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Schneider Electric says it is bringing together electrification, automation and digital intelligence to help mines operate as AI-powered, software-defined platforms that connect energy and industrial intelligence.
AI across three connected layers
The company describes three layers through which it helps mining companies apply AI:
- Connected devices: Intelligent devices sense operating conditions and provide data and control capabilities;
- Edge control and computing: Simulation and automation use AI to support flexible, efficient and resilient system performance; and
- Apps, analytics and services: AI-driven insights support collaboration, informed action and enterprise value.
Schneider Electric uses its portfolio of technologies to combine process and energy data from the device, edge and analytics layers with mining expertise and ecosystem integration. This supports digital twins throughout the mining operations lifecycle, from design and construction to operation and maintenance.
Torngat Metals: an integrated rare earth partnership
Torngat Metals is drawing on these expertise and integration capabilities as it develops the Strange Lake rare earth project in Canada. The companies have signed a non-binding Memorandum of Understanding (MoU) to explore a strategic industrial partnership supporting a resilient and responsible rare earth value chain.
The proposed partnership centres on Strange Lake in Nunavik, associated infrastructure in Labrador and a planned separation facility in Sept-Îles. It would connect upstream resource development, technology integration and future industrial demand through an end-to-end collaboration.
The MoU outlines a potential 360° partnership with two objectives:
- Support project development: Apply Schneider Electric’s expertise in electrification, automation, digital systems and sustainable industrial design to a next-generation mining and processing project; and
- Strengthen supply chain resilience: Improve Schneider Electric’s long-term access to a secure, responsible and compliant rare earth and magnet ecosystem for strategic applications.
Strange Lake is expected to produce heavy and light rare earth elements essential to permanent magnets used in renewable energy, digital infrastructure and advanced manufacturing.
Schneider Electric says the proposed collaboration goes beyond project support: it represents a step towards a trusted, end-to-end critical minerals value chain.
Aligning project delivery and future demand
Vivek Kapoor, Schneider Electric’s Global Lead for Mining, Minerals & Metals, said arrangements of this kind are similar in strategic intent to an early-stage offtake relationship. He described them as going beyond what Schneider’s peers typically undertake, while giving both companies an incentive to deliver on project KPIs.
“By engaging at such an early stage and exploring how future industrial demand could support the project, we are invested in its long-term success,” he told IM on the sidelines of the FT Metals & Mining Summit in London last month.
Torngat plans to bring together a mine, seasonal access road and port, and a separation and processing plant capable of producing 15,000 t/y of rare earths. Schneider Electric says it has experience across the four areas needed to support that integrated approach: electrification, automation, digital systems and sustainable industrial design.
Engineering and operational applications
Connecting engineering and operations
Through AVEVA engineering solutions, the CONNECT data services platform, AVEVA PI System and its advanced analytics ecosystem, Schneider Electric says it helps organisations unify operational technology, information technology and engineering technology data. This enables these organisations to apply AI-driven insights and put those insights into practice in near real time.
AVEVA’s agentic AI capabilities, built into its unified engineering software solution, are already helping mining companies and engineering, procurement and construction (EPC) firms improve detailed engineering productivity, the company says. These capabilities automate repetitive tasks and comparisons, helping teams develop and optimise 3D models more efficiently.
SQM: process optimisation
At SQM’s operations, Kapoor says AI-powered predictive and optimisation models delivered:
- A 10-15% reduction in process variability;
- An increase in nitrate yield of up to 1%; and
- A 1-3% improvement in raw material efficiency.
These improvements translate into higher productivity, less waste and improved sustainability.
Roy Hill: a remotely connected pit-to-port operation
Kapoor also highlighted Schneider Electric’s earlier work at the 55 Mt/y Roy Hill iron ore project in Western Australia. The company delivered remote operations and demand chain planning capabilities, integrating mine, plant, rail and port operations into a single remotely controlled ecosystem. These capabilities supported one of the industry’s most advanced greenfield deployments and helped optimise the journey from resource to market.
“This really was a pit-to-port project for Roy HIll,” he said.
For lead EPC provider Samsung C&T, Schneider Electric delivered the port facility’s complete power infrastructure, including 14 switch rooms, transformers, drives, backup power, cooling and power management systems.
Vale: centralised monitoring
Schneider Electric and AVEVA, which Schneider Electric owns, have also worked with Vale in Brazil to integrate, centralise and remotely monitor operations across its Mariana and Itabira complexes.
Schneider Electric says it is also applying hybrid advanced process control and AI to improve milling-circuit performance. In other industrial applications, these solutions have reduced furnace NOx emissions.
Removing data silos and putting insights to work
A shared data backbone
Schneider Electric sees its ability to unify siloed operational data within a scalable, enterprise-ready architecture as a key market differentiator.
That capability is also important to Torngat Metals’ integrated project approach.
In traditional mining and processing environments, data from sensors, control systems and laboratories often sits in disconnected systems, limiting its usefulness, the company says.
Schneider Electric says CONNECT provides a centralised data backbone on top of AVEVA PI System, enabling secure, near-real-time sharing between on-premises systems and cloud analytics platforms.
At SQM, for example, operational data is made accessible in Databricks through Delta Sharing. This supports advanced AI, machine-learning and simulation workflows with near-real-time refresh cycles, according to Schneider Electric.
“This architecture eliminates data silos and provides an open, developer-friendly environment for building and scaling AI solutions – an essential foundation for industrial transformation,” the company says.
From recommendations to operational action
Schneider Electric describes a “closed-loop approach” in which model outputs are fed back into operational systems, rather than remaining separate analytical insights.
At SQM, predictive and optimisation models generate real-time recommendations for feed ratios, temperatures and flows. Schneider Electric says these outputs are written back into CONNECT and displayed in operator tools such as AVEVA PI Vision. Operators can then compare actual performance with AI-driven optimal conditions and act on the recommendations.
Prescriptive setpoint optimisation similarly allows AI models to recommend and guide operational adjustments. In some cases, these adjustments can run automatically to support continuous performance improvement.
“This integration transforms AI from a decision-support tool into an active driver of operational performance,” it says.
Adapting AI models and scaling deployment
Continuous model development
Schneider Electric says its architecture supports rapid development and deployment of machine-learning models, hybrid simulation models and dynamic optimisation algorithms. In practice, this includes:
- Training and retraining machine-learning models using the large volumes of data held in AVEVA PI System, which is widely adopted at mining sites;
- Developing hybrid models that combine physics-based simulations with data-driven AI; and
- Running real-time scenario simulations to optimise production conditions.
At SQM, Schneider Electric says predictive models achieved approximately 90% accuracy, increasing operator confidence and improving decision quality. Model retraining cycles were also significantly reduced, enabling faster adaptation to changing process conditions and continuous performance improvement.
From individual use cases to enterprise-wide deployment
Scalability is another key differentiator, according to Schneider Electric.
The company says its architecture supports deployment across multiple plants, processes and business units, rather than being limited to individual use cases.
At SQM, the success of nitrate optimisation has led to plans to expand into additional plants and incorporate richer datasets, including maintenance and laboratory analytics, the company claims. Schneider Electric presents this as an example of the progression from pilot projects to broader digital transformation.
Looking ahead: an integrated mine-to-metal benchmark
The MoU between Schneider Electric and Torngat Metals points towards a similarly integrated approach. Schneider Electric hopes the proposed mine-to-metal collaboration will establish a benchmark for digital transformation across Torngat’s operations.
“By embedding sustainability, efficiency and digital performance across the project lifecycle, from design through operations, the partnership aims to set new standards for responsible resource development,” it says.
Lead image from the Roy Hill owner-operated mine and processing operations (credit: Roy Hill)
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