Why Mining Operations Choose Circuito AI
Most process optimization tools are legacy control systems with AI features added as an afterthought. Circuito AI was built from the ground up as an AI-native platform purpose-designed for mineral processing — delivering measurable, verified results on your existing infrastructure.
What Sets Circuito AI Apart
Six fundamental differences between Circuito AI and conventional process optimization approaches
AI-First Architecture
Rule-based control systems (MPC, APC) with AI features added as an afterthought. Static models that degrade as ore conditions change and require constant manual recalibration.
Built from day one on reinforcement learning and neural networks. Models continuously retrain on fresh operational data — adapting to ore blend changes, liner wear, and seasonal variations without manual intervention.
Cross-Process Optimization
Each circuit optimized in isolation — grinding, flotation, and crushing managed by separate systems. Local gains in one circuit often degrade performance downstream.
Unified optimization strategy connecting crushing, grinding, and flotation as one integrated system. Prevents local gains from creating downstream losses. Proven to reduce manual interventions by 96%.
10-Second Control Frequency
Optimization cycles run every 15–20 minutes. Operators make manual adjustments based on experience between cycles, reacting to problems rather than preventing them.
AI makes control interventions every 10 seconds — managing up to 46 control actions per flotation line simultaneously. 120x faster than manual response, capturing optimization opportunities that slower systems miss entirely.
A/B Verified Results
Results based on simulations, internal benchmarks, or cherry-picked time periods. No statistical methodology to isolate the optimization system’s contribution from normal process variation.
Every result measured through statistically significant A/B testing — alternating AI-optimized and manual-baseline operation under identical ore conditions. Results verified jointly by our team and the client’s process engineers.
On-Premise Data Sovereignty
Cloud-based or hybrid platforms that require process data to leave the plant. Ongoing concerns about data security, latency, and regulatory compliance across jurisdictions.
100% on-premise deployment. All data processing, model training, and control happens within your plant network. No cloud dependency for operational control. Remote access is strictly regulated and authorized.
Fast Time to Results
Enterprise integration projects that take 12–18 months before measurable results. Complex vendor dependencies, extended commissioning, and long ramp-up periods.
4–5 months from project start to measurable results. Data diagnostic, platform deployment on existing infrastructure, and A/B validated pilot tests — all before committing to full operation. Typical payback: 3–6 months.
What Circuito AI Won't Do
Circuito AI requires 3-6 months of historical process data to build reliable models — there are no shortcuts to AI that works in production. It won't replace your process engineers or your DCS. And it's not the cheapest option on the market. What it will do is deliver measurable, verified results on your existing infrastructure — and prove it through A/B testing before you commit to full deployment.
Works With Your Existing Infrastructure
Circuito AI is 100% DCS and OEM agnostic. It integrates with any control system and equipment manufacturer — adding an AI intelligence layer on top of your current platform, like adding autopilot to a car that already has cruise control.
Metso
Visit WebsiteOCT platform with rule-based APC, fuzzy logic, and MPC. Proprietary sensors including RockSense, MillSense, and FrothSense for grinding and flotation monitoring.
Traditional APC — not AI-native. Static rules that don’t learn or adapt to changing ore conditions. Optimization capabilities strongest with Metso’s own equipment.
AI that learns and adapts in real-time via reinforcement learning. Works WITH Metso’s sensors and equipment, adding an intelligent optimization layer that goes beyond rule-based control.
ABB
Visit WebsiteExpert Optimizer with MPC, neural networks, and fuzzy logic on 800xA DCS. Over 25 years of advanced process control experience across industries.
Traditional MPC architecture — not modern deep learning. Cross-industry approach means generic models, not mining-specialist AI. Best results on ABB’s own 800xA DCS.
Integrates with 800xA AND any other DCS. Adds modern AI/ML that goes beyond legacy MPC — mining-specialist algorithms built by process engineers with 20,000+ production hours.
FLSmidth
Visit WebsiteECS/ProcessExpert (PXP) for grinding and flotation APC. SmartCyclone and LoadIQ smart sensors. AI/ML integration beginning with PXP v8.5+.
AI/ML integration still maturing. Equipment-centric — optimization capabilities tied to FLSmidth hardware ecosystem. Complex implementation path.
Closed-loop AI optimization alongside FLSmidth equipment and sensors. No hardware dependency — Circuito AI delivers the AI layer that PXP alone cannot provide.
Honeywell
Visit WebsiteForge APC with Profit Controller (MPC), Profit SensorPro (virtual sensors), and Plant-Wide Optimizer. Experion PKS DCS covering the full mineral processing chain.
Heritage is refining and chemicals — mining is a secondary focus. Traditional MPC, not AI-native. Limited published mining-specific optimization results.
Mining-specialist AI built by PhDs with 20,000+ production hours in mineral processing — not a refining APC adapted for mining. Integrates with Experion PKS and any other DCS.
Schneider Electric
Visit WebsiteEcoStruxure Process Expert IIoT platform with APC. Plant Advisor for AI self-learning. Modicon M580 ePAC native integration. Broad industrial coverage.
Mining represents approximately 15% of platform focus — not a mining specialist. Generic optimization platform, not purpose-built for mineral processing circuits.
Purpose-built for mineral processing by mining PhDs. Integrates with EcoStruxure infrastructure while adding domain-specific AI that generic industrial platforms lack.
Frequently Asked Questions
What makes Circuito AI different from traditional process control systems?
Circuito AI was built from day one on reinforcement learning and neural networks — not a legacy control system with AI added as an afterthought. It optimizes across crushing, grinding, and flotation as one integrated system, making AI control interventions every 10 seconds versus manual adjustments every 20 minutes.
Does Circuito AI work with existing mining equipment and DCS?
Yes. Circuito AI is 100% DCS and OEM agnostic. It integrates with Metso, ABB, FLSmidth, Honeywell, Schneider Electric, and any other control system or equipment manufacturer — adding an AI intelligence layer on top of your current infrastructure.
How quickly can Circuito AI deliver measurable results?
4 to 5 months from project start to measurable, A/B verified results. This includes data diagnostics, platform deployment on existing infrastructure, and industrial pilot tests. Typical payback period is 3 to 6 months after results are achieved.
Start With a Free Assessment
We'll analyze your plant data and show you where AI can move the needle — no commitment, no contract. If the numbers make sense, we run a pilot on one circuit before you scale.