PlantIQ Studio

Evidence-Driven Feasibility, Modelling & Practical Decision Support for mining and mineral processing plants

PlantIQ Studio helps plants solve specific operational challenges by combining targeted data collection, fast modelling, and low-risk pilots. Whether the issue comes from raw materials, upstream variability, equipment behaviour, or operating modes, we help you understand what is really happening — and what actions will create real value — before committing to major CAPEX.

When the data needed for analysis doesn’t exist, we gather it.
We deploy quick-install sensing, collect the missing information, and build a clean dataset around the problem.
The outcome is a decision-ready foundation for plant improvements, upgrades, or digital initiatives.

Ideal for plants without large innovation or digital teams.
PlantIQ Studio provides end-to-end capability to investigate problems, build evidence, test solutions, and turn them into industrial systems — without requiring extra internal resources.

What PlantIQ Studio Delivers

PlantIQ Studio helps you answer the core questions behind every plant improvement project:

  • How do raw materials and upstream conditions affect kiln, mill, or plant stability
  • What data is missing, and how can we collect it quickly and reliably?
  • Which improvement ideas will actually create value, and which won’t?
  • What is the expected ROI of making a change?
  • Should we invest in new equipment, sensing, or control logic — or wait?

You gain:

  • a structured dataset built around your specific problem
  • custom models and scenario testing based on real plant behaviour
  • a low-risk MVP or pilot with real “before/after” evidencequantified value estimates (throughput, quality, recovery, energy, stability)
  • operational recommendations grounded in data, not assumptions

When data is missing, we design or provide the sensing required to fill the gaps — ensuring the analysis reflects actual plant conditions, not theoretical assumptions.

How PlantIQ Studio Works

PlantIQ Studio follows a milestone-based approach.
Each step delivers clear outputs that support innovation, plant upgrades, and the development of fully industrial solutions.

1.  Problem framing and diagnostic review

We work with your R&D, process, and operations teams to:

  • define the exact question to be answered
  • review existing data (PLC/SCADA, lab, geology, production logs)
  • identify suspected drivers such as raw-material changes, impurities, moisture, loading patterns, or equipment behaviour

Output: a concise problem definition and shared diagnostic hypothesis.

2.  Data foundation and gap filling

We establish what data exists and what is missing:

  • mapping available data and evaluating quality
  • identifying gaps (material properties, upstream conditions, variability drivers)
  • designing a focused data-collection plan
temporary sensing installation on a conveyor

Where needed, we:

  • select the best measurement points (belts, feeders, chutes, stockpiles)
  • deploy temporary or permanent sensing — computer vision, spectroscopy, multispectral imaging, sonic, induction, and more
  • configure simple, robust data pipelines to collect everything reliably

Output: a clean, analysis-ready dataset that reflects how your plant actually operates.

3.  Modelling and scenarios

With the data foundation in place, we:

  • build diagnostic models linking raw materials, operating modes, and environmental conditions to plant performance,
  • develop digital-twin style scenarios around your specific ideas, such as:
    ♦  sorting or diversion strategies,
    ♦  blending or stockpile changes,
    ♦  control logic adjustments,
    ♦  equipment or configuration options.

Output: transparent models and scenarios showing how each option is expected to behave at your site

Commissioning of Equipment

4.  MVP / pilot in real conditions

We implement a small, low-risk MVP to validate the most promising option:

  • using existing infrastructure where possible
  • using lean sensing and logic instead of large engineering changes
  • comparing “before vs after” performance under normal plant variability

Output: pilot evidence — real-world numbers demonstrating impact.

5. Decision support and next steps

We consolidate everything into:

  • quantified benefits and uncertainty ranges
  • operational recommendations (set-points, rules, triggers)
  • clear options: proceed, adapt, or stop
  • a practical plan to turn the MVP into a fully industrial, plant-integrated system, with the option to deploy it across other sites once proven

Output: a decision-ready package supporting management approval and full implementation.

PlantIQ Studio is shaped by more than 20 years of hands-on work across mining, oil & gas, and mineral processing — including delivering full-scale sensing and decision-support systems in European industrial-mineral operations.
The approach combines practical field experience with modern data, modelling, and lightweight sensing methods to produce insights that hold up in real plant conditions.

Why Plants Choose PlantIQ Studio

Focused on your real problem

Every engagement starts with your plant’s specific challenge — not a generic optimisation framework.

From problem to proof

We help you reach evidence: a model, an MVP, or a pilot that shows what actually works at your site.

Efficient use of time and budget

Lean, milestone-based work avoids unnecessary scope and long studies — your budget goes into learning what matters.

Practical for small and mid-sized plant teams

Clear outputs, real numbers, and realistic implementation plans — even if you don’t have a big innovation or digital department.

PlantIQ Studio — practical, data-driven validation before investing in major changes.

Ready to explore your challenge?

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