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An automated water dispensing system for controlling fires in coal yards

Abstract

In spite of recent moves to wean the world of fossil fuels, coal remains the main source of power in many countries. Coal yards are prone to spontaneous ignition, a problem faced in every country that stores or transports coal. Depending on the environment-temperature, ventilation, and the rank of the coal-heating and self-ignition can be a longer or shorter process, but the possibility can never be entirely dismissed. A plethora of studies have modelled this oxidation behavior and proposed countermeasures. Most often, human intervention is necessary, which is both slow and dangerous for the firefighters involved. In this study, we propose to build a complete firefighting solution which is mounted on a number of towers sufficient to cover the area of an open coal yard, complete with redundancy. Each tower includes an inexpensive infrared detector, a water dispenser and a controller programmed to identify areas of elevated temperature, and actuate the dispenser. The heat direction algorithm calculates the parameters to position the water dispenser so that it covers the area. A prototype has been built from inexpensive components to demonstrate the effectiveness at detecting and extinguishing arising fires, and a solution has been costed for the coal yard in the case study. This work has been conducted in collaboration with the managers of the coal yard of a power plant.

Introduction

Coal is a leading energy source among non- renewables which can be burnt for electricity or heat. About two-thirds of the coal mined today is burnt in power stations. Coal is first milled to a fine powder, which increases the surface area and allows it to burn more quickly. In these pulverized coal combustion (PCC) systems, the powdered coal is blown into the combustion chamber of a boiler where it is burnt to drive a steam turbine. Pricing and a deliberate reduction of Sulphur content have led to a shift from traditional bituminous coal to subbituminous coal, which is of a lower rank than traditional coal. The lower the rank, the higher the risk of oxidation, as the coal contains more moisture and higher amounts of volatile matter (Sipil et al. 2012). Oxidation generates heat, and if the heat does not dissipate fast enough, self-ignition occurs. Therefore, ventilation and ambient temperature play an important role in the self-ignition of coal. Ventilation provides oxygen but also dissipates heat. The most dangerous conditions arise under high ambient temperatures and low continuous air flow.

Coal is exposed to spontaneous combustion in mines, storage and during transport. The spontaneous ignition is a constant danger in every country that runs coal facilities, from South Africa (Pone et al. 2007) to the US, many European countries, India and China (Wang et al. 2016). In Australia, the Hazelwood open cut mine was ablaze for weeks in February 2014, incinerating 45 houses and necessitating the evacuation of the town of Morwell in Victoria (Lord 2014).

To monitor the coal facilities for developing fires, most often heat sensing is applied, alternatively the levels of combustion-related gases like CO2 levels are monitored (Sipil et al. 2012). Fire prevention relies on the reduction of available oxygen, targeted ventilation for heat reduction or special measures such as the application of gels. Water, foam or nitrogen (in closed, airtight facilities) are used to extinguish fires (Rosner et al. 2011).

Coal yards or open cut mines allow a good visual overview of large areas, which opens up the opportunity for video surveillance. Although UAV1-based infrared sensing has been proposed (Cao et al. 2016) in combination with intelligent algorithms that identify the location of a potential fire, the method does not provide an automated response.

In this study, infrared sensors are used to monitor the heat emanating from the coal storage. The location of the heat source is determined and water jets are pointed at it using actuators. This avoids the need for human firefighting, which is obviously a hazardous task close to a burning coal fire. It is also slow, because the responder first has to reach the fire. In essence, the contribution of this paper is a complete solution for the cost-effective control of random fires on coal surfaces as they are found in coal yards, open containers and open-cut mines.

Background

Coal fires are constant hazards in coal production and consumption. The extent of the problem in China, the largest producer and user of coal, has been documented by Song and Kuenzer (2014). They also provide a comprehensive overview of detection and firefighting methods applied in the context of coal fires. Numerous detection methods have been applied underground, on ground level, from the air and even from space. Many of these approaches are suitable for large areas, such as Kuenzer et al. (2007). Thermal imaging from UAVs has been proposed for landscapes difficult to access (Vasterling et al. 2013). A theoretical model to predict the most susceptible areas has been presented (Akgun et al. 2001). The electrical potential, arising from oxidation and/or the change in temperature, have also been explored as indicators (Shao et al. 2014; Li et al. 2012), as have electromagnetic (Shao et al. 2013) and electrical (Xiong et al. 2013) imaging, as well as ground-penetrating radar (Gundelach 2010).

Clearly, most of these approaches focus on natural deposits of coal that may catch fire in areas that are often remote. Underground coal seam fires and their prevention have been the topic of many studies (Kuenzer et al. 2012). As the conditions are quite different, these methods are generally not applicable to coal yards. A few articles investigate combustion suppression in coal storage facilities.

Kim and Sohn (2012) modelled the temperature and ignition behavior of a – roughly pyramid-shaped – heap of coal and verified it against a coal storage facility in Korea. According to their observations, heat travels from the aerated sides of the pile towards the middle over a period of 100 days. The authors experimented with the installation of a 2 m high wall 5 m from the edge of the pyramid to separate the coal on the sides into compartments. This was shown to delay ignition by about 15 days. Ventilation of the pile through holes in the ground below was also investigated, and found to have a detrimental effect at flow rates below 100 L/s. A dual wind barrier produced the best effect, delaying the ignition by 28 days given a wind speed of 10 m/s.

Using computational fluid dynamics, Taraba et al. (2014) investigated the effect of wind on the heating of coal, finding that the heat shifts toward the centre of the pile with increasing wind speeds. In similar work, Zhu et al. (2013) simulated fluid dynamics for bituminous coal, concluding that an oxygen concentration of 5% and sufficient wind velocity can balance the heat in a pile. A considerable number of studies that model the ignition behavior exists (Song et al. 2014; Lin et al. 2017; Zhang et al. 2016; Kim et al. 2015). A few laboratory-based experimental studies have also been published (Lu 2017; Deng et al. 2018).

In addition to the mitigation strategies discussed by Kim and Sohn (Kim et al. 2012), nitrogen has been used to extinguish fires in closed spaces (Sipil et al. 2012), tight coal layering is often practiced (Rosner et al. 2011) and gel is sometimes spread on the surface to prevent exposure to oxygen (Rosner et al. 2011). Water from fire hoses is recommended as an easy solution, but only has effect on the top layers (Sipil et al. 2012).

Fig. 1
figure 1

Surface of coal yard in Norocholai

The use of sensors in firefighting is not new. Commonly, the area to monitor is too large to cover with sprinklers, and sensors are used to inform the firebrigade (Jiang et al. 2004; Hong et al. 2018). Recently, Eltom et al. (2018) introduced an IoT solution for home fire protection, where the detector primarily alerts the owner while shutting down power. It can also be programmed to actuate a Sprinkler system. A great number of studies, e.g. (Madhevane et al. 2017; Rakib et al. 2016) propose robotic firefighting systems. In practice, these face numerous challenges, which make them costly and limit their use (Amano 2002).

Based on the alternatives available, in this project, the following solutions were considered for implementation:

  1. (1)

    Converting the storage location to a more controlled environment with less oxygen.

  2. (2)

    Importing coal of a higher grade which is less likely to ignite.

  3. (3)

    Deploying fire-fighting robots on the yard.

  4. (4)

    Developing a solution of moving water containers suspended on a support grid.

  5. (5)

    Using a fire detection system that controls sprinklers.

Solutions 1 and 2 were dismissed as too expensive. Solution 3 was not pursued due to the problems an uneven ground would pose for robots. As our case study example in Fig. 1 shows, coal yards are not flat, but contain heaps as well as depressions where coal has been extracted to feed the power plant. Solution 4 faces similar problems. The metal grid envisaged as a rail structure to move along may bend under the weight of the container unless supported at short intervals. In addition, the container may take a long time to traverse its designated area, and fires may have time to take hold in the meantime, if they ignite at the ‘wrong time and place’. Given this topology of an open coal yard with limited depth of coal, it was decided to explore a solution based on Solution 5. Several visits were made to the coal yard and the solution was discussed with its representatives.

Pole-mounted firefighting system

Spontaneous ignition can lead to fires burning out of control, endangering human life, incurring financial losses and releasing harmful gases into the atmosphere. Fighting fires before or immediately after they occur is a pressing priority. Water sprinklers are inexpensive, environmentally friendly and effective. Infrared heat sensing is inexpensive and reliable. For a fast and reliable response, each sensor is equipped with an actuator that can point a water valve to the best location. A water pump delivers the flow needed to extinguish or prevent the fire at the position from which the heat emanates.

Fig. 2
figure 2

Pole design used in the solution

A single system consisting of controller, sprinkler, detector and the pertinent actuator cannot cover an entire coal yard. Therefore, our solution is to cover the coal storage in a grid of individual firefighting as shown in Fig. 2, which also provides overlap between units and therefore a redundancy that increases reliability. The following sections explain the system in detail.

Detection

For the fire detection, a number of sensor options have been developed for home and industrial use. One of the most prevalent solutions is smoke detection, which can use either the ionisation or photoelectric methods. Both rely on chambers where smoke concentrates, by detecting the changes in either the lighting or the ionisation of the particles in the chamber. As such, they are badly suited for open, ventilated spaces. Similarly, RGB images have difficulty capturing smoke that dissipates fast in open areas. Heat detection is problematic in warm countries, and the problem is exacerbated by the natural variations in temperature across the coal yard. Fires can also start as smoulders, creating little heat until the fire intensifies. Temperature sensors have to be installed at a distance from the surface, which introduces further delays in detection. In contrast, Infrared (IR) sensors can detect heat reliably from a distance. They are also inexpensive; as normal webcams can be used after removing the IR filter. While industrial infrared cameras typically achieve a ± 2 °C accuracy, the inexpensive modified camera trialled in the prototype has an accuracy of around ± 10 °C.

With the IR filter removed, a video of the coal sur face was recorded by a camera overseeing part of the area. The frame rate was set based on the time it takes to process each image and initiate appropriate action: Sufficient time has to be allowed between two frames to allow the processing of the image to detect a fire, determine the coordinates in case of a fire and communicate with the actuator that triggers a response.

Given a single frame, the controller interprets the brightness of the pixels and identifies potential areas of heat, based on a threshold provided. The image processing uses the OpenCV2 library.

Each image is processed by initially removing noise and areas not lit brightly. This means after this step, only self-ignitions are visible on the image. Then these self-ignition regions are converted to contours and their areas are calculated. In the next step, the contour with the largest area is determined, as this identifies the area where the self-ignition event is spreading the most at that moment. If there are several self-ignition events on the same frame, priority is given to this location. The coordinates of the centre point of this contour are calculated and used to determine the angle for the water jet, as well as the voltage needed to create the water spray.

The routine ends when the controller calls other modules to trigger the water jet as a response.

Water expulsion

To adjust the water dispensing valve, servo motors were selected which rotate the nozzle to the required coordinate. Servo motors do not require a motor driver and can be connected directly to a power supply, and they provide precise angle control. They also have a good stall torque, meaning the position of the motor arm is sufficiently stable against external torques applied on the motor arm. This will ensure, after rotating the motor arm to calculated position, that the arm does not move due to weight of the water hose, or due to the jerks induced from rapid flow rate variations of the water jet.

Prototype

Arranging a coal fire in a controlled environment is both difficult and dangerous. The timing of ignitions cannot be predicted with reasonable accuracy, while leaving the premises unattended after hours causes an unacceptable fire hazard. Coal hotspots have temperatures of 100–800 °C. At Norocholai, industrial thermal cameras would be used such as FLIR A310 or ThermoView TV40. Candles burn at 1200 °C.

A simple webcam was used for the IR imaging, with the IR filter removed. Given its lower sensitivity, the combination with a candle was deemed an appropriate setup to represent a more challenging environment captured by professional equipment.

The camera was mounted on a movable support as shown in Fig. 3, because the camera orientation may likely have to be adjusted once the system is installed in the coal yard. Motors were mounted on a square base initially as shown in Fig. 3a, but later used a circular shape as shown in Fig. 3b to ensure smooth movement of the water hose.

The MG996R servo motor was chosen due to its accurate position control. Mounting two motors on top of each other, it is possible to cover a 360° area using 180° rotation servo motors. The lower servo motor rotates 180°, while the upper servo motor is attached to an arm which carries it up to a further 180° as the lower servo rotates. For each position of lower servo between 0° and 180°, the upper motor is capable of covering series of points which lies on a straight line going through two quadrature making it possible to shoot water at any point that lies on any quadrature within the field of view of the camera.

For the water pump, the Anself Ultra-Quiet Mini DC 12 V model was chosen, an IP68 grade fully submersible pump with 30,000 h of working life with a flow rate of 300 L/h and a maximum head of 4.5 m, meaning it can lift the water up to this height.

A circuit board had to be created to connect all com-ponents, which were powered through regulators to ensure a safe environment. Also, a 5 V/12 V relay was used to switch the water pump following a signal from the Arduino. Figure 4 shows the completed electronics.

Fig. 3
figure 3

Details of the prototype built, with camera on top of the pole and water dispenser at the lower level

Fig. 4
figure 4

Electronics of the prototype showing connections between microcontroller, relay module and voltage regulators

The Arduino microcontroller provides an API for the rotation of servo motors, however without the option of setting a rotation speed. Therefore, a separate function had to be written to turn the motor smoothly and slowly to the required position.

Detecting the depth of the hotspot is not critical in this solution, and instead water jet will be directed to the identified point on surface. Water can then sink through the layers to reach the hotspot. As stated by Lu (2017), we also observed that heat from the hotspot leaks to the surface, which enables us to capture the exact location using infrared imaging.

Case study

Coal yard site

The only coal yard currently in operation in Sri Lanka services the Lakvijaya Power Station, situated in Norocholai, Puttalam, on the southern end of the Kalpitiya Peninsula. Due to its location, the plant is also known as the Norocholai Power Station. The 450 m × 150 m yard is supplied twice a year by ships which moor at a distance and unload to boats which land at a purpose-built jetty. Three conveyor belts distribute the coal across the yard, from where bulldozers push it onto underground conveyor belts that transport it to the power plant. The conveyor belts are equipped with sprinklers to prevent ignition in transit. About a quarter of the coal stored is under cover to keep it dry. The ambient temperature of a tropical region, the internal energy of the coal and the coastal wind providing ample oxygen are all factors that increase the combustion hazard. In addition, Puttalam is one of the hottest regions in Sri Lanka. Areas of oxidation are invariably going to emerge over time, and these tend to build heat over several days until self-ignition. To alleviate this risk, three measures have been put in place at the plant:

  1. (1)

    Sprinklers over the conveyor belts ensure the coal is moist on arrival.

  2. (2)

    Wind barriers were installed on the shore side.

  3. (3)

    Bulldozers compress the coal in the vicinity of fires. This poses a hazard to the drivers of the bull-dozers.

Although the yard is the only coal storage in Sri Lanka at present, increasing power demands and limited availability of hydro power may lead to the introduction of new plants in the future.

Installation at the coal yard

For the purpose of the installation of the towers equipped with detectors and water jets as trialled in the prototype, the area of the coal yard at Norochcholai Lakvijaya Power plant is subdivided as shown in Fig. 5. Each camera and water jet cover a rectangular shape having with a width of 70 m and length of 90 m, which leads to 20 towers placed at the blue dots. The equipment of each tower cannot reach the very base of the tower. Therefore, a second set of towers are placed at the locations marked by red dots, where they can also serve as backup for the region between the first set of towers. This means that the area covered by yellow rectangles is monitored by two towers. This provides robustness through redundancy: If one tower becomes inactive, a backup tower can still fight an emerging fire. The area outside the yellow rectangle is monitored only by one tower. This region is at the boundary of the coal yard and automatically monitored by personnel moving around the area performing tasks. Therefore, the absence of dual coverage in this area is not considered as dangerous.

The height of the poles needed to cover the rectangles was calculated based on the movements of the cameras that can cover a 120° angle. As the pole is in the middle of the area, the longest distance has to fit into a 60° angle. The longest distance the camera has to cover is 57.008 m, which is the diagonal of each of the four quadrants (’sub-rectangles’) of the tower’s rectangle, whose sides are 70 m × 90 m. Each of the four right triangles measures 45 m × 35 m, leading to a diagonal length of 57 m. Hence, we obtain the height h = 32.9 m of the pole by divided by tan 60° and determine the pole height as 33 m.

Given the layout of the poles shown in blue in Fig. 5, the design requires 20 base towers. A second set of towers is added to provide overlap between the areas covered. The additional 12 towers are shown in red in Fig. 5, adding to 32 towers in total to cover the entire yard.

Fig. 5
figure 5

Pole coverage of Lakvijaya coal yard. The blue dots are the towers to cover each of the 20 rectangles. The red dots show the location of supplementary towers which cover the bases of the first set of towers

Cost

The components used for the prototype were chosen to be sufficiently robust, yet very inexpensive (Table 1).

Table 1 Cost of prototype

Even though we did not quote the lowest prices avail- able, all required items cost $20 or less each, except the Arduino controller. Therefore, at a conservative estimate, the prototype is worth around $110 in total.

Table 2 Cost of individual tower

The installation at the coal yard would use a FLIR thermal imaging camera, two industrial servos and a variable frequency drive APM series water pump. All items, including the poles, are costed in Table 2.

The coal yard at Novocholai takes 32 towers to cover the yard. Table 3 details the costs of the towers, the water and electricity lines to install between them, and a server grade computer for running the algorithm centrally. The PLC controller includes the cost of the required power supplies, cables, and software.

Table 3 Total cost of the project

Discussions

Our algorithm demonstrated reliable performance after making a few minor changes to the prototype, such as changing the shape of the base on which the motor is mounted, providing sufficient space for the water hose, and smoothing the servo turning movement. Tests were conducted by simulating the fires in all quadrants, and also at varying distances from the pole. Also, several hotspots were simulated simultaneously, and the ability of the system to extinguish all of these within a short time was verified. Six hotspots were created per test, and 50 testing cycles were conducted. The system only failed to extinguish a flame at the first attempt in 6 cases. The average time taken for a single test was varied between 3 and 4 min depending on the locations of the simulated hotspots.

Figure 6 illustrates how one candle (of four burning) is extinguished. In Fig. 6a, the jet is still on the right of the flame, about to be turned into it. Figure 6b shows the jet streaming at the high end of the flame. Figure 6c and d see the jet reduce pressure to move lower towards the wick, which it hits in Fig. 6e. In Fig. 6f, the flame has been extinguished.

Fig. 6
figure 6

Test that demonstrates how the prototype extinguishes a single flame the detector has identified as a heat source

Thereafter, Norocholai coal yard was revisited to study further optimization possibilities to our algorithm. A set of water sprinklers are already available on the coal yard to shoot water manually towards the hotspots by a dedicated coal yard monitoring team. During our study, the temperature was measured on several hotspots while water shooting continued. As our tests showed, and according to the experience of the coal yard monitoring team, water has to be streamed onto a hotspot for around 15 min to fully neutralize it. However, after about 3–4 min of continuous water spray, the surface temperature of the hotspot decreased to the ambient temperature, meaning that our system will no longer shoot water towards it since it will detect no thermal image. For this reason, we could conclude that water shooting duration of our code has to be increased when it is applying to the real ground allowing water to sink through coal layers up to the hotspot location.

The possibility of establishing a precise coordinate system on the actual coal surface was also studied. Due to the unevenness of the coal surface as well as the altitude variations when the total area (126,000 square meters) of the coal yard is considered, we concluded that initially planned point to point water expulsion might not work since it may require considerable precision in the coordination of camera and motors as well as the construction of the support pole. Eliminating these errors is time consuming and error-prone, as unexpected shifts in the final installations could potentially compromise the effectiveness of the system. Therefore, our algorithm was changed to instruct the two servo motors to move in circles with increasing radius around the coordinates of the centre of the detected ignition area. Initially, the jet is pointed at the centre of the area. Spraying water around the hotspot, has other benefits: It increases the chances for sinking water to reach a hotspot located beneath the coal surface.

Conclusions

A cost-effective and environmentally friendly solution has been explored in the current work for controlling spontaneous fires on coal yards. The IR heat sensing solution does not require a dangerous proximity to potential fires, and the automated actuation of the response is both quick and safe for humans.

The prototype was tested under more difficult conditions than the actual ignition areas pose, as candles are very small and require higher precision than larger areas of coal. The water jet will rarely miss such an area entirely, rather skip small spots which may be no more than a few centimetres in diameter. These spots will be picked up by the analysis of the next frame, and also by the detector of the additional tower.

Considering the experiments conducted with the system prototype, and also based on the tests and observations made on actual field, it can be concluded that an automated water jet system based on IR range hotspot identification is a better method for random fire controlling on coal yards.

One of the remaining tasks is to correct for the effect gravity has on the impact area of the water jet. The ac- curacy decreases with increasing distance from the pole. As a quick fix, the water pressure was increased to counteract the gravity. A mathematical solution that models the change of impact location as a dependency of the amount of water and the distance from the dispenser is planned as further work.

References

  • Akgun F, Essenhigh R (2001) Self-ignition characteristics of coal stockpiles: theoretical prediction from a two-dimensional unsteady-state model. Fuel 80(3):409–415

    Article  Google Scholar 

  • Amano H (2002) Present status and problems of fire fighting robots, in: Proceedings of the 41st SICE Annual Conference. SICE 2002, IEEE, pp 880–885

  • Cao X, Ren X, Jiang J, Intelligence A (2016) (URAI), IEEE, 2016, 288–292

  • Deng J, Ren LF, Ma L, Lei CK, Wei GM, Wang WF (2018) Effect of oxygen concentration on low-temperature exothermic oxidation of pulverized coal. Thermochimica acta 667:102–110

    Article  Google Scholar 

  • Eltom RH, Hamood EA, Mohammed AA, Osman AA, Engineering E (2018) (ICCCEEE), IEEE, 2018, 1–7

  • Hong SG, Son KH, Lee H, Bae M, Lee KB (2018) Augmented iot service architecture assisting safe firefighting operation, 2018 Global Internet of Things Summit (GIoTS), IEEE, 2018, 1–6

  • Jiang X, Chen NY, Hong JI, Wang K, Takayama L, Landay JA (2004) Siren: Context-aware computing for firefighting. International Conference on Pervasive Computing, Springer, 2004, 87–105

  • Kim CJ, Sohn CH (2012) A novel method to suppress spontaneous ignition of coal stockpiles in a coal storage yard. Fuel Process Technol 100:73–83

    Article  Google Scholar 

  • Kim J, Lee Y, Ryu C, Park HY, Lim H (2015) Low-temperature reactivity of coals for evaluation of spontaneous combustion propensity. Korean J Chem Eng 32(7):1297–1304

    Article  Google Scholar 

  • Kuenzer C, Zhang J, Hirner A, Jia Y, Sun Y (2007) Multi-temporal insitu mapping of the Wuda coal fires from 2000 to 2005: Assessing coal fire dynamics, ERSEC Ecological Book Series 4

  • Kuenzer C, Stracher GB (2012) Geomorphology of coal seam fires. Geomorphology 138(1):209–222

    Article  Google Scholar 

  • Gundelach V (2010) Contributions to the exploration of coal seam fires in china with ground penetrating radar, Latest Developments in Coal Fire Research—Bridging the Science, Economics, and Politics of a Global Disaster, Berlin. 93–98

  • Li X, Li X, Xu G (2012) The anomaly investigation of self-potential method for coal fire area survey. Geophys Geochem Explor 36(3):382–385

    MathSciNet  Google Scholar 

  • Lin Q, Wang S, Liang Y, Song S, Ren T (2017) Analytical predic- tion of coal spontaneous combustion tendency: Velocity range with high possibility of self-ignition. Fuel Process Technol 159:38–47

    Article  Google Scholar 

  • Lord K (2014) As it happened: The Hazelwood mine fire, ABC News. https://www.abc.net.au/news/2014-09-02/hazelwood-coal-mine-fire-morwell/5711564

  • Lu Y (2017) Laboratory study on the rising temperature of spontaneous combustion in coal stockpiles and a paste foam sup- pression technique. Energy Fuels 31(7):7290–7298

    Article  Google Scholar 

  • Madhevan B, Sakkaravarthi R, Singh GM, Diya R, Jha DK (2017) Modelling, simulation and mechatronics design of a wire- less automatic fire fighting surveillance robot. Def Sci J 67(5):572

    Article  Google Scholar 

  • Pone JDN, Hein KA, Stracher GB, Annegarn HJ, Finkleman RB, Blake DR, McCormack JK, Schroeder P (2007) The spontaneous combustion of coal and its by-products in the wit- bank and sasolburg coalfields of South Africa. Int J Coal Geol 72(2):124–140

    Article  Google Scholar 

  • Rakib T, Sarkar MR (2016) Design and fabrication of an autonomous fire fighting robot with multisensor fire detection using pid controller, 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), IEEE, 2016, 909–914

  • Rosner C, Roepell H (2011) Experiences with fires in silos for coal storage in the tiefstack chp; erfahrungen mit kohlesilobraenden im heizkraftwerk tiefstack,VGB PowerTech91

  • Sipil J, Auerkari P, Heikkila¨ AM, Tuominen R, Vela I, Itkonen J, Rinne M, Aaltonen K (2012) Risk and mitigation of self- heating and spontaneous combustion in underground coal storage. J Loss Prev Process Ind 25(3):617–622

    Article  Google Scholar 

  • Shao Z, Wang D, Wang Y (2013) Simulation of high-density electrical method in detecting coal fires and its application, Journal of Mining & Safety Engineering 3

  • Shao Z, Wang D, Wang Y, Zhong X (2014) Theory and application of magnetic and self-potential methods in the detection of the heshituoluogai coal fire, China. J Appl Geophys 104:64–74

    Article  Google Scholar 

  • Song Z, K u¨nzer C (2014) Coal fires in china over the last decade: a comprehensive review. Int J Coal Geol 133:72–99

    Article  Google Scholar 

  • Song Z, Zhu H, Jia G, He C (2014) Comprehensive evaluation on self-ignition risks of coal stockpiles using fuzzy ahp approaches. J Loss Prev Process Ind 32:78–94

    Article  Google Scholar 

  • Taraba B, Michalec Z, MichalcovaV, Blejchar T, Bojko M, Kozubkova M (2014) Cfd simulations of the effect of wind on the spontaneous heating of coal stockpiles. Fuel 118:107–112

    Article  Google Scholar 

  • Vasterling M, Meyer U (2013) Challenges and opportunities for uav- borne thermal imaging. in: Thermal Infrared Remote Sensing, pp 69–92

  • Xiong S, Yu C (2013) Characteristics and mechanisms of rock magnetic increasing in underground coal spontaneous combustion area-take wuda coal mine of inner Mongolia and ruiqigou coal mine in Ningxia as examples. Chin J Geophys 56:2828–2836

    Google Scholar 

  • Wang S, Wang X, Li D (2016) Mining-induced void distribution and application in the hydro-thermal investigation and control of an underground coal fire: A case study. Process Saf Environ Prot 102:734–756

    Article  Google Scholar 

  • Zhang J, Liang Y, Ren T, Wang Z, Wang G (2016) Transient cfd modelling of low-temperature spontaneous heating behaviour in multiple coal stockpiles with wind forced convection. Fuel Process Technol 149:55–74

    Article  Google Scholar 

  • Zhu H, Song Z, Tan B, Hao Y (2013) Numerical investigation and theoretical prediction of self-ignition characteristics of coarse coal stockpiles. J Loss Prev Process Ind 26(1):236–244

    Article  Google Scholar 

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Jayasuriya, J., Moser, I. & de Mel, R. An automated water dispensing system for controlling fires in coal yards. Int J Coal Sci Technol 9, 23 (2022). https://doi.org/10.1007/s40789-022-00488-y

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Keywords

  • Coal yard fire
  • Fire detection
  • Internet of things
  • Spontaneous combustion detection
  • Infrared heat detection