Nov-2022

Artificial intelligence drives the way to net zero

AI and smart manufacturing can be used to optimise profits as well as produce insights into a companys carbon footprint and accelerate its sustainability.

Aaron Yeardley
Tunley Engineering

Article Summary

Businesses are responding to the threat of global warming by quantifying their carbon footprint and reducing their greenhouse gas (GHG) emissions. Companies such as Tunley Engineering aid businesses in data collection, data analysis, and GHG reporting before focusing on reducing emissions from the hotspots calculated in the business carbon assessment. Consultancy from Carbon Reduction Scientists at Tunley Engineering is pivotal for businesses to accurately measure their emissions because of the sheer difficulty and expense of measuring the full extent of their business activities.

The fourth industrial revolution (Industry 4.0) is already happening, and it is 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 companys carbon footprint and accelerate its sustainability. Some of these methods are available to help companies reduce their GHG emissions now. Other methods have the potential to reduce global GHG emissions in the future.

Scope 3 identification
Scope 3 emissions are from a companys supply chain, both upstream and downstream activities. This means Scope 3 covers all of a companys GHG emission sources except those 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 (BSR, 2020). 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, a 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 that 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 relevant sensors. If a company deploys enough sensors across the whole area of operations, it can identify sources of emissions and even detect methane plumes (Seeq, 2022).

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 at the Nanyang Technological University used digital twins across 200  campus buildings over five years and managed to save 31% in energy and 9,600 tCO. The research used IESs ICL technology to plan, operate, and manage campus facilities to minimise energy consumption (IES, 2022).

Digital twins can also be used as virtual replicas of building systems, industrial processes, vehicles, and many other opportunities. This virtual environment offers much more testing and iteration, so that everything can be optimised towards achieving its best performance. This means digital twins can be used to optimise building management, creating 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 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 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 to everybody.

An optimal maintenance schedule produced from predictive maintenance prevents work that is often not required. Carbon savings will be made via controlling deployment of spare parts, less travel 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 optimal maintenance scheduling (Yeardley, Ejeh, Allen, Brown, & Cordiner, 2021). 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 fewer journeys are made and GHG emissions are saved.