Using AI to enhance Metallurgical Performance - What are the factors to be considered?
--- ## 🔧 1. **Process Optimization** AI can identify optimal process parameters for maximizing recovery and grade in real-time.
### Techniques:
* **Machine Learning (ML)** models trained on historical process data (temperature, feed grade, flow rate, pH, etc.)
* **Reinforcement Learning** for dynamic control of variables (e.g., reagent dosage, residence time, flotation air rate)
### Applications:
* Optimizing **Dense Media Separation (DMS)**, flotation, leaching, and smelting parameters
* Reducing energy and reagent consumption
* Predicting optimal setpoints to maximize throughput without sacrificing recovery
--- ## 📊 2. **Predictive Maintenance** Avoid downtime and improve equipment reliability.
### Techniques: * **Anomaly detection** using sensor data (vibration, temperature, current draw)
* Predictive models trained on maintenance logs and failure history ### Applications:
* Forecasting failure of pumps, crushers, cyclones, or flotation cells
* Scheduling maintenance activities proactively to prevent unplanned shutdowns
--- ## 🧪 3. **Ore Characterization & Feed Blending** Improve feed consistency to ensure stable metallurgical performance.
### Techniques:
* **Computer vision** for ore texture and size classification
* **Spectral analysis** (e.g., XRF, LIBS) integrated with AI to identify mineral composition in real-time
* AI-assisted **ore tracking** from mine to mill ### Applications:
* Automated grade control
* Real-time blending strategies to smooth feed variability
* Adjusting processing strategy to suit ore variability
--- ## ⚙️ 4. **Real-Time Monitoring & Control** Implement adaptive process control with AI integration. ### Techniques:
* **Digital Twins** that simulate and predict plant behavior
* **Advanced Process Control (APC)** enhanced with AI
* Real-time feedback loops using neural networks or decision trees
### Applications:
* Real-time adjustment of flotation air rates, DMS media density, grinding pressure
* Early detection of process inefficiencies
* Dynamic adjustment of cut points in DMS
--- ## 📈 5. **Recovery & Quality Prediction** Use AI models to predict metallurgical performance based on current inputs. ### Techniques:
* Regression or ensemble models (Random Forest, XGBoost)
* Neural networks for complex nonlinear process behavior
### Applications:
* Predicting concentrate grade, tailing losses, or overall recovery
* Identifying root causes of poor metallurgical performance
* Suggesting operational changes to improve output
--- ## 📷 6. **Image and Video Analysis** Automate inspection and quality assurance.
### Techniques:
* Deep Learning (CNNs) for image classification
* Machine vision systems for real-time monitoring
### Applications:
* Froth analysis in flotation cells (bubble size, color, texture) * Particle size distribution in comminution circuits
* Slag or matte quality in smelting
--- ## 🔄 7. **Simulation & Scenario Planning** Test operational changes virtually before implementation.
### Techniques: * AI-integrated simulation tools (e.g., METSIM, HSC Chemistry)
* Scenario analysis with ML-enhanced process simulators
### Applications:
* Evaluating "what-if" scenarios for process changes
* Training operators using AI-based virtual environments
--- ## 🧠 8. **Knowledge Capture & Decision Support** Retain expert knowledge and make it accessible across shifts and plants.
### Techniques: * AI-based recommendation systems
* Natural language processing (NLP) for processing historical reports and logs
### Applications:
* AI-assisted control room advisors
* Suggesting actions based on historical performance under similar conditions
--- ## Tools & Platforms * **PI System + ML**:
For time-series process data * **Microsoft Azure ML**, **AWS SageMaker**, or **Google Vertex AI**
* **GE Digital**,
**AspenTech**,
**Seeq**, or
**Metso Metrics**
AI for Process optimization: Where do I get started?
-- ## 🧠 AI in Process Optimization – Key Steps
### 1. **Data Collection and Preprocessing** Start with gathering high-quality historical and real-time data from your plant, such as:
* Ore feed characteristics (grade, PSD, mineralogy)
* Process variables (media density, cyclone pressure, air flow, pH, temperature)
* Equipment metrics (pump pressure, torque, flow rates)
* Output results (recovery, concentrate grade, tailings composition)
✅ **Tip**:
Use a PI System, SCADA, or DCS for time-series data. Clean and standardize your data (handle missing values, outliers, etc.).
--- ### 2. **Model Development** Use **Machine Learning** to model the relationship between input parameters and process outputs.
🧪 **Use Case**: Predict flotation recovery or DMS yield based on ore type, density, cyclone pressure, feed rate.
--- ### 3. **Process Optimization Engine** Layer optimization algorithms on top of ML models.
#### Options:
* **Genetic Algorithms** or **Bayesian Optimization**: for multivariable optimization
* **Reinforcement Learning**: for continuous, real-time adjustments 🎯 Goal: Maximize recovery or grade while minimizing energy/reagent costs and maintaining throughput.
--- ### 4. **Real-Time Decision Support / Closed-Loop Control**
* Integrate the AI model into a **Digital Twin** or **APC (Advanced Process Control)** system.
* AI recommends or automatically applies parameter adjustments based on incoming sensor data.
📉 Example: Adjusting flotation air rates, reagent dosing, or cyclone underflow based on predicted optimal setpoints.
--- ### 5. **Continuous Learning** Use feedback loops to retrain models as process conditions or ore types change.
🔄 **Online Learning**: Keep updating the model with fresh data to adapt to seasonality or geological variability.
--- ## 🧪 Example: Flotation Optimization
AI learns how these inputs affect the outputs and suggests the best settings.
--- ## ⚙️ Tech Stack Recommendations
--- ## ✅ Benefits from AI-Driven Process Optimization
* **Higher recovery & grade**
* **Lower reagent and energy consumption**
* **Fewer process upsets**
* **Increased throughput**
* **Adaptive control for ore variability**
What is AI driven predictive maintenance?
Here’s a full breakdown of how to implement AI-driven **Predictive Maintenance (PdM)** in your operation:
--- ## 🛠️ What Is AI-Driven Predictive Maintenance? It uses **machine learning models** trained on **historical sensor and maintenance data** to **predict failures** before they happen — so you can schedule interventions **just-in-time**.
--- ## 🔁 Workflow Overview
### 1. **Data Collection** Use real-time and historical data from:
* Vibration sensors (for bearings, pumps, motors)
* Temperature sensors
* Acoustic emission sensors
* Pressure and flow readings
* Motor current/load
* Maintenance logs and failure reports
* Operational logs (runtime hours, throughput, downtime)
✅ Example: Vibration patterns from cyclone feed pumps or noise from crusher bearings.
--- ### 2. **Data Processing** Steps include:
* Filtering sensor noise
* Feature engineering: extract relevant statistics (mean, RMS, kurtosis, frequency spectra)
* Labeling data: healthy vs. failure/fault conditions Tools: Python (Pandas, SciPy), MATLAB, Seeq
--- ### 3. **Model Development** You can use:
✅ Example:
Train a model to predict when a **hydrocyclone pump** will fail based on vibration + temperature + flow history.
--- ### 4. **Failure Prediction** Models output:
* **Probability of failure** within a future time window
* **Remaining Useful Life (RUL)**
* **Anomaly scores** for abnormal behavior
✅ Example:
* “Pump #3 has a 78% chance of seal failure within 72 hours”
* “Cyclone is operating outside of normal baseline – investigate motor load trend”
--- ### 5. **Alerts & Integration**
* Display alerts on **operator dashboards** (Grafana, Power BI)
* Trigger **work orders** in CMMS (SAP, IBM Maximo)
* Link to **SCADA systems** for automatic interventions (e.g., diverting flow to standby equipment)
--- ## 🧠 Predictive Maintenance Use Cases in Metallurgy
--- ## ⚙️ Technology Stack
--- ## 🔍 KPIs to Track * % reduction in **unplanned downtime**
* Increase in **equipment uptime**
* Reduction in **emergency repair costs**
* Improved **mean time between failures (MTBF)**
What is the objective of AI with regards to ore characterization and feed blending?
Here's how AI can transform this area:
--- ## 🎯 Objectives of AI in Ore Characterization & Feed Blending
* Achieve **consistent feed quality** (grade, mineralogy, particle size) * Adapt processing parameters to ore variability in real time
* Blend ores to meet **target properties** (e.g., density, hardness, grade)
* Improve **process predictability** and **reduce upsets**
--- ## 🧪 1. **AI for Ore Characterization** AI can extract ore properties using real-time sensor data and machine learning models.
### 🔍 Inputs: * **XRF/XRD data** (chemical and mineralogical composition)
* **Hyperspectral imaging** or **LIBS** (laser-induced breakdown spectroscopy)
* **Particle size analyzers** (PSA, image-based systems)
* **Ore hardness proxies** (Bond index estimates, drop tests)
### 🔧 AI Applications:
✅ Example: Automatically classify ore types (oxide/sulfide/fresh/transitional) on a conveyor belt.
--- ## ⚗️ 2. **Real-Time Feed Blending with AI** Once ore is characterized, AI can **predictively control the blending** of material from stockpiles or direct mine sources.
### 🔀 Key Components: * Stockpile management system (with live grade, size, and hardness info)
* Belt scales, PGNAA analyzers, or lab data feeds * Real-time optimizer (e.g., Python, MATLAB, or vendor software)
### 📈 AI-Driven Optimization:
* Use ML models to **predict plant performance** (recovery, yield, wear) based on feed properties
* Use **Linear Programming** or **Genetic Algorithms** to optimize the blend ratio to:
* Match target grade
* Avoid processing upsets (e.g., too much fines or hard ore)
* Maintain consistent DMS density windows or flotation response
✅ Example: Dynamically blend three stockpiles (A, B, C) to hit a 2.8 SG DMS target while avoiding excess pyrite.
--- ## 🧠 3. **Integrated Ore-to-Plant AI Modeling** Create **digital twins** or hybrid AI models that simulate downstream impact of ore variability.
### Use Case:
* Input: real-time ore composition, PSD, moisture, hardness
* Model: predict **grindability**, **media density response**, **flotation kinetics**
* Output: recommend blending strategy and process settings
--- ## 🗂️ Tech Stack Recommendations
--- ## 📉 Measurable Benefits
* **Reduction in recovery variability** due to more stable feed
* Lower reagent and energy consumption
* Less downtime due to clogging, density shifts, or grind circuit instability
* Better product quality (e.g., fewer off-spec shipments)
--- ## ✅ You Can Start With:
1. **A blend optimizer prototype** in Python (for 2–4 stockpiles)
2. **AI model template** to classify ore type from XRF/hyperspectral data 3. **Flowchart** of an AI-integrated ore tracking and blending system Would you like help creating an **AI-based ore blending optimizer** or a **real-time characterization system** based on your available data/sensors?
What are the core attributes of an AI-driven Real Time monitoring and control system
Here’s how to implement it step by step:
--- ## ⚙️ Core Architecture of AI-Driven Real-Time Monitoring & Control
### 🧩 1. **Data Acquisition Layer** Continuously capture time-series data from:
* **Process sensors** (flow rate, pressure, density, temperature)
* **Quality analyzers** (PGNAA, XRF, XRD, froth cameras, PSD analyzers)
* **Control systems** (PLC/SCADA, DCS)
✅ Example: Real-time cyclone feed pressure, underflow density, DMS SG, flotation air rate.
--- ### 🧠 2. **AI/ML Layer** Train models to **predict future outcomes** and recommend **optimal setpoints**.
#### Model Functions:
| ✅ Example:
Predict when media density in DMS will drift out of range based on feed changes.
--- ### 🛠️ 3. **Control & Optimization Layer** Use AI model outputs to **adjust process parameters in real time**, either autonomously or via operator assistance.
#### Control Techniques:
| ✅ Example:
Adjust cyclone feed rate, underflow valve, and media density automatically to maintain sharp DMS cut point.
--- ### 📊 4. **Visualization & Alerts** Display actionable insights in control rooms or dashboards.
* Trends, KPIs, and model predictions (recovery, grade, downtime risk)
* AI-driven alerts (e.g., “Approaching flotation instability”)
* What-if impact of control decisions Tools: Grafana, PI Vision, Power BI, Seeq
--- ## 🎯 Use Cases by Unit Operation
--- ## 🧠 Real-Time AI Example:
Flotation Control ### Inputs:
* Froth camera data (bubble size, color, texture)
* pH, air rate, pulp density, reagent dose ### AI Actions:
* Detect froth instability or grade drop
* Adjust air rate and reagent dosing live
* Predict and prevent overflow or losses
--- ## 📈 Benefits
* 📉 **Reduced variability** in recovery, grade, and throughput
* 🔄 **Faster response** to ore or equipment changes
* 📉 **Lower operating costs** (reagent, energy, wear)
* ⏱️ **Fewer manual interventions** and human error
* 📈 **Sustained optimal performance**, even under variable conditions
--- ## 🧰 Recommended Tools & Platforms
--- ## ✅ Want to Start Small?
I can help you prototype:
1. A **real-time AI monitoring dashboard** (e.g., for DMS or flotation)
2. A **Python-based control model** that predicts outcomes and suggests setpoints
3. An **alerting system** for anomalies in key process KPIs Would you like to focus on a specific process unit (e.g., grinding, DMS, flotation)? Or should I show you a flow diagram or code example?
How to use AI for Recovery and Quality predictions?
--- ## 🎯 Objectives
* Predict **metal recovery** and **product grade** in real-time or ahead of time
* Diagnose root causes of **performance drops**
* Enable **“what-if” analysis** for process changes
* Support **dynamic control** and **feedforward optimization**
--- ## 🧠 1. What You’re Predicting
| --- ## 📥 2. Key Input Variables for Models
✅ For **DMS**: media SG, cyclone pressure, particle size
✅ For **Flotation**: pH, air rate, froth features, reagent dose
--- ## 🤖 3. AI/ML Models You Can Use
✅ Best Practice: Train multiple models and use ensemble averaging to improve accuracy.
--- ## 🧪 4. Example Use Case: Flotation Recovery Prediction
### 📥 Inputs: * Feed grade (Cu, Fe)
* Grind size (P80)
* Air flow, pH, reagent dosing
* Froth texture (from camera/CNN)
* Residence time
### 🎯 Outputs:
* Cu recovery (%)
* Cu concentrate grade (%)
* Expected tailings grade
### 🧠 Model:
* Train Random Forest or XGBoost on historical data
* Feature engineering: combine variables like air/pulp ratio, ΔpH, ore type
--- ## 🧰 5. Implementation Steps
1. **Data Preparation**
* Use cleaned time-series data (e.g., 1-min or 5-min intervals)
* Sync output tags (e.g., assay results) with input sensor data via timestamp alignment
2. **Model Training & Testing**
* Split into train/test sets by time to preserve sequence
* Use cross-validation to avoid overfitting
3. **Validation & Explainability**
* Use SHAP values or feature importance to understand drivers
* Compare predicted vs actual recovery/grade over time
4. **Deployment**
* Push model into production (on-premise or cloud)
* Connect to DCS or SCADA to provide real-time predictions and alerts
--- ## 📊 6. Visualization & Action
* Plot predicted vs actual recovery or grade in Grafana, Seeq, or Power BI
* Set up alerts for prediction drift (e.g., “Recovery expected to drop by 3% in 30 min”)
* Allow operators to simulate “what-if” scenarios by adjusting variables
--- ## ⚙️ Tools to Use
--- ## ✅ Results You Can Expect *
📈 3–10% increase in **recovery**
* 📉 5–20% reduction in **off-spec product**
* 🔄 Faster reaction time to process variation
* 🔍 Better **root cause diagnosis** of recovery dips
--- Would you like a **code template** to build a recovery prediction model (e.g., XGBoost)? Or help designing a **real-time dashboard** that shows predicted vs actual recovery in a live process?
Use of AI for Image and Video Analysis
Here’s how you can leverage AI for **recovery improvement**, **equipment reliability**, and **quality control**:
--- ## 📷 1. Froth Flotation Monitoring (Real-Time Video)
### ✅ Goal: Improve flotation **recovery**, **grade**, and **stability** **AI System**:
* Uses video feeds from flotation cells
* Processes images with **convolutional neural networks (CNNs)** and optical flow
* Classifies froth conditions (e.g., “too stable”, “boiling”, “draining”) **Extracted Froth Features**:
* **Bubble size and distribution**
* **Color and brightness**
* **Texture (smooth, turbulent)**
* **Velocity / flow patterns** **Actions Enabled**:
* Auto-adjust air rate, frother dose, pulp level
* Alert operator when froth behavior deviates from target profile
* Predict recovery changes 10–30 min in advance 🛠 Example Tools: Metso VisioFroth, DeepFroth AI, custom CNN models with OpenCV + TensorFlow
--- ## 📸 2. Conveyor Belt Ore Characterization (Image Analysis)
### ✅ Goal: Maintain consistent feed to crushing, DMS, or grinding circuits **AI System**:
* Cameras mounted above conveyors
* Computer vision models estimate:
* Particle size distribution (PSD)
* Lumps vs fines ratio
* Ore color and texture (to infer ore type)
* Moisture sheen (via reflectivity) **Outcomes**:
* Control crusher gap or feeder speed to manage throughput
* Optimize blending and avoid oversize or too-fine feed
* Alert for "ore type switch" (oxide vs sulfide vs transition) 🛠 Example Tools: SmartSizer, WipWare, BeltVision, custom OpenCV + PyTorch/CNN models
--- ## 🧱 3. Leach Pad & Heap Health (Drone + AI)
### ✅ Goal: Detect **blockages**, **channeling**, or **dry spots** early **AI System**:
* Drone images or thermal cameras detect anomalies in:
* Irrigation uniformity
* Leachate pooling
* Heat zones indicating under-irrigated areas **Actions**:
* Adjust irrigation layout or flow
* Identify subsurface failures before recovery drops 🛠 Tools: DroneDeploy + CNN segmentation models + thermal camera feeds
--- ## 🧼 4. Screen, Filter & Trommel Monitoring
### ✅ Goal: Prevent downtime, improve classification Use high-speed video + computer vision to:
* Detect blinding or pegging in screens * Monitor spray pattern uniformity * Confirm oversize/fines movement 🛠 Tools: OpenCV with edge detection, motion tracking, YOLO models for particle/object detection
--- ## 🔧 5. Equipment Condition Monitoring via Video **Use Cases**:
* Visual inspection of conveyor alignment or belt damage
* Visual detection of slurry leaks, spillages, valve failures
* Monitoring motor movement or abnormal vibration visually Can use **YOLOv8**, **OpenCV motion tracking**, or **thermal imaging** for early-stage fault detection.
--- ## 📽️ 6. Digital Twin Enhancements via Video Feeds
* Use visual inputs to **validate and calibrate process models** (e.g., froth appearance vs predicted kinetics)
* Feed real-time images into **hybrid AI-physics models** for improved prediction of recovery or product quality
--- ## 🛠 Implementation Stack
--- ## 📈 Measurable Benefits
* **+5–15% improvement in recovery** (froth optimization)
* **Reduction in downtime** via early visual anomaly detection
* **Tighter control over PSD** and ore blending consistency
* **Fewer human inspections**, safer and more consistent monitoring
--- ## ✅ Next Steps for You Would you like:
* A **starter Python script** to process flotation froth video?
* A **design sketch** for an AI camera system for conveyors or tanks?
* Help choosing **hardware + model architecture** for your use case? Let me know your plant priorities, and I’ll tailor a solution (e.g., DMS camera, flotation froth, conveyor monitoring).
Use of AI in Simulation and Scenario Planning
--- ## 🎯 Why Use Simulation & Scenario Planning?
* 🧠 **Predict performance** under changing feed, process, or equipment conditions
* ⚖️ **Balance trade-offs** between recovery, grade, energy use, and reagent cost
* 🔄 **Train operators** and validate control strategies before implementation
* 📊 **Support decisions** with quantitative, data-driven insights
--- ## 🧩 1. Types of AI-Enhanced Simulations
--- ## 🛠 2. Tools and Frameworks
--- ## 🧪 3. Inputs for Scenario Modeling
--- ## 📈 4. Examples of Metallurgical Scenarios to Simulate
### 📊 A. **DMS Simulation**
* Vary feed SG and PSD to evaluate impact on sink/float recovery
* Change cyclone geometry or media type and simulate density windows * Use Monte Carlo to simulate impact of ore variability over 1 month
### ⚗️ B. **Flotation Circuit Simulation**
* Model frother dose vs. grade-recovery curve under 3 ore types
* Evaluate what happens if pH drifts by ±0.5 for 2 hours
* Test new collector blend before committing at plant scale
### 🪨 C. **Grinding + Classification**
* Simulate the impact of screen wear or hydrocyclone bypass
* Model throughput vs grind size vs recovery interactions
--- ## 🤖 5. AI-Driven Scenario Generator Create an AI tool that:
1. Learns from historical data and simulations
2. Suggests **“optimal paths”** to reach a recovery or grade goal
3. Warns you about **likely failure points** under specific ore types or process loads
✅ Could be built with:
* Decision trees or XGBoost for interpretable predictions
* Bayesian optimization for trade-off exploration
* Interactive Streamlit dashboard for scenario input/output
--- ## 🧠 6. Value of Simulation in Decision-Making
--- ## ✅ Quick Wins to Get Started
1. **Build a recovery prediction simulator**
— input feed + settings, get predicted output
2. **Develop a “what-if” planning tool**
— simulate pH drift, media SG loss, or feed blending
3. **Wrap existing physics model with AI**
— improve accuracy under real plant noise
--- ## 📌 Want to Try One? I can help you:
* Design a **scenario planner dashboard**
* Create a **Python-based DMS simulation with variable ore inputs**
* Develop an AI layer to enhance your HSC or METSIM model Let me know your target process area (e.g., flotation, DMS, leaching), and I’ll suggest a tailored simulation approach.
How to use AI for Knowledge capture and decision support
--- ## 🧠 What Is It? It’s an AI system that:
* **Captures expert decisions, logic, and reasoning**
* **Links historical data, documents, and outcomes**
* **Provides recommendations** based on similar past situations
* Acts as an intelligent **assistant to metallurgists, operators, and engineers**
--- ## 🧩 1. Use Cases in Metallurgical Plants
| --- ## 🧰 2. Tools to Build These Systems
--- ## 📌 3. Sample Applications
### 📁 A. **Shift Log Analyzer (AI-Augmented)**
* Digitizes handwritten/operator logs
* Uses NLP to extract key actions (e.g., “Air increased due to rising tails grade”)
* Connects logs to process data and plant outcomes
### 📚 B. **SOP Chat Assistant**
* Reads and indexes all plant SOPs, troubleshooting guides, OEM manuals
* Operators ask: “What should I do if cyclone pressure is unstable?”
* AI responds with concise, context-specific answers
### 📊 C. **Decision Tree Builder**
* Auto-generates logic trees from past decisions
* Shows “if-then” paths used by successful operators under various ore types
--- ## 🧠 4. Combine Data + Domain Knowledge Create **hybrid models** that integrate:
* **Sensor & lab data**: (e.g., rising tailings SG, media dilution)
* **Historical logs**: Operator comments and corrective actions
* **Outcome data**: How recovery or grade changed post-decision
🔍 This creates a system that not only **remembers what was done**, but **learns what worked**.
--- ## 🔄 5. Feedback Loops
* Experts validate AI recommendations (Human-in-the-loop)
* System learns from new outcomes (e.g., “This intervention improved recovery by 2%”)
* Decision suggestions improve over time
--- ## 📈 Benefits
--- ## ✅ Getting Started Here’s what you can build quickly:
1. **AI search assistant for SOPs, lab reports & logs**
2. **Interactive decision recommender** trained on past interventions
3. **Real-time suggestion system** for recovery or quality anomalies Would you like a prototype of a **decision support chatbot** for your plant? I can show you how to build one using your existing documents, logs, and process data.
What are the available tools and Platforms for AI development and application
--- ## 🧱 1. **Data Infrastructure & Collection**
✅ These feed live and historical process data into AI pipelines.
--- ## 🤖 2. **AI & Machine Learning Platforms**
These tools are used to train, deploy, and monitor AI models:
--- ## 🖥 3. **Visualization & Dashboards**
Used for live monitoring, prediction, and scenario analysis:
| ✅ AI predictions can be embedded alongside real-time plant trends.
--- ## 🧪 4. **Simulation & Digital Twin Tools**
Used for scenario planning, optimization, and training:
✅ Combine simulations with AI to evaluate “what-if” cases and test interventions safely.
--- ## 📚 5. **Knowledge Capture & Decision Support** Capture SOPs, logbooks, documents, and convert them to searchable intelligence:
✅ Enables operators to “ask” an AI for guidance, troubleshooting, or SOP recall.
--- ## 🎥 6. **Computer Vision (Image/Video Analysis)** Used for flotation, conveyor belt, DMS and equipment monitoring:
--- ## 🧠 7. **Workflow Integration & Automation**
--- ## 🧩 Example AI Stack for Metallurgical Plant
--- ## ✅ Next Step Would you like:
* A suggested stack for a **DMS or flotation plant**?
* Help designing a **real-time AI dashboard**?
* Code template for deploying an **AI model on plant data**?
Let me know your process focus or site architecture — I’ll tailor a stack recommendation or prototype to match.
