The power of AI for environmental stewardship and optimised industry

Free podcast: Listen to Pina Schlombs discuss sustainability and the future of industrial AI  with CoLab New Scientist 

We’ve never had such powerful tools to solve the challenges we face in sustainability,” says James Cole, Chief Innovation Officer at the Cambridge Institute for Sustainability Leadership (CISL), in the UK. “Artificial Intelligence (AI) systems have the potential to help us understand the world in all its complexity to optimise industrial processes for holistic business, social and environmental outcomes.”

The arrival of AI across the industrial sector has been heralded as the next great industrial revolution. But unlike past revolutions, which typically accelerated the consumption of resources, AI offers the opportunity to slash waste while boosting efficiency to levels only previously dreamed of. Within this context, global technology company, Siemens, is working to strengthen the emerging link between AI-optimised industry and environmental stewardship as society pushes towards net-zero carbon emissions.

At heart, AI is software that performs tasks traditionally requiring human intellect, such as understanding text, identifying complex patterns, modelling processes, and making predictions. But in an industrial context, AI systems must be engineered for reliability and security. That allows them to be built into the industrial backbone of economies, optimising and improving processes in everything from healthcare and mobility to power generation and infrastructure.

Take water management. One way to improve water sustainability is to lose less of it through leaks. Yet ageing pipes and ground movements make leaks inevitable. “About 30 per cent of the drinking water the world produces is wasted – a shockingly high figure,” says Adam Cartwright, Siemens’ Industry Strategy Director for Software in Water and Waste Water. “Every time you avoid losses, you’re not only saving money but better managing precious water resources.”

VA SYD is one of Sweden’s largest water companies supplying drinking water to over half a million customers. Previously, it was losing 10 per cent of its water but had no means of detecting small leaks.

Water Intelligence

By training an AI model on historical data from VA SYD’s water network, the Siemens Leak Finder application learned to identify and locate leaks, even small ones losing just 0.25 litres per second. That allowed VA SYD to reduce leaks to a world-leading level of less than 8 per cent.

Meanwhile, Siemens is working with Yorkshire Water and the University of Sheffield, using AI to protect the environment at the other end of the water cycle. In combined sewage systems, stormwater runoff and household sewage together flow to water treatment plants. And in times of intense rainfall, combined sewer outlets are designed to release excess water and sewage into rivers to prevent flooding in public areas. One challenge is that blockages in pipes can lead to unnecessary releases, but these obstructions are hard to detect (see diagram).

To address this, Siemens developed a blockage-predicting AI trained on data from thousands of sensors on Yorkshire Water’s sewer outlets, in combination with rainfall data. It learned what a properly functioning sewage network looks like under different weather conditions. When its monitoring system detects flows in the network behaving unexpectedly, it simulates potential blockages in different locations to work out where a real blockage may be developing.

The predictor finds 90 per cent of potential issues; three times more effective than traditional, statistical approaches. And it provides as much as two weeks’ warning of impending blockages, while halving the previous rate of false alarms.

Looking ahead, Cartwright considers the potential for AI and digital technologies to reduce carbon and environmental impact by more carefully managing water and energy use. “Pumping water accounts for 2-3 per cent of a country’s power use,” he says. By ensuring that this pumping is done when energy is at its cheapest and greenest, industrial AI can reduce costs for water and increase resilience for both sectors.

AI is already helping manage the environmental impact of another key part of modern society’s infrastructure: data centres. Their energy usage is significant, much of it used to keep their thousands of servers cool.

But consider Greenergy Data Centers’ facility in Estonia. Already powered solely by renewable energy, the company further reduced its environmental footprint and energy costs using AI.

Servers produce heat depending on their workload but this can change more quickly than conventional cooling systems can react to. To combat this, Siemens developed AI-supported software that uses real-time temperature and airflow data collected by sensors all over the data centre, in addition to information on server workload. The system can then anticipate cooling needs to maintain optimal temperatures throughout the facility.

“When we first launched the system, it improved our efficiency by approximately 30 per cent at the push of a button,” says Kert Evert, Chief Development Officer of Greenergy Data Centers.

It’s an example of AI being part of the solution to one of its own challenges, given concerns over the amount of energy AI requires. Here Pina Schlombs, Sustainability Lead, Siemens Digital Industries Software, notes the outlook of AI computing efficiency is improving drastically.

Consider also an AI model in the industrial space that accelerates product design for optimal environmental lifetime impact or increases resource and energy efficiency, she says. “With a holistic perspective, we can gauge whether the sustainability benefits of AI outweigh the resources to train and run it.”

To realise AI’s benefits within any industry, continues Cartwright, the first step is making full use of existing infrastructure, data and sensor networks. If additional hardware is required, it should be easily linked to existing hardware and asset-management software through secure, standard protocols. “This interoperability is key to supporting the diverse, evolving needs of industrial applications,” says Cartwright. Organisations can then benefit from the convergence of AI with other technologies; its ability to help master complex problems at speed and scale.

CISL’s accelerator programmes reflect this potential, having supported over 350 startups in the past three years, including those showing the power of AI to solve complex challenges. Monumo, for example, is revolutionising electric motor design, enabling more design simulations to be run than conventional approaches – finding faster answers to energy efficient vehicle design.

All this makes AI an ally in humanity’s effort to achieve a smarter, environmentally friendly future. Cole agrees. “AI has the potential to help us make better decisions through greater understanding. This facilitates better collaboration across industries, to foster alignment on how we realise a sustainable tomorrow.”

Hear more from Pina Schlombs in our in-depth podcast interview available to listen now.

Further reading from Siemens here.