Inspired by biology
Figure 1: Looking back to the 1940s, computer researchers were inspired by the human brain when they developed the first conceptual model of an artificial neural network.
Looking back to the 1940s, computer researchers were inspired by the human brain (Figure 1) when they developed the first conceptual model of an artificial neural network. Their work focused on solving certain kinds of problems that are easy for humans, but difficult for machines or computers—otherwise known as pattern recognition.
Applying NNT to safety monitoring
Fast forward to the present, and you’ll find there are now a variety of applications of neural networks, some of which are at work in the newest generation of gas and flame detectors. With the application of neural network technology (NNT), which is based on ANN, these detectors operate, in essence, with an artificial intelligence capability.
The breakthrough advantage with NNT-enabled gas and flame detectors is their ability to learn. They learn through a type of apperceptive process, which means the comprehension or assimilation of something such as a new idea can then be related in terms of previous experiences or perceptions. NNT-enabled devices operate similarly, and are much like a human mind in the way that it enables a person to recognize a face from the distant past. The brain, for example, facilitates recognition by matching a face with an image stored as a memory.
Just like the human brain, NNT-based gas and flame detectors each have thousands of pieces of data stored in their memories from hundreds of gas leak, non-gas leak, flame and non-flame events that have been observed in the past. Such detectors have been trained through NNT intelligence to recognize an actual gas leak or flame based on that data, and they make decisions about whether they're detecting an actual gas leak or flame, even if they haven't seen that exact pattern in the past.
Multi-sensory layered safety monitoring
Taking NNT a step further, because no single sensing technology can detect every gas threat or every flame hazard, there is a new strategy emerging in safety monitoring. What if you combined multiple gas and flame detection technologies together, and then layered them where they fit best in terms of their reliability in each unique plant layout?
Gas and flame detection sensing technologies, if you think about it, all mimic the senses of the people who invented them. Catalytic bead detectors “sniff” gases, infrared and optical type sensors “see” gases and flames, and ultrasonic sensors “hear” gases. What if some of these detectors behaved more like people—reacting based on their intelligence (NNT enablement) and retained past memories?
Layering sensor technologies throughout the plant where they fit best in terms of their reliability achieves a human sensory chain of plant defense against hazardous gases and flames (Figure 2). This human sensory model builds a chain of defense to protect industrial plants, helping process and plant engineers add layers of protection that increase overall system protection and reliability, which includes avoiding false alarms.