IIT-M team uses AI to study production of fuel from biomass

Researchers at the Indian Institute of Technology (IIT) Madras are using Artificial Intelligence tools to study the processes involved in conversion of biomass to gaseous fuel.

With increasing environmental concerns associated with petroleum-derived fuels, biomass is a practical solution, not in the conventional sense of directly burning wood, cow dung cakes, and coal, but as a source of energy-dense fuel.

While models are being developed all over the world to understand the conversion of biomass into fuels and chemicals, most models take a long time to become operational. Artificial Intelligence tools such as Machine Learning (ML) can hasten the modelling processes.

The IIT Madras team used an ML method called Recurrent Neural Networks (RNN) to study the reactions that occur during the conversion of lignocellulosic biomass into energy dense syngas (gasification of biomass).

"The novelty of our ML approach is that it is able to predict the composition of the biofuel produced as a function of the time the biomass spends in the reactor. We used a statistical reactor for accurate data generation, which allows the model to be applied over a wide range of operating conditions,a explained Dr Niket S Kaisare, Professor, Department of Chemical Engineering, IIT Madras.

The researchers detail the study in the peer-reviewed journal Reaction Chemistry and Engineering.

The team used AI tools not only for biomass-biofuel conversion studies but also for socially relevant and environmentally beneficial processes such as carbon capture (the capture of CO2 to prevent climate change) and the electrification of the chemical industry.

Researchers all over the world are finding methods to extract fuel from biomass such as wood, grass, and even waste organic matter.

Such biomass-derived fuel is particularly relevant to India because the current availability of biomass in India is estimated at about 750 million metric tonnes per year and extracting fuel from them can tremendously help the country attain fuel self-sufficiency.