Mining & Metals: Time for a decarbonization revolution with Sustainable AI

Elon Musk, CEO of Tesla had announced in 2020 that they would give a “giant contract for a long period of time if you mine nickel efficiently and in an environmentally sensitive way.” After this statement, buyers of nickel started exerting considerable pressure on the mining companies to accelerate decarbonization. Owing to the fact that the mining industry is responsible for 2 to 3% of global carbon emissions, the industry is in the spotlight in terms of sustainability initiatives.

Achieving the United Nation’s net-zero goals and limiting global warming to 1.5 degrees will require the carbon emissions to be reduced by 45% till 2030. Consequently, the mining industry is at the focal point of the decarbonization exercise. Energy transition will be the fundamental activity in the pursuit of the net-zero economy. Given that the metals and mining industry provides raw materials for almost all the heavy industries, the sector is under the scrutiny of investors, stakeholders, and customers.

Better ESG scores means better shareholder value

As per Mckinsey, the low ESG (environmental, social and corporate governance) scores of mining companies are leading to 20 to 25% higher capital costs. It was reported during the pandemic that mining firms with high ESG scores provided 10% higher returns to the shareholders. Companies with high ESG ratings tend to be viewed as better investments in the long term, as they continue to attract interest from investors who want to support the company’s sustainability initiatives.

How mining burdens the environment

Emissions from mining operations can be classified into three main types:

Scope 1- Diesel emissions

Scope 2- Nonrenewable electricity usage emissions

Scope 3 – Supply chain and transportation emissions

A study discovered that Scope 1 and Scope 2 emissions from the mining industry measured about two billion metric tons in 2018. Commodities like Metallurgical Coal used in steel manufacturing, copper, iron ore, gold, and nickel used in lithium batteries have the highest carbon footprint.

Global Emission

The severe problem of metallurgical coal and fugitive emissions

As shown in the above analysis, metallurgical coal used extensively in steel production accounts for the highest carbon emissions – making it an important factor in reducing Scope 1 emissions.

During the coal mining process, a large amount of methane is released into the environment, as fugitive emissions, having a severely damaging effect. Interestingly, the reports state that an underground mine emits far more methane than an open mine.

Ironically, the underground mines require lesser fuels and are less harmful environmentally. Still, the enormous carbon footprint of fugitive methane can make underground mines hugely costly from the point of view of carbon taxes.

Mining Decarbonization: What’s The Way Forward?

Reducing emissions in the mining operations will require various measures such as process improvement, usage of sustainable fuels and electricity, enhanced landfill mining, as well as effective supply chain management.

Operational Efficiency will be the game-changing factor for reducing Scope 1 and 2 emissions. Using cutting-edge technologies like Artificial Intelligence, mining companies can reduce machine downtimes, thereby reducing emissions resulting due to asset overburden. Eugenie’s solutions have helped many global mining companies in increasing their machine availability by as high as 10%.

Supply Chain Transparency: Artificial intelligence can analyze large amounts of data, discover new trends and relationships, provide businesses with a greater understanding of their operations, and even help companies make better decisions.

Complex ecosystems of procurement, production, and distribution can be made more efficient with AI, saving costs as well as reducing carbon footprint.

The cutting-edge products of Eugenie have helped several companies in achieving the optimum performance of supply-chain management with accurate demand forecasting, planning, optimization, and transparency.

Understanding the cause and effects of the supply chain is super easy in Eugenie through an interactive and user-centric platform. Companies have saved as high as 15% of logistics costs through Eugenie, also making a huge difference to the carbon footprint reduction.

Landfill Mining: The world’s most industrial waste is discarded in landfills, resulting in serious environmental hazards due to the emission of methane – a gas more harmful than Carbon Dioxide.

The U.S. generates about 7.6 billion tons of industrial waste every year. Most of the industrial waste is discarded in landfill sites, which usually contains significant residues of valuable metals like zinc, aluminum, copper, steel, or gold. The concept of ‘enhanced landfill mining’ is increasingly gaining popularity where the ‘resource recovery’ of high-value metal residues is excavated.

The primary resources of metals are going to be dwindling with time due to ever-increasing human consumption. The possibilities of landfill mining will be sky high in the future due to the positive environmental and economic prospects.

From Predictive Maintenance to Environmental Sustainability with Eugenie

Most mining operations are asset-heavy and intensive, involving multiple heavy pieces of machinery like Dozers, Trucks, Shovels, Motor Graders, Loaders, Scrapers, etc. The heavy equipment helps in Extraction, Material Handling, and Processing.

Eugenie’s digital ecosystem processes machine data in real-time to detect anomalies and predict probable machine failures. At every stage of processing, Eugenie’s products ensure accurate insights for achieving high performance. Not just Eugenie’s data-based insights, but the human-centered representation of the alerts and predictions make the business decision-making highly uncomplicated. The Explainable AI framework of Eugenie’s products makes the interpretation of insights completely effortless for the operations and maintenance staff.

The achieved machine reliability leads to lesser system redundancies, which is a major contributor to high OpEx and carbon footprint.

How can Predictive Maintenance be applied in Mining Operations

Eugenie’s products Ray-Finn and Papillon have been successfully deployed across global mining companies for several use cases. A few examples of predictive maintenance, handled successfully by Eugenie are:

  • Prediction of boiler bundle leakages
  • Predictive maintenance for roaster air blowers for hydro smelters
  • RMH optimization for hammer mills and vibrating screens
  • Real-time asset monitoring for thickeners and pumps in utility areas
  • Monitoring and optimization of stack emissions from plants and power units

In addition to the above use cases, ore concentration is another aspect that requires extensive fuel processing  – leading to a high carbon footprint. The required quality standards of the final products usually result in increased emissions during ore processing.

Eugenie’s products can be deployed and used within 2 to 3 days, making it a preferred choice for leading mining companies across the globe.

Zero carbon mines: All-important aim for the net-zero future

As the world moves toward a decarbonization-focused energy transition, mining companies will need to evaluate their own environmental footprint and consider how best to reduce emissions across different operations.

Mining companies who’ll fast embrace technology will swiftly benefit in all the vital aspects – be it stakeholder management, regulatory requirements, or economic profits.

Do you need help with your mining operations? We’d be happy to assist your machines with our best-in-class AI products.

Thrive SustainablyTM with Eugenie for a net-zero future.

Write to us at support@eugenie.ai for scheduling a product demo.

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