Improving early fire detection: thermal imaging and the Internet of Things


By warning earlier on the path to ignition, managers of facilities that store biomass can prevent costly and potentially deadly fires before they are allowed to ignite and spread.

Biomass is typically stored in outdoor bulk piles near the power generation facility. These mounds of material are particularly prone to self-heating as they break down naturally. The decomposition process is accelerated as moisture is introduced by rain and humidity, generating even more heat. Since most biomass materials are good insulators, the internal heat generated by the pile cannot escape and cool, thus increasing temperatures and spreading to a larger internal area. Eventually, the material begins to smolder. Smoldering, flameless fires start more easily than flaming fires and are more difficult to extinguish.

Early detection is essential if a bulk biomass pile fire is to be avoided. Unfortunately, it is difficult to detect the formation of a fire at an early stage in a bulk pile fire. For example, the surface temperature of a pile may be ambient while the internal temperature may be above 200 degrees Celsius. Traditional methods using linear heat sensing cable can be used, but are susceptible to damage during material transport and are generally not recommended. Spot measurements are also used but do not detect gradient effects. Tracking temperature trends over time is generally most useful for detecting the early onset of warming, when mitigation measures can be deployed before the situation becomes dangerous.

Infrared (IR) cameras for early fire detection
Infrared cameras work on the principle of heat transfer from radiation. The infrared camera has a focal plane array of detector elements that detect infrared light from object surfaces. The radiation captured by the infrared camera detector is digitized, converted into data and displayed as a visible image. Calibrated infrared cameras can report temperature measurements from specific points, lines and areas on live or recorded images.

IR cameras are the first to alert before a fire develops. They “see” a heating of the material at the beginning of the fire development process before forming smoke particles or flames. These heating materials appear as hot spots in a thermal image and are quantified with regions of interest (ROI) as points, lines or areas that report temperature values. Applying multiple ROIs to an image and setting temperature thresholds per ROI allows monitoring and alarming at multiple locations within the camera’s field of view. When an ROI’s threshold condition is met, alarms trigger notifications to appropriate personnel.

What is IoT (Internet of Things)?
The Internet of Things (IoT) refers to interconnected sensors, instruments, and other devices networked into software applications that use predictive analytics and artificial intelligence (AI). These connected networks create systems that monitor, collect, exchange, analyze, and provide valuable information about a system or process. The IoT is revolutionizing automation by using cloud computing to simplify integration and improve process control.

Thermal imaging and early detection of IoT fires
Fire safety for biomass storage is one area that realizes the benefits of thermal imaging when combined with IoT. By connecting infrared cameras that alert from the earliest stages of development, potential fires can be more easily detected and prevented. Security alerts are sent quickly and efficiently to hundreds of people using the IoT. Communication options include voice calls, text messages and emails to targeted recipients to establish quick and effective awareness. Another benefit is scalability. Facility managers can connect multiple locations to a central monitoring and alarm dashboard. Understanding the situation across all facilities improves monitoring and management of multiple systems from a single point of control.

IoT EFD systems can improve emergency planning by using algorithms and analytics to help prepare better emergency and evacuation plans quickly. For example, the analysis may consider factors such as the number of people in the facility, maps of the facility, the location of the fire, and the rate at which the fire is spreading to develop better evacuation plans. Evacuation plans based on analysis can prevent congestion by guiding workers to different locations for an optimal evacuation route.

IoT early fire detection (EFD) systems are less expensive to install and maintain than traditional detection systems. As the EFD application resides in the cloud, there is no need for a dedicated computer server. Any potential conflict with operating system software is eliminated, as access to the application only requires an internet connection. Users access the EFD system anywhere and anytime with any Internet-connected device. And with the proper credentials, control and alarm settings can be changed remotely to optimize performance.

Another key benefit of a cloud-based EFD system is the ability to share dashboards and map views. For example, sharing a live map view with first responders allows the scene to be assessed before arriving on site, saving time and optimizing safety. These maps identify the location of the alarm sensor, the monitored area, the alarm conditions, the entry and exit points of the installation.

Early detection of IoT fire by IR camera
IR Camera IoT EFD systems for biomass monitoring can integrate multiple sensing technologies to track temperatures and detect smoke particles at critical locations. The most common detection sensors for EFD biomass monitoring include: infrared cameras for quantitative and qualitative monitoring of hot spots; visible cameras for identification of smoke or flames; and aspirating smoke detectors for detecting smoke particles.

Proper selection and placement of sensors for biomass monitoring is essential to ensure optimal detection performance. For example, infrared cameras require a direct line of sight to the area of ​​interest to ensure detection. Critical areas hidden from the camera’s field of view could be monitored by smoke detectors, increasing the camera’s detection. For outdoor or high airflow installations, infrared sensors are best for detection, as dilution effects can limit the performance of smoke detectors.

Another critical consideration for biomass EFD is early warning notification to those responsible for materials handling. Prior to DFT, material handlers unknowingly spilled hot materials, increasing the risk of fire. With EFD and early warning notifications, informed heavy machinery operators can avoid trouble spots and prevent the spread of potential fire hazards.

It is important to note that IR camera IoT EFD systems do not replace existing detection and response protocols. Instead, the system operates as an early warning system, detecting areas of the facility where ignition may be occurring. New methods of detecting heat, smoke and fire are constantly evolving. Many new sensing devices include wireless capabilities that make EFD IoT integration a simple exercise. Beyond alarms and notifications, IoT EFD systems can provide automation commands such as initiating and directing an extinguishing system. Since EFD IoT systems leverage cloud computing, they require less hardware with reduced installation overhead. Available communication technology can be added to existing detectors, making it easy to upgrade existing systems. By warning earlier on the path to ignition, managers of facilities that store biomass can prevent costly and potentially deadly fires before they are allowed to ignite and spread.

Author: David C. Bursell
MoviTHERM, Vice President of Business Development
[email protected]


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