A team of researchers at the Indian Institute of Technology (IIT) Madras is using artificial intelligence (AI) and machine learning (ML) to emulate processes of producing fuel from biomass. The researchers state that while fuel from biomass is deemed as an environment friendly solution over fossil fuels, creating real-world models to test their veracity is a time and resource intensive process – which is where AI and ML algorithms can chip in.
Himanshu Goyal, assistant professor at IIT Madras’ chemical engineering department, said, “Understanding the complex mechanisms involved in the conversion of raw biomass into fuel is important for designing the processes and optimising reactors for the purpose. There is an urgent need to train the next generation of engineers on high-performance computing and machine learning skills so that they can address some of the biggest challenges before us, such as developing zero-emission technologies to tackle climate change.”
Called a ‘Recurrent Neural Network’, the researchers have claimed in their study that the new AI model could decrease the cost of erstwhile existing computation models in biomass fuel engineering by four times. The study was published in the peer-reviewed journal ‘Reaction Chemistry and Engineering’, under the Royal Society of Chemistry.
While a working prototype model based on the recommendations of the neural network model showcased by the IIT researchers has not been revealed yet, the researchers have claimed that such a computation system could be used for a wider range of purposes. These include environmental engineering sectors such as carbon capture, as well as scaling electrification opportunities in the global chemical industry.
Fuel produced from biomass is looked upon as a potential alternative to fossil fuel, which alongside being a finite resource, is also a highly polluting one. In a bid to reduce their impact on the environment, global corporations and institutes have been exploring cleaner energy channels – but biomass has so far failed to offer an economically scalable alternative to fossil fuels.