AI-Optimized Comminution Operations
This AI solution uses AI to optimize crushing, grinding, and mineral processing circuits in mining operations, dynamically tuning equipment settings and process parameters in real time. By improving throughput, energy efficiency, and ore recovery while reducing wear, downtime, and manual intervention, it drives significant gains in production output and operating margins.
The Problem
“Your comminution circuit leaves millions on the table every year due to static tuning”
Organizations face these key challenges:
Throughput and recovery swing day-to-day with ore variability, making production forecasts unreliable
Control room operators constantly chase setpoints and alarms, but performance still drifts
Energy consumption per tonne creeps up over time with no clear root cause
Optimization studies show gains in trials, but benefits fade as soon as conditions change
Impact When Solved
The Shift
Human Does
- •Monitor SCADA/DCS trends and alarms for crushers, SAG/Ball mills, cyclones, flotation, and separation circuits.
- •Manually adjust setpoints (feed rate, mill speed, water/addition rates, crusher CSS, recirculating load) based on experience and shift targets.
- •Run offline optimization projects and test work, then translate results into operating guidelines and SOPs.
- •Diagnose instability (oscillations, surging loads, trips) after the fact and attempt to retune PID loops or change control logic.
Automation
- •Basic PID/PLC control loops enforce fixed setpoints for speed, level, and pressure.
- •Historian/SCADA systems log data and provide trend plots and alarms but do not self‑optimize.
- •Rule‑based interlocks prevent catastrophic failures by tripping equipment when hard limits are exceeded.
Human Does
- •Define business objectives and constraints (maximize throughput vs. maximize recovery vs. minimize energy) and approve optimization policies.
- •Validate and oversee AI recommendations, focusing on exceptions, constraint changes, and long‑horizon planning (blend strategy, campaign planning).
- •Handle edge cases, safety‑critical overrides, and coordination with mine planning and maintenance (e.g., planned shutdowns, liner changes).
AI Handles
- •Continuously ingest real‑time sensor data (power draw, load, feed size, densities, levels, vibration, reagent flows) and learn the plant’s dynamic behavior.
- •Predict key KPIs (throughput, grind size, energy per tonne, recovery, equipment stress) and compute optimal setpoints subject to safety and equipment constraints.
- •Dynamically adjust crusher gaps, mill speeds, feed rates, water/reagent dosages, and recirculation/partitioning to keep the circuit in its optimal operating window.
- •Detect drifts in ore hardness, fragmentation, or equipment condition and adapt control strategies without manual retuning.
Solution Spectrum
Four implementation paths from quick automation wins to enterprise-grade platforms. Choose based on your timeline, budget, and team capacity.
Historian-Driven Comminution Performance Advisor
Days
Real-Time Comminution Setpoint Recommender
Adaptive Comminution Optimization Controller
Self-Tuning Comminution Digital Twin with RL Control
Quick Win
Historian-Driven Comminution Performance Advisor
A lightweight analytics layer on top of existing historian data that provides data-driven insights and simple recommendations for comminution operations. It uses offline ML models to relate operating conditions and ore hardness proxies to throughput, energy, and grind size, surfacing recommended setpoint ranges and best-practice operating envelopes. Operators still adjust controls manually but with clearer guidance based on historical performance.
Architecture
Technology Stack
Data Ingestion
Extract historical comminution data from plant systems for offline modeling and dashboards.OSIsoft PI / AVEVA Historian
PrimarySource of time-series process data (flows, pressures, power, levels).
OPC UA Connector / PI SDK
Export tags and events from DCS/PLC into data lake or files.
Python ETL Scripts
Schedule periodic extraction of historian data into Parquet/CSV for modeling.
Key Challenges
- ⚠Data quality issues in historian (bad tags, missing data, inconsistent units).
- ⚠Capturing ore hardness and variability with limited online measurements.
- ⚠Gaining operator trust in model-derived recommendations.
- ⚠Ensuring models are only used within validated operating envelopes.
Vendors at This Level
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Market Intelligence
Technologies
Technologies commonly used in AI-Optimized Comminution Operations implementations:
Key Players
Companies actively working on AI-Optimized Comminution Operations solutions:
Real-World Use Cases
Metso AI-Integrated Mineral Processing and Crushing Equipment
This is like putting a very smart autopilot into rock crushers and mineral processing lines. The AI continuously watches how the equipment is running and how the ore behaves, then automatically tweaks settings to get more metal out of the same rock while using less energy and wearing out parts more slowly.
AI-Driven Mineral Processing Optimization Platform
Think of this as a self‑tuning autopilot for a mineral processing plant. It watches all the sensors on your grinding, flotation, and separation circuits, learns what “good” looks like, and keeps nudging setpoints so the plant runs closer to its best performance without engineers constantly babysitting it.
Mining Sector Remodeled by Digital Transformation
This is about turning mines into ‘smart factories in the ground’ by wiring equipment, sensors, and software together so decisions are made with real‑time data instead of clipboards and radios.