Model developed by researchers in the Indian Institute of Technology-Hyderabad (IITH), using computational methods like Machine Learning techniques to understand the factors and impediments to incorporating biofuels into the fuel sector in India, has found production cost to be the main hinderance to acceptance and use of biofuels.
The research, led by Associate Professor in Department of Chemical Engineering Kishalay Mitra with his research scholar Kapil Gumte, has been spurred by the increasing need to replace fossil fuels by bio-derived fuels, which, in turn, is driven by the dwindling fossil fuel reserves all over the world, and pollution issues associated with the use of fossil fuels. The results of this work were recently published in the prestigious peer-reviewed Journal of Cleaner Production.
The model developed by the IITH team has shown that in the area of bioethanol integration into mainstream fuel use, the production cost is the highest (43 %) followed by import (25 %), transport (17 %), infrastructure (15 %) and inventory (0.43 %) costs. The model has also shown that feed availability to the tune of at least 40 % of the capacity is needed to meet the projected demands.
A unique feature of this work is that the framework considers revenue generation not only as an outcome of sales of the biofuel but also in terms of carbon credits via greenhouse gas emission savings throughout the project lifecycle.
This research paper also won the ‘Best Paper Award’ during the Sixth International Conference on Advances in Control and Optimization of Dynamical Systems held at IIT Madras earlier this year, which was attended by large sections of the whole process systems engineering community of India. Mr. Mitra and his team analysed the bio-supply chain network through computational studies.
Highlighting the need for such research, Mr. Kishalay Mitra said: “In India, biofuels generated from non-food sources is the most promising source of carbon-neutral renewable energy. These second-generation sources include agricultural waste products such as straw, hay and wood, among others, that do not intrude upon food sources.”
Biofuel technologies are evolving in India. The design and implementation of technological, regulatory and policy approaches and pricing strategy of biofuels depend on a deep understanding of the supply chain network. Models such as those developed at IIT Hyderabad allow society to understand the effects of uncertainty in the network parameters on the demand-supply dynamics and can help policymakers devise and revise strategies to meet the future demands of biofuels, Mr. Mitra explained.
Elaborating on this research, Mr. Kapil Gumte said: “We use Machine Learning techniques to understand the supply chain network. Machine learning is a branch of Artificial Intelligence in which, the computer learns patterns from available data and updates automatically to produce an understanding of the system and make forecasts of the future.”
“The techno-economic-environmental analysis on country-wide multi-layered supply chain network and the use of Machine Learning techniques have helped us capture the uncertainty in forecasting demands and other supply chain parameters and their effects on the operational and design decisions in the long run,” Mr. Mitra added.