Mining, steel & cement

The adoption of technologies like carbon capture and AI-driven maintenance is revolutionizing mining, steel, and cement industries. Collaborative ecosystems are accelerating material and process innovation, reducing environmental footprints while boosting operational efficiency. These advancements enable industries to meet global sustainability targets and achieve competitive growth.

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Innovation in mining, steel & cement

The mining, steel, and cement industries are undergoing a significant transformation as they adapt to global decarbonization and sustainability demands.

Key trends include industrial decarbonization through carbon capture technologies and green hydrogen, material decarbonization with innovations like green steel and supplementary cementitious materials (SCMs), and the rise of predictive maintenance to maximize operational efficiency. Waste reduction and recycling of by-products are also reshaping these sectors, enabling companies to lower their environmental footprint while creating new revenue streams. Case studies demonstrate the potential of AI-driven solutions, predictive safety systems, and asset availability monitoring to optimize production, minimize downtime, and reduce emissions.

These advancements are driving the industries toward a more efficient, sustainable future, while enhancing their ability to meet evolving market and regulatory expectations.

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Mining, steel & cement technology trends

Industrial decarbonization

Global efforts to reduce greenhouse gas emissions drive the push for industrial decarbonization. Technologies like direct air capture, flue-stack carbon capture and introducing hydrogen and alternative fuels for steel production are gaining traction to meet stringent environmental regulations and achieve sustainability goals.

Material decarbonization

As the demand for greener construction materials grows, material decarbonization—through innovations like green steel and supplementary cementitious materials—offers a pathway to lower the carbon footprint of the entire value chain, helping companies meet market and regulatory demands.

Digitalization & Efficiency

Maximizing industrial efficiency with advanced data analytics, AI, and predictive maintenance is essential to reducing operational costs, improving productivity, and minimizing energy consumption across the supply chain.

Product & process improvements:

Companies increasingly focus on improving product quality and operational processes through innovations such as vision learning, moisture control, and automated quality assurance, enhancing overall production consistency and efficiency.

Waste reduction

Waste reduction is becoming critical as industries aim to reutilize by-products from steel and mining processes, reducing environmental impact and creating new revenue streams from what was once considered waste.

Victor Bistritzki

Senior research associate

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Use cases

Sustainable cement production with SCMs

Supplementary cementitious materials (scms) are gaining traction due to their ability to reduce co2 emissions by up to 90% per ton. by integrating scms into cement production, companies can decrease reliance on traditional cement while maintaining product strength and reducing water usage.

The impact:

Carbon footprint reduction | Water conservation | Cost reduction |

85-90%

Reduction in co2 emissions per ton of scm

30%

Replacement of traditional cement in concrete mixes

50%

Reduction in water demand

10%

Improvement in material strength

Optimizing industry data for performance

Steelmaking processes generate vast amounts of data from industrial control and automation systems, yet much of this data is underutilized due to poor storage and analysis. by restructuring data systems using data engineering, ai, and digital twin technologies, companies can unlock actionable insights, optimize performance, and improve decision-making.

The impact:

Increased efficiency | Cost reduction | Improved decision-making |

30%

Improvement in production efficiency

25%

Reduction in energy consumption

40%

Reduction in downtime

50%

Increase in data processing speed

Predictive safety solutions for industrial operations

Using advanced data analytics and ai-driven tools, companies can enhance workplace safety by automating the detection and prediction of safety anomalies. this approach enables more accurate risk assessments and prevents workplace incidents, improving overall safety management.

The impact:

Risk mitigation | Increased efficiency | Safety improvement |

50%

Reduction in workplace incidents

40%

Improvement in safety data accuracy

25%

Reduction in safety-related downtime

30%

Faster incident response time

No items found.

Asset availability through predictive monitoring

Operational downtime due to unexpected asset failures, such as conveyor systems, can lead to major production slowdowns. by deploying predictive monitoring solutions that track the entire asset lifecycle, companies can continuously detect early signs of wear, misalignment, and other potential issues across critical assets. this technology enables proactive interventions before faults occur, significantly increasing the availability of assets like conveyor belts and minimizing production stoppages.

The impact:

Downtime reduction | Increased asset lifespan | Improved process reliability | Production continuity |

35%

Reduction in unplanned downtime

40%

Increase in asset availability

30%

Improvement in failure detection accuracy

20%

Increase in overall material handling capacity

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