Emissions monitoring produces not only compliance reports but provides a stream of continuous operational data which can help to optimize operations and diagnose and plan maintenance procedures. However, developing the architecture of such emissions monitoring system is a difficult task.
Here is how such pipeline should look in the real world.
Step 1: Obtain Reliably Measured Data at the Edge Level
All monitoring processes start with instrumentation.
Gas analysers, dust monitor devices, flow and temperature sensors continuously collect data on process parameters. Before considering anything related to cloud computing and dashboards, it is important to make sure that the measurements will be reliable due to adequate calibration, good sensor condition, and good communication protocol.
Poor quality of the data means costly technical debt.
Step 2: Implement Standard Communication Protocol
Any industrial plant is never homogenous from the perspective of devices manufacturer.
It is much easier to implement integration of monitoring devices with PLCs and SCADA systems using standard communication protocols such as Modbus TCP, OPC UA or MQTT. Also, this approach will make any future upgrade easier.
Step 3: Process Data at Edge Locations
Edge computing is growing in significance for industry monitoring.
Rather than uploading all raw sensor readings to the cloud, edge nodes can filter out noise, check the validity of data being received, monitor abnormal situations, and trigger alarms, only uploading meaningful data afterward.
This strategy cuts down the required bandwidth and increases the system’s responsiveness.
Step 4: Create a Centralized Layer for Storing Data
After validation, emissions data can be kept together with operational metrics such as production output, state of machinery, and energy usage.
Looking at both datasets allows engineers to explore correlations between process changes and performance in terms of emissions rather than examining each system separately.
Context is usually more important than the values themselves.
Step 5: Concentrate on Meaningful Information
The purpose of emissions monitoring systems is not to accumulate more data.
Instead, dashboards must be able to provide answers to questions like:
- Has equipment performance changed?
- Is the emission level increasing?
- Should maintenance be arranged?
- Are operational parameters kept stable?
- What process variables have the greatest effect on emissions?
Systems capable of giving such answers are usually more useful than systems with many plots.
Conclusions
Monitoring industrial emissions is rapidly growing into an important part of the connected manufacturing system architecture. With continued growth in edge computing, IIoT and operational analytics, monitoring systems will play a greater role in reliability, preventive maintenance, and process optimization, not only compliance management.
If you want to know more about instruments used for emissions and stack monitoring, here is Emissions and Stack – an informative resource about gas analysis, particulate dust analysis, flow measurement technologies, and connected monitoring systems.
In conclusion, the best monitoring architectures should be based on one idea – accurate data matters only if it enables people to make better engineering decisions.
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