Tackling Global Warming With Explainable Machine Learning

Human-influenced climate change will surge within the next two decades, causing extreme heat waves, droughts, and flooding, according to a stark new report produced by the UN's Intergovernmental Panel on Climate Change (IPCC).

The scientists don't sugarcoat their findings, which they call "a code red for humanity." However, they also offer room for hope by making clear that slashing greenhouse gas emissions can stabilize the rising temperatures and prevent the most extreme effects.

With manufacturing being one of the world's heaviest polluters, accounting for roughly a quarter of US emissions, it's clear that CO2-reduction efforts focused on the manufacturing sector are critical for lowering global emissions. Yet this creates a major challenge for manufacturers. How can you reconcile new (and increasingly urgent) sustainability goals with existing needs to boost profit and production volume?

That's where machine learning fits into the picture. With an explainable machine learning solution, industrial engineers no longer have to make tradeoffs. Rather, they can prioritize emissions while simultaneously achieving other goals, including cost savings and throughput optimization.

How can factories leverage data to minimize emissions?

Traditionally, engineers set broad safety margins, designing conservatively in order to mitigate risk. However, this directly translates into more waste, emissions, and energy consumption. Our goal at Fero is to help factories replace conservative estimates with tangible real-time data, so they use only as much energy and raw materials as they actually need. The result? A process that's not only less wasteful, but more optimized and efficient, improving the bottom line.

Sustainability use cases for Fero

  • Recommending the minimum amount of alloys engineers can add to meet tensile strength and yield specs
    Result: Fewer raw materials used; cost savings
  • Shortening production times for each unit
    Result: Reduced per-unit emissions
  • Using digital twins to simulate alternate settings
    Result: Fewer physical plant tests

Historically, manufacturers have focused on quality, cost, and volume; sustainability and emissions have often fallen by the wayside. Fero allows engineers to address these competing objectives at the same time, so they don't have to prioritize. With explainable machine learning, manufacturers can tackle waste management, overuse, and their carbon footprint all at the same time and mitigate some of the most dramatic effects of climate change. But they must act quickly. As the IPCC report makes clear, we don't have much time.

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