AI-Material Composition Analyzer

Real time & continuous analyzer with Visual AI

AI-Material Composition Analyzer is a Visual AI system designed to analyze, characterize, and optimize industrial material streams in real time. It enables continuous inspection of alternative fuels, waste, soil, and raw materials through automated image-based analysis.

AI-Material Composition Analyzer

The challenge

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01 Material Variability

Inconsistent input quality affects operational stability. Alternative fuels, waste, biomass, and raw materials constantly vary in composition, size, moisture, and contamination levels.

02 Limited real-time visibility

Traditional analysis methods are slow and reactive. Manual inspections and laboratory sampling delay decision-making and reduce operational responsiveness.

03 Process Inefficiency

Uncontrolled variability impacts performance and costs. Lack of continuous material intelligence can reduce efficiency, increase process instability, and affect overall operational control.

Analyzed Variables

From visual data to operational insights. Variables measured in real time

AI-Material Composition Analyzer extracts key operational parameters from material streams:

Calorific value

Material energy potential for fuel quality and combustion control.

Material composition analysis.

Beyond the visible spectrum, hyperspectral image analysis.

Contamination detection

Detects unwanted materials and impurities that may affect the quality standards.

Foreign object detection

Identifies unexpected or non-process materials within industrial material streams.

Temperature monitoring

Tracks thermal conditions of material streams to support operational stability.

Moisture estimation

Estimates material moisture conditions through AI-powered visual analysis.

Particle size distribution

Analyzes material granulometry to improve process consistency

Material quality assessment

Evaluates visual quality parameters linked to operational efficiency.

Volume and flow characterization

Monitors material accumulation, movement, and flows in real time.

AI-Material Composition Analyzer

AI-powered material intelligence across operations

  • Real-time visual inspection

    • Continuous monitoring of material streams using industrial camera systems.

  • Automated classification

    • AI models identify, segment, and categorize materials without manual intervention.

  • Quantitative material analysis

    • Estimation of size distribution, composition indicators, and contamination levels.

  • Operational integration

    • Seamless connection with industrial control systems and data infrastructures.

  • Scalable AI engine

    • A unified platform adaptable to different materials and industrial environments.

Explore our product ecosystem

Find the solution that best fits your operational needs. Any doubts? Contact us.


AI-Alt Fuel Cement

Real-time analysis of alternative fuels used in cement production. It enables control of calorific value, fuel consistency, and supplier quality, improving combustion efficiency and reducing operational variability.

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AI-Soil  Analyzer

AI-powered classification and characterization of soil and aggregate materials. It supports quality control in construction and raw material processing through continuous visual inspection.

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Mat-Scan-Inferida


AI-Biomass Analyzer

AI-driven visual analysis of biomass materials for energy applications. It enables continuous characterization of biomass streams, improving fuel quality control and operational efficiency in bioenergy and power generation environments.

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Biomass Granulometry


AI-Waste Engine

Automated waste classification and material recovery analysis. It improves sorting efficiency, material recovery rates, and operational visibility in waste management systems.

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AI-Waste Engine - Labels

AI-MATERIAL COMPOSITION ANALYZER

Benefits & Integration

Operational impact through continuous monitoring

Operational Intelligence in Real Time

Continuous monitoring that improves decision-making across the process.
Enables constant visibility of operational conditions, allowing faster quality control decisions and more efficient plant performance.

Less Lab Dependency

Minimizing delays through on-site with automated insights.
Decreases reliance on laboratory sampling by providing immediate data and more agile operational workflows.

Standardization and Quality Control

Consistent classification and stronger control process.
Ensures uniform material classification. Input control for higher and more predictable quality standards.

Industrial Integration

Designed to fit into existing industrial ecosystems without disruption.
AI-Material Composition Analyzer integrates into current infrastructures and can be connected to industrial camera systems, SCADA/DCS environments, cloud platforms, and plant monitoring systems.

Frequently Asked Questions

  • How does the system maintain accuracy in demanding industrial environments?

    Pixelabs’ hardware is designed for continuous operation in industrial environments, with dust or rough conditions. AI models are trained to isolate relevant material data and minimize environmental visual interference, ensuring reliable real-time analysis directly from the material streams.

  • What happens if the composition or characteristics of the material stream change?

    Our technology and AI models are designed to analyze physical characteristics across different kinds of material streams. Model training is adapted to each specific application and material type, while remote adjustments allow the system to maintain high analysis accuracy as operational conditions and material compositions evolve.

  • Does the system require a plant shutdown for installation?

    No. The system is designed as a non-intrusive solution that integrates into existing industrial infrastructures. Installation can be coordinated during scheduled maintenance windows for example, to minimize operational disruption and reducing intervention time on-site.

  • How does the Pixelabs signal integrate with our current control system (DCS/SCADA)?

    Integration is native and straightforward. The system delivers variables (humidity, volume, and PCI) via 4-20mA current loops or standard protocols such as Modbus. This allows the plant system to receive the information as if it were any other process sensor.

  • Does this system replace laboratory tests?

    No. The Pixelabs system acts as a real-time process control tool, analysing 100% of the fuel. Real-time measurement is highly accurate and correlates well with laboratory results, but it is not intended to replace detailed testing of small quantities of material using industry-standard analytical methods. Both systems provide positive feedback for improved plant performance.

  • What is the estimated service life of the system and its maintenance requirements?

    The system is built with highly durable industrial-grade components. Preventive maintenance is limited to cleaning the external protections of the equipment and optics, and replacing bulbs if they burn out. At the software level, the system evolves through remote updates included in the support plan, ensuring that the technology never becomes obsolete, as well as through periodic recalibrations described in a Technical Annex to the product.

Get a closer to our ecosystem and discover how our products will improve your business

Still have questions? Schedule your demo with our experts
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(1)The data reflects how our client currently uses the system. Keep in mind that this information is shared based on mutual understanding. We’ve also estimated potential cost savings using details from our clients, keeping everything within the agreed terms and conditions. We recommend contacting us for detailed information. (2) This is a simplified explanation of the product’s functionality and may not fully represent the complete process involved. These representations are intended for the purpose of clarity and ease of understanding. We recommend contacting us for detailed information. (3) All images used in this website are for illustrative purposes only.

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