AI-Powered Industrial IoT Gas Leak Detection System Development Using STM32

In large-scale petrochemical environments, gas leak detection is not just a monitoring task. It is a critical safety requirement.

The client had a large facility with traditional Gas Detection, which was based primarily on a fixed point to detect gases. Most of the traditional systems were reactive since they provided an alert only after a predetermined concentration of gas occurred.

Target Audience

Industrial Safety Officers, Petrochemical Plants, Refinery Operations Teams

Several key issues were identified:

Delayed Detection:

Delay in detection of a leak, sensors were activated only after gas had built up and thus created a lapse in the response to the leak.

Lack of Localization:

It was difficult to pinpoint the exact source of the leak, increasing response time.

False Alarms:

Inaccurate detection, environmental factors such as temperature and humidity variable caused erroneous readings by the sensors.

Limited Coverage:

Limited geographical coverage, posed sensors was not capable of adequately monitoring large and complex pipeline systems.

The problem was addressed by developing an AI-driven, multi-faceted gas detection system utilizing embedded electronics, edge processing, and computer vision.

Hardware Architecture

A custom STM32-based embedded controller was at the centre of our gas detection systems with:

• High-quality pressure transducers
• Industrial-quality gas detectors (MQ and NDIR)
• Temperature and humidity measurement devices to provide environmental correction to the gas measurement.

The STM32 microcontroller was selected for its:

• Low volumetric power consumption
• Real-time processing ability
• High performance and reliability in industrial conditions

Each node was placed at critical pipeline junctions and storage areas.

Analyzing Real-Time Pressure Drops

We developed an algorithm that allows continuous monitoring of pressure variations across pipelines, allowing early leak detection even before gas is dispersive due to low concentrations/values

• Monitor micro level deviations in applied pressures.
• Use statistical anomaly detection techniques.
• Use established patterns from collected data to identify possible leaks.

We created an algorithm that provides early leak identification before gas has begun to create a measurable dispersion.

Edge AI Thermal Imaging

To address localization challenges, AI-equipped thermal imaging cameras were developed with the capacity to process the information collected at the edge of the cloud.

The following configurations were made to the thermal camera:

• Determine the presence of invisible gas plumes by measuring thermal differentials on the surface of objects
• Capture real time video streams
• Run inference on AI on edge devices

A proprietary deep learning model (CNN) was trained using synthetic gas leak simulations, actual industrial datasets, and thermal signatures for each gas.

This model provides for:

• Segmentation of the gas cloud
• Detection of gas leak source
• Tracking of the path of gas dispersal

Inference at the edge has resulted in eliminating latency and decreasing reliance on processing on a cloud-based server.

Sensor Fusion & Data Intelligence

One of the most critical innovations was sensor fusion—combining multiple data streams:

• Pressure data
• Gas concentration levels
• Thermal imaging output

We developed a rule-based + AI hybrid engine that:

• Correlates anomalies across different sensors
• Filters out false positives
• Assigns confidence scores to alerts

This significantly improved accuracy and reliability.

Communication & Alert System

All edge devices were connected via:

• Industrial IoT protocols (MQTT over secure TLS)
• LoRaWAN (for long-range, low-power communication in large plants)

Data was transmitted to a centralized monitoring dashboard where:

• Real-time alerts are generated
• Leak location is visualized on plant maps
• Historical data is stored for analysis

We also implemented:

• SMS and mobile push notifications
• Automated escalation workflows

Dashboard and Analysis

The safety teams created a web-based dashboard that provides the following features:

• Live status of system
• Heat map of the describes the gas activity
• Historical trend prediction leaks
• Monitoring the overall health of each device

Advanced analytics included:

• Predictive maintenance predictions
• Risks on zones of plants
• History and compliances with respect to all incidents.

Step 1: Conduct a Site Survey and Map Risk.

High-risk areas and optimum locations to install sensors were identified.

Step 2: Test prototypes.

Prototypes were tested for accuracy in simulated leak detection.

Step 3: Pilot.

Pilot implementation of the system in a small area of the actual plant.

Step 4: Roll-Out All At Once.

System installed throughout the plant and calibrated.

Step 5: Train Staff and Integrate With Current SCADA Systems.

Trained safety personnel and integrated with existing Supervisory Control and Data Acquisition (SCADA) systems.

• 45% Reduction in Safety Violations
• 60% Quicker Incident Response Time (from proactive detection of leaks)
• Exact Leaking Location (thus minimizing downtime due to leak repairs)
• 90% Reduction in False Alarm Rate
• Increased Compliance with Regulatory Requirements

In addition to the above measurable outcomes, the implementation provided the ability to transition from being a reactive manager of safety to being a predictive manager of risk and preventing injuries in a high-risk industry.

• Low Latency Real Time Edge AI Inference
• Hybrid Anomaly Detection (Rule-Based and Machine Learning)
• Scalable IoT Architecture for Large Scale Industrial Environments
• High Accuracy Gas Visualization with Thermal Imaging
• Communication is Resilient to Harsh Industrial Conditions

We have gone from traditional reactive systems to predictive intelligence that can not only detect leaks before they happen but also help prevent them from occurring altogether.

The scale and flexibility of the solution creates a new standard for intelligent safety systems for industry.