply put, this project will help Council respond faster to assets that need maintenance.” Cutting-edge technology

The stereo vision and depth cameras attached to the garbage trucks will collect 3D perception data at a rate of 900mbps. For comparison, average mobile download speeds in Australia in 2020 were around 43mbps. To add extra complexity, the garbage trucks travel across every street in the council area each week and need to transmit the data in real-time while moving at varying speeds. This makes the environment perfect for testing the capabilities of the super-fast 5G network, while also helping maintenance teams work more effectively. As part of the project, maintenance teams will be able to get information directly on their phones and upload proof of maintenance performed on the spot. With more than 900km of road under maintenance and an estimated $15m to $20m spent every year of maintaining and improving road and roadside assets in Brimbank alone, it is hoped that the project will not only improve the lives and safety of local residents, but also help councils around Australia save millions.

Reprinted courtesy of Swinburne University of Technology swinburne.edu.au