Virtual Sensor
Unlock continuous quality insights and predictive guidance that traditional measurement methods can’t deliver.
Why Virtual Sensor?
Manufacturing operations face critical blind spots where the most important quality parameters—composition, purity, efficiency—cannot be measured directly in real-time. Laboratory analyses introduce delays, online analyzers require expensive maintenance, and some variables simply cannot be measured at all.
These measurement gaps force operators to make decisions based on outdated data, leading to reactive operations, quality variations, and missed optimization opportunities.
What is a Virtual Sensor?
Virtual sensing transforms existing sensor data into comprehensive process visibility through intelligent modeling. Instead of installing costly hardware, the Orise Virtual Sensor uses current plant data combined with advanced algorithms to estimate critical parameters continuously.
How Virtual Sensor Works
Step 1: Identify measurement gaps and define requirements based on your process needs
Step 2: Build models using historical plant and laboratory data
Step 3: Deploy real-time predictions to estimate unmeasurable parameters continuously
Step 4: Optional optimizer provides actionable setpoint recommendations by calculating the ideal settings
Step 5: Optional integration with control systems for automated optimization
How Orise’s Virtual Sensor Outperforms Traditional Methods
Virtual Sensor delivers what laboratory measurements, rule-based control, and physical inline sensors cannot: continuous quality visibility for unmeasurable parameters with actionable guidance that adapts to process changes. Unlike expensive analyzers that require constant maintenance, Virtual Sensor scales seamlessly across assets while providing real-time insights that traditional methods simply cannot match.
The Benefits of Virtual Sensor Technology
Early Detection
Interventions at the first signs of deviation keep processes under control, giving operators time to stabilize production before quality loss occurs.
Consistent Quality
Predictions and guidance ensure output stays stable and closer to specification across production runs.
Higher Throughput
Fewer interruptions and less rework enable lines to run more efficiently, with greater capacity and reduced downtime.
Efficient Resource Use
Operators and engineers focus on targeted actions instead of troubleshooting and late corrections.
Knowledge Transfer
Advisory recommendations capture best practices, making expertise explicit and reducing reliance on individual experience.
Scalability
New parameters, assets, or sites can be added seamlessly without rebuilding the system from scratch.
Virtual Sensor in Action: Case Studies
Pomuni: Real-Time Quality Monitoring
Challenge: Manual, offline quality control of potato mash dry-matter content led to undetected defects and inefficient resource management.
Solution: Developed a Virtual Lab with virtual sensors predicting dry matter content after spiral freezing, based on upstream data including inline sensors.
Benefits: Reduced food waste through real-time dry matter estimation, improved inline quality control, and enabled faster decisions with energy savings.
Leading Chemical Company: Consistent Quality Control
Challenge: Physical inline pH sensors frequently clogged and produced inaccurate values, delaying corrective actions and creating quality deviation risks.
Solution: A predictive model estimated intermediate pH in real-time using historical process and lab data, auto-calibrating against lab samples when available.
Benefits: Achieved continuous and reliable pH visibility, reduced dependency on unreliable inline sensors, and improved consistency of downstream process quality.
VPK Paper: Improved Yield During Startup
Challenge: Inline water content sensors were unreliable during process phases, forcing operators to rely on delayed lab results.
Solution: A predictive model continuously estimated final product water content using available process signals, filling gaps when physical sensors were unavailable.
Benefits: Reduced production time, improved stability after disturbances, and gained continuous quality visibility even when inline sensors failed.
Frequently Asked Questions
What industries benefit most from virtual sensor technology?
Virtual Sensor excels in pharmaceutical, biotech, chemical, energy, food, and beverage manufacturing where quality parameters are critical but difficult to measure inline.
Does the Orise Virtual Sensor work with existing control systems?
Yes, Virtual Sensor integrates seamlessly with existing DCS and SCADA systems through standard protocols, allowing predictions to be used directly in control loops when desired.
What data is required to use Virtual Sensor?
Historical process data and laboratory measurements are the primary requirements. The system works with your existing data infrastructure without requiring additional hardware.
How accurate are virtual sensor predictions?
Orise’s Virtual Sensor delivers highly reliable predictions with exceptional accuracy, continuous availability, and no maintenance requirements.
What kind of technology can Virtual Sensor be integrated with?
Orise’s Virtual Sensor can integrate with both our MPC Platform and Industrial AI Application solutions, making it possible for you to find a truly custom fit for your manufacturing facility.
Ready to Transform Your Process Visibility?
Stop operating with blind spots. Discover how Orise Virtual Sensor can provide continuous quality insights and predictive guidance for your manufacturing operations.