FIRE & EXPLOSION

IMPROVING EARLY WARNING FIRE DETECTION: THERMAL IMAGING AND THE INTERNET OF THINGS

BY DAVID BURSELL

Biomass is commonly stored in bulk outdoor piles near the power generation facility. These mounds of material are especially prone to self-heating as they naturally decompose. The decomposition process is accelerated as moisture is introduced from rain and humidity, generating even more heat. As most biomass materials are good insulators, the internal pile heat generated is not allowed to escape and cool, thus increasing temperatures and spreading to a larger internal area. Eventually, the material begins to smolder. Smoldering and flameless fires are more easily ignited than flaming fires and more challenging to extinguish.

Early detection is critical if a bulk biomass pile fire is to be avoided. Unfortunately, detecting early stage fire formation within a bulk pile fire is difficult. For example, a pile’s surface temperature may be ambient while the internal temperature could be more than 200 degrees Celcius. Traditional methods using linear heat detection 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. Monitoring the temperature trends over time is generally more helpful in detecting the early onset of heating, when mitigation measures can be deployed before the situation becomes dangerous.

Infrared (IR) Cameras for Early Fire Detection

IR cameras operate on the heat transfer principle of radiation. The infrared camera has a focal plane array of detector elements that sense infrared light from object surfaces. The radiation captured by the infrared cam-

Biomass pile thermal image with area regions of interest

IMAGE: MoviTHERM

era detector is digitized, converted to data and displayed as a viewable image. Calibrated IR cameras can report temperature measurements from specific spots, lines and areas on live or recorded images.

IR cameras are the first to alert before a fire develops. They “see” a warming up of material early in the fire development process before forming smoke particles or flames. These warming materials appear as hot spots in a thermal image and are quantified with regions of interest (ROIs) like spots, 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 the threshold condition of an ROI is satisfied, alarms trigger notifications to the 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 deliver valuable insights into a system or process. IoT revolutionizes automation by using cloud computing to simplify integration and enhance process control.

Thermal Imaging and IoT Early Fire Detection

Fire safety for biomass storage is an area that realizes the benefits of thermal imaging when combined with IoT. By connecting infrared cameras that alert at the earliest stages of development, potential fires can more

readily be detected and prevented. Safety alerts are sent to hundreds of people quickly and effectively with IoT. Communication options include voice calls, texts and emails to targeted recipients to establish quick and effective awareness. Another advantage is scalability. Facility managers can connect multiple locations to a central monitoring and alarming dashboard. Understanding the situation at all facilities improves the oversight and management of multiple systems from a single control point.

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

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 facility computer server. Any potential for operating system software conflicts 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 appropriate credentials, control and alarm settings can be modified remotely to optimize performance.

Another key advantage to 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 for scene assessment before arriving on-site, saving time and optimizing safety. These maps identify the alarm sensor location, monitored area, alarm conditions, facility entry and exit points.

IR Camera IoT Early Fire Detection

IR Camera IoT EFD systems for biomass monitoring can integrate multiple detection technologies to track temperatures and detect smoke particles at critical loca-

A sample map view display from an IoT early fire detection application

PHOTO: MoviTHERM

tions. The most common detection sensors for biomass monitoring EFD include: infrared cameras for quantitative and qualitative monitoring of hot spots; visible cameras for identification of smoke or flame; and aspirating smoke detectors for detection of smoke particles.

Correct sensor selection and placement for biomass monitoring are critical to ensure optimum detection performance. For example, infrared cameras require a direct line of sight to the area of interest to provide detection. Critical areas obscured from the camera’s field of view could be monitored by smoke detectors, thereby augmenting the camera’s detection. For outdoor or high airflow installations, infrared sensors are best for detection, as dilution effects may limit the performance of smoke detectors.

Another critical consideration for biomass EFD is the early warning notification to individuals responsible for material handling. Before EFD, material handlers unknowingly spread hot materials, increasing the size of the fire hazard. With EFD and early alert notifications, informed heavy machine operators can avoid problem spots and prevent spreading potential fire hazards. Conclusion

It is important to note that IR camera IoT EFD systems do not replace existing detection and response protocols. Instead, the system functions as an early warning system, detecting areas in the facility where ignition may occur. New detection methods for heat, smoke and fire are continually developing. Many new detection devices include wireless capabilities that make integrating IoT EFD a straightforward exercise. Beyond alarms and notifications, IoT EFD systems can provide automation controls like initiating and directing an extinguishing system. Because IoT EFD systems leverage cloud computing, they require less hardware with a reduced installation burden. Available communication technology can be added to existing detectors, making retrofitting existing systems easy. By warning earlier on the pathway to ignition, managers of facilities that store biomass can avert costly and potentially life-threatening fires before they are permitted to start and spread.

Author: David C. Bursell MoviTHERM, Vice President of Business Development www.movitherm.com info@movitherm.com