Lyncis - Online Coal Analyzer

The Online Coal Analyzer has no gamma-ray, neutron or x-ray radiation. No governmental permissions and licenses are needed for operating, storing and transporting the equipment. No radiation handling personnel is required at premises making it simpler to operate and manage the production or mining process.

Laser-based (LIBS) technology has clear signal analytical lines of Carbon, ash constituents (Si, Al, Fe, Ti, K, Na, Ca, Mg, etc) and volatiles with high signal/background ration and without interference. Averaged and accumulated chemical composition data is updated every 30 seconds to 5 minutes and can be integrated with any type of automation system.

Accurate measurement technology and advanced machine learning algorithms ensure long-lasting and stable online measurements with good correlation with laboratory analysis.
Fully automatic 24/7 operation and integration to customer SCADA provides real-time chemistry of material streams without sampling and sample preparation.

The analyzer is a self-contained unit with minimal maintenance requirements. Routine maintenance involves only regular air filter replacement and basic laser window cleaning. No spatial licenses or certificates are required to operate the equipment.

• Performance monitoring and auto-notification if calibration fine-tuning is recommended.
• Easy addition of new sample points to the calibration database.
• Remote Control and assistance in monitoring and  adjusting machine performance.
• Full SCADA/PLC integration and networking capabilities allow the analyzers to be integrated. into any Industry 4.0 and the manufacturing ecosystem.

Online elemental analyzers are equipped with data processing modules to monitor and learn the material changes during continuous processes.
To ensure accurate and stable measurements comprehensive calibration and data processing algorithms are build using advanced machine learning and chemometrics technics such as PCA/PCR (Principal Component Analysis/Regression), Neural Networks, SVM (Support-vector machine), PLS (Partial least squares regression), Classification algorithms.