Researchers at the Indian Institute of Technology in Madras (IIT-Madras) have used artificial intelligence (AI) tools to study fuel production from biomass. With increasing environmental concerns associated with petroleum-derived fuels, biomass is a practical solution as a source of energy-dense fuel. In a statement, IIT-Madras explained that the researchers used AI and computer simulation and modelling to understand the concept, which saved time and costs.
The team used a machine-learning method called recurrent neural networks (RNN) to study the reactions that occur during the conversion of biomass into energy-dense syngas (gasification of biomass). A researcher said that the technology is able to predict the composition of the biofuel produced as a function of the time the biomass spends in the reactor. The team used a statistical reactor for accurate data generation, which allows the model to be applied over a wide range of operating conditions.
Researchers across the globe are finding methods to extract fuel from biomass such as wood, grass, and even waste organic matter. Studying the processes through hands-on experiments is time-consuming and expensive. Computer simulations and modelling studies can provide quicker insights that can be used to build the processes and plants for biomass processing. In India currently, where about 750 million metric tonnes of biomass is available annually, biomass-derived fuel is particularly relevant. IIT-Madras believes extracting fuel from biomass can tremendously help the country attain fuel self-sufficiency.
According to one of the researchers, understanding the complex mechanisms involved in the conversion of raw biomass into fuel is important for designing the processes and optimising reactors. It calls for training engineers in high-performance computing and machine learning to address the challenges of developing zero-emission technologies.
The IIT-Madras group uses AI tools not just for biomass-biofuel conversion studies but also for other socially relevant and environmentally beneficial processes such as carbon capture and the electrification of the chemical industry. The team believes that the rapid advancements in computational methods must be integrated with core engineering for the quick development and deployment of deep tech solutions. These developments cannot be constrained by specialities and departments. Recent results of their modelling studies were published in the peer-reviewed Royal Society of Chemistry journal, Reaction Chemistry and Engineering.
Earlier this year, experts from India and the United States discussed the challenges and opportunities to combat climate change through technology-led “carbon capture and utilisation solutions” at an event jointly organised by the two governments. As OpenGov Asia reported, India aims to be a net-zero emissions nation by 2070. Officials stated that under a strict climatic regime, the country can identify and adopt a balance of portfolio of emission curtailment technologies.
Carbon capture, utilisation, and storage (CCUS) research are among key pathways to reduce emissions while developing rapidly and sustainably at an unprecedented pace. CCUS aligns with five of the seventeen United Nations Sustainable Development Goals (SDGs): climate action; clean energy industry; innovation and infrastructure; responsible consumption and production; and partnerships to achieve the goals.
