Machine Learning and Deep Learning for Fire Detection
By definition, a fire is a process in which substances combine chemically with oxygen from the air and typically give out bright light, heat, and smoke; combustion or burning. In the oil and gas industry, fire hazards are a big issue which is faced on a regular basis. In order for companies to understand how to reduce these fires as well as damage and injuries, it is important to know the difference between the different types of fires that can occur. This article is about understanding the types of fires, more specifically comparing the differences between a jet fire, pool fire, and flash fire. As well, we will look into how these fires can be prevented with the use of machine learning. In order to understand the differences, we first need to see what causes these fires, and what variables are the same throughout all three, and which create a change between them.
Jet fires are caused by high pressure releases of hydrocarbon, causing flames to shoot out in one direction, similarly as a flamethrower. In order for this fire to occur, there is also a need for oxygen which allows the fire to breathe. However, in order for the fire to start, there needs to be a source of ignition. There are many potential sources of ignition, such as a spark, heat from hot surfaces, or even the reflection from mobile or tablet devices. Another source could also be cigarettes, which is why they are forbidden even in gas stations. The image above represents a case of a jet fire, which we can identify for certain by the way the fire is shooting in one direction.
Pool fires on the other hand are the result of liquid hydrocarbon which also interacts with oxygen and a source of ignition. Since in this case the hydrocarbon is liquid, the fire is spread out along the liquid, rather than shooting out from a highly pressurized point, which can be seen in the image above, as they are not high in pressure and a lot more spread out. This type of fire can be caused when liquid hydrocarbon interacts with air, as well as any one source of ignition as mentioned previously. This in turn starts a flame that then spreads out throughout the liquid, as shown in the image.
Finally, a flash fire is caused when gas hydrocarbon slowly seeps out, until it catches on fire from a source of ignition, weather it is from a spark or high temperatures, it goes through the air until it consumes all the gas in the air. The image above represents a scenario like this, where the fire is fully spread out in the air, consuming the gasses all around.
Having an understanding of how the different fires happen allows them to optimize work, to fix the roots of the problems before they are able to happen. For jet fires, it is important that highly pressurized gases aren’t able to interact with oxygen as well as having a source of ignition. To prevent pool fires pipe leaks of liquid hydrocarbon must be sealed off in order for them not to catch on fire. As for flash fires, hydrocarbon gas shouldn’t be able to seep out from pipes as it can in an instant catch on fire, causing safety risks for workers. These steps are simple in theory, but in practice a lot harder considering how big the working facilities are, and how many pipes need to be maintained.
Luckily, as technology progresses rapidly, we have more equipment at our disposal, such as sensors and IoT devices. However, it is still hard to process the data which is given from them in order to prevent potential fires, which is why deep learning is used in order to create a predictive model that helps companies predict disasters ahead of time, allowing them to be proactive rather than reactive. This is done by converting information based on past results as well as real-time results, creating simple insights that allow users to detect defects which would have lead to a malfunction in the system.
Conclusion:
The challenge with this lies in the fact that the oil and gas industry is structured towards avoiding failure, meaning that the needed examples of failure patters can be hard to find, but not impossible. Still, what deep learning brings is an improved approach if done correctly that can prevent many catastrophic events both for the environment and for workers, and over time these methods will further improve, removing a lot more risks from the industry that is heavily connected to our everyday lives.
