BRAV-7120 Empowers Gas Detection, Leaving Toxic and Hazardous Gases Nowhere to Hide

In frontline energy production environments such as coal mines, oil and gas fields, and power stations, toxic and hazardous gases are hidden threats that can act as invisible killers. Colorless, odorless, and difficult to detect, these gases can endanger worker health, damage equipment, trigger explosions or suffocation incidents, and even lead to severe safety and environmental consequences when leaks occur.

Traditional gas monitoring methods often suffer from coverage blind spots, delayed response times, and limited data utilization, making it difficult to meet the growing demands for safe production and intelligent operation and maintenance.

How to build a high-performance, low-latency, interconnected, and predictive toxic and hazardous gas monitoring system? JHCTECH BRAV-7120 provides the answer.

Current Industry Pain Points

In energy production environments such as coal mines, oil and gas fields, and power stations, toxic and hazardous gases remain a major safety concern. Today, the industry faces five key major pain points:

• Limited monitoring points and blind spots in fixed-sensor coverage;
• Delays between gas anomalies and alarm activation, resulting in missed opportunities for timely intervention
• Massive volumes of data are underutilized, making it difficult to identify patterns and potential risks
• Inadequate maintenance of sensors and monitoring equipment, leading to inaccurate or unreliable data
• Frequent gas leakage incidents resulting in significant safety, environmental, and economic losses

Requirements for Toxic and Hazardous Gas Monitoring System Controllers

To achieve efficient and reliable gas monitoring and integrated control, the controller must meet the following key requirements:

Multi-protocol Compatibility: Supports communication protocols such as RS485, Modbus RTU, and RS232, enabling seamless integration with multiple sensors, including SF₆, O₂, CH₄, H₂S, CO, temperature and humidity sensors, as well as dissolved gas monitoring devices for transformer oil.

Real-time Integrated Control: Equipped with DI/DO interfaces, it can receive alarm switch signals and manual reset inputs, while directly controlling field devices such as audible and visual alarms, fan controllers, exhaust dampers, and solenoid shut-off valves.

Local Edge Computing Capability: Perform data acquisition, data processing, anomaly detection, and trend analysis at the edge, achieving millisecond-level alarm response times without relying on a centralized control center.

Ease of Operation and Maintenance: Supports local display (HDMI), USB data export, and debugging terminal access, facilitating on-site maintenance and system upgrades.

JHCTECH Solutions

The BRAV-7120, powered by the NVIDIA Jetson Orin Nano, delivers up to 67 TOPS (INT8) of AI computing performance, enabling AI models to perform inference in milliseconds. This reduces alarm response times from minutes to seconds, or even milliseconds. Leveraging the NVIDIA JetPack SDK and a rich sensor ecosystem, the system supports automatic sensor calibration, simplifying maintenance while ensuring long-term data stability, accuracy, and reliability. Through real-time monitoring and AI-driven analysis, the system transforms gas monitoring from a reactive process into a proactive prevention strategy. It can provide early warnings of potential faults and gas leakage risks, helping prevent major incidents such as forced substation shutdowns caused by gas leaks, thereby reducing economic losses and improving operational safety.

Hardware Level

• Multiple sensors, including SF₆ leak sensors, O₂ sensors, CH₄/H₂S detectors, CO detectors, temperature and humidity sensors, and dissolved gas sensors for transformer oil, are connected via RS485 and the Modbus RTU protocol for real-time gas concentration data acquisition.
• Some legacy gas alarm controllers and debugging terminals are connected via RS232 to ensure compatibility with legacy equipment in the field.
• A Gigabit Ethernet port connects to the on-site industrial Ethernet switch, enabling data transmission to the SCADA server in the central control center.

• DI interfaces receive alarm signals from gas detectors and manual reset buttons, while DO interfaces control field devices such as audible and visual alarms, fan controllers, exhaust dampers, and solenoid shut-off valves. When gas concentrations exceed predefined thresholds, the system can immediately trigger local interlock control actions.
• HDMI and USB 3.0/2.0 interfaces are also supported, allowing operators to view system status and monitoring dashboards during on-site maintenance, as well as perform data export, troubleshooting, and system upgrades.

Software level

Based on JetPack 6.2 (supports Ubuntu 20.04/22.04), pre-installed with CUDA, cuDNN, TensorRT, etc. The system features:

• A gas concentration threshold detection model that continuously compares sensor readings against predefined thresholds in real time.
• A gas leak trend prediction model (e.g., LSTM/GRU time series model) that predicts leak trends of SF₆/O₂/H₂S/CO/CH₄ based on historical data, enabling early warning.
• A multi-source sensor fusion anomaly detection model that combines gas concentration, temperature, and humidity data to identify complex abnormal events. By correlating data from multiple monitoring points, the system can estimate potential leak locations, evaluate the impact of ventilation conditions on gas dispersion in real time, and generate risk heat maps.

The sensor polling cycle can be configured from 1 to 5 seconds via the Modbus RTU/TCP protocol stack. The collected data undergoes cleaning, outlier removal, unit conversion, and timestamp alignment. TensorRT-accelerated time-series prediction models (such as LSTM/GRU) and anomaly detection models perform real-time AI inference on the edge. Upon detecting an anomaly, a pre-programmed alarm rule engine and ventilation and shut-off control logic issue an early warning. Simultaneously, structured data (gas concentration values, alarm events, and trend prediction results) are uploaded to the central control center's SCADA system.

Solution Value:

✅ Enhance Personnel Safety

Toxic gases such as SF₆ are colorless and odorless, making leaks difficult to detect. Long-term exposure can pose serious health risks, while high concentrations may lead to oxygen deficiency and suffocation. Through real-time monitoring and on-site audible and visual alarms, the system can immediately activate ventilation and exhaust systems when a leak is detected, helping protect personnel safety.

✅ Prevent Equipment Damage

The decomposition products of leaked SF₆, such as SOF₂ and SO₂F₂, are highly toxic and corrosive, potentially degrading the insulation performance of GIS equipment. With early warning and trend prediction capabilities, maintenance personnel can be alerted before insulation performance deteriorates, helping prevent equipment failures and unplanned downtime.

✅ Significantly Reduce Alarm Response Time

Gas monitoring points are often widely distributed, while traditional manual inspection methods involve long inspection cycles and delayed fault detection. Automated data collection enables 24/7 online monitoring with alarm response times below 100 ms, reducing response times by 4 to 6 orders of magnitude compared with manual inspections.

✅ Enable Predictive Maintenance

AI-powered time-series analysis provides trend forecasting and fault prediction, transforming operations from reactive alarm handling to proactive risk prevention and enabling predictive maintenance.

✅ Highly Reliable Edge Architecture

Data acquisition, AI analytics, and integrated control are performed locally at the edge. The system can continue operating independently during network outages and only uploads structured alarm data to the central control system, reducing bandwidth consumption while ensuring system reliability.

The BRAV-7120 edge computing controller combines powerful AI computing performance, rich connectivity, a comprehensive AI software ecosystem, and industrial-grade reliability to build a visible, intelligent, and highly reliable toxic and hazardous gas monitoring solution for the energy industry.

BRAV-7120

Protecting Safety with AI Computing. Predicting Risks with Intelligence.

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