Researchers from the Indian Institute of Technology in Bombay (IIT-Bombay) have developed a new data-processing technique to measure low amounts of soot accurately. This will help designers build better combustion-based devices such as internal combustion engines in cars.
Soot is tiny black particles that rise from a flame. Soot is formed when the fuel does not burn entirely. When fuel burns properly, a blue flame is emitted, whereas the flame is yellow when the soot is formed during burning and it becomes hot. Soot can cause cancer and respiratory and cardiac disorders and can also reduce the life of machine parts, a news report has explained.
Accurately measuring small amounts of soot can be a challenge and has spawned several research projects. The team from IIT-Bombay demonstrated a new technique to effectively reduce measurement errors when soot is present in low amounts. They analysed digital camera pictures of burning fuel to guess the temperature of the fuel and use the information to estimate the soot volume. The amount of soot can be measured using methods such as collecting and weighing the soot and studying a light beam shone on soot particles. The current study uses the last method. The researchers passed a beam of red laser light of a specific frequency, through a droplet of burning fuel and took images as it burnt. The light falling on the camera also contains the light from the burning fuel. The researchers used a narrow band filter to let only the laser light pass and filter out the light emitted by the burning fuel.
The report noted that when a flame having soot particles is shone with light, called background light, the particles absorb and scatter some of this light, so light reaching the camera is less bright. The researchers used the relation between the initial brightness of the laser light, the brightness of the light falling on the camera, and the soot volume to calculate the amount of soot. They then used a data-processing technique to compute the values of brightness from their images. Their challenge was to estimate the initial brightness of background light falling on soot particles since this isn’t directly captured in the images.
The team predicted the brightness of background light at every moment instead of using an average. They observed the flickers in background light at areas present outside the flame of the burning fuel, where there is no soot. They used it to estimate the background light falling on the soot particles. Using the new data processing technique, the team got lower errors, especially when the amount of soot produced is low. The technique does not require any additional equipment or extra expenditure, an added advantage.
The report added that to further reduce errors in the experiment, the researchers passed the laser light beam through a fixed and a rotating diffuser — a glass sheet that scatters light — before the light was incident on the burning fuel. A diffuser gives an evenly bright light and avoids the many speckles in the camera image. Speckles need to be removed while processing the data, leading to a loss of information. The researchers also validated their data processing technique. They used it to calculate the amount of soot for some previous measurements reported in the literature and verified the results. They also qualitatively checked their experimental observations.
They burnt a droplet of toluene (a carbon-based fuel) and compared their experimental observations with that in the literature. The team observed a similar peak value of the amount of soot. As expected, they saw high amounts of soot slightly inside the outer edges of the flame, where temperatures and fuel concentration are high, a researcher explained. The quantification of soot is crucial from an environmental perspective. This is an effective method to quantify soot to help identify strategies to mitigate combustion-based practices in India.
