With the advent of Industrial IoT (IIoT), the way facilities gather, process, and analyze their data changed greatly. One area that was impacted by IIoT is emissions monitoring as continuous data from sensors would be helpful for tracking plant environmental performance and equipment health.
Instead of having scheduled inspections and using monitoring equipment separately, now a modern monitoring system utilizes sensors, edge devices, cloud platforms, and data analytics tools providing near real-time insights.
What does modern monitoring system look like?
The traditional limitations
There are plenty of plants that still rely on scheduled measurements or monitoring equipment. Although these methods allow you to meet all your reporting requirements, they bring certain limitations like:
- Operational limitations
- Manual data collection
- Delayed anomalies detection
- Inability to analyze trends
- Remote access issues
As your facility becomes more connected, the above-mentioned limitations become more obvious.
A modern monitoring architecture
A typical cloud-based monitoring system has several layers.
Field instrumentation
Data starts its journey right at the source.
Typical field instruments include:
- Gas analyzers
- Particulate monitors
- Opacity monitors
- Flow sensors
- Temperature sensors
- Pressure transmitters
These instruments provide continuous data from industrial stacks.
Edge Processing
Prior to transmitting data to the cloud, the edge processing performed by these devices includes:
- Signal Conditioning
- Validation
- Protocol Translation
- Temporary Storage
- Alarm Generation
This decreases redundant traffic over the network, and improves reliability.
Secure Data Transmission
Industrial sites commonly employ communication methods such as:
- Ethernet
- Modbus
- OPC UA
- MQTT
- Cellular Connectivity
- Specialized Industrial Networks
The choice of protocol is based on the available site infrastructure, acceptable latency, and cybersecurity considerations.
Cloud Analytics
Once data arrives at the cloud, organizations can:
- Store Historical Data
- Create visualizations
- Set up alerts
- Create compliance documentation
- Compare different sites
- Remotely monitor their assets
Using the cloud platform makes collaboration between operations teams and environmental experts easy.
Importance of Continuous Data
Continuous monitoring allows engineers to detect slow changes which might otherwise be missed using periodic inspection.
Some examples include:
- Increasing levels of particulates
- Flow inefficiencies
- Drifting sensors
- Degradation of equipment
- Abnormal operating conditions
Often times, early identification of the issue allows maintenance crews to investigate before impacting production or compliance requirements.
Where AI Comes in
With growing amounts of data, AI and machine learning may prove valuable for such purposes as:
- Predictive maintenance
- Anomaly detection
- Equipment failure forecasting
- Automated analysis of trends
- Smart prioritization of alerts
AI does not replace the engineering knowledge but helps analyze large volumes of sensor data faster.
Security Must Not Be an Afterthought
Connecting industrial devices creates certain security challenges.
Some best practices may include:
- Encryption of communications
- Authentication mechanisms
- Access control according to role
- Monitoring of traffic
- Updating the firmware regularly
These aspects must be considered at the design stage of the system architecture.
Conclusion
Industries are moving away from standalone devices used for monitoring emissions and towards creating connected data environments which utilize field sensors, edge computing, cloud technologies, and advanced analytics capabilities to increase visibility of operations while meeting compliance requirements and facilitating maintenance activities.
If you want to learn more about stack monitoring technologies, industrial emissions monitoring, and cloud-connected solutions, check out our Emissions and Stack website that contains technical information about the above-mentioned systems.
With growing number of applications for IIoT, emissions monitoring becomes another source of operational intelligence besides compliance management.
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