Modern industrial facilities generate increasing amounts of data. Sensors are able to continuously measure emissions, temperature, pressure, particulate matter, air quality, and equipment status.
However, gathering data is not enough.
The true value of such data becomes obvious after connectivity, efficient data processing, and insights generation. In this respect, Industrial Internet of Things (IIoT) starts to be used in environmental monitoring.
What is Industrial IoT?
Industrial IoT is an ecosystem of sensors, machines, gateways, software platforms, and analytics.
IIoT architecture can look like below:
Sensors
↓
IIoT Gateway
↓
Edge Processing
↓
Cloud Platform
↓
Analytics
↓
Dashboard & Alerts
↓
Action
Every layer contributes to transformation of raw measurements to valuable information for operations.
Why Connected Monitoring Is Needed?
Traditional monitoring approaches may involve periodic inspections or manual measurement collection. Although they are useful, they can provide only snapshot of the environmental state.
Connected monitoring enables continuous visibility.
Users can control changing conditions and investigate anomalies more effectively.
For instance, connected sensors can be used to monitor emissions and air quality over time. It will be easier to discover patterns in such case rather than analyze separate measurements.
Smart Sensors' Functionality
Smart sensors are the core elements of IIoT systems.
Depending on requirements, they can measure:
- gas concentrations
- particulate matter
- temperature
- humidity
- pressure
- flow
- vibration
- equipment state
When multiple sensors are connected, companies get opportunity to see bigger picture of what is happening in facilities.
Why Edge Computing is Important?
Not every industrial environment can fully depend on cloud connectivity.
Edge computing makes it possible to perform certain amount of processing locally. It reduces latency and unnecessary data transfer. Also, it allows to do some local monitoring even when cloud connection is lost.
While cloud is responsible for central storage, dashboards, historical data analysis, and general analytics.
Thus, it creates edge-to-cloud architecture.
Adding AI to Industrial Monitoring
After establishing connection and having access to reliable data, companies can implement another layer of processing – artificial intelligence.
Machine learning algorithms are able to analyze historical and live data from sensors in order to detect unusual patterns and anomalies.
AI can be used for:
- anomaly detection
- predictive maintenance
- emissions trends analysis
- equipment diagnostics
- process optimization
However, artificial intelligence needs to be a part of the sensor and engineering processes as well, since it is not supposed to replace any of them.
From Measurements to Decisions
The key benefit of IIoT is not just increasing the number of measurements.
It is creating a way for information to pass through the following stages:
Measurement → Validation → Processing → Analysis → Insights → Actions
Such an approach can allow industrial specialists to base their decision-making on data and its analysis instead of separate observations.
Challenges of IIoT Environmental Monitoring
Creating the connected measurement process still implies certain steps to take.
Companies need to consider the following aspects:
- Sensors' accuracy and calibration
- Data quality
- Cybersecurity
- Network stability
- Integration with legacy systems
- Data storage
- Management of devices
- Scalability
A decent architecture needs to take all of them into account from the very beginning.
The Future of Industrial Environmental Monitoring
Further development of IIoT, AI, edge computing, cloud platforms, and smart sensors makes environmental monitoring more and more connected.
The future is not just adding more sensors. It is creating systems which can transform accurate measurements into timely insights.
For industries, it means increased visibility, timely investigation, better maintenance, and improved decision-making.
To learn more about IIoT, emissions monitoring, AI, and other environmental technologies, visit Emissions and Stack.
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