Indian, Russian scientists in biodiesel breakthrough using shrub: Report
Scientists from Russia and India have developed a method for improving the production of biodiesel from the seeds of a poisonous tropical shrub, reported Sputnik News Agency.
According to the authors of the study, a method for processing the Barbados nut (Jatropha curcas) which is developed by training a deep neural network will cut labour costs and improve the quality of the subsequent biofuel.
Biodiesel is a long-chain fatty acid methyl ester that is derived from vegetable oil and animal fats. According to experts, it has several advantages as compared to petroleum-based diesel fuel, such as improved biodegradability, non-toxicity, can be used in any diesel engine or blends. Its usage also leads to a reduction in exhaust gas emissions, unburned hydrocarbons, and particulate matter.
Scientists from multiple institutes including, Don State Technical University (DSTU, Rostov-on-Don, Russia), the Federal Scientific Agroengineering Center VIM (FNAC VIM, Moscow, Russia) together with colleagues from the Department of Systemics, School of Computer Science, and Department of Applied Sciences (Chemistry) and University of Petroleum and Energy Studies in Dehradun, India, used deep machine learning to optimize the parameters of the interesterification process of vegetable oils derived from Barbados walnut seeds and predict the characteristics of the biodiesel produced.
This will improve the quality of the resultant fuel, the accuracy of forecasting its volume and reduce labour costs of the production process, reported Sputnik News Agency.
"According to scientists, the study is particularly relevant for developing countries in Africa and South America, where the Barbados nut grows as a weed. It began to be cultivated commercially in India and China after specialists pointed to this plant as one of the best candidates for future biodiesel production," reported Sputnik News Agency.
Meanwhile, the Russian-Indian research team has planned to continue working to enhance the quality of forecasting of the developed deep learning model, as well as, training the model based on a different type of raw material.
Additionally, there is a need to conduct feasibility studies, life cycle assessments, energy and exergy assessments of the application of the model in the production of biodiesel fuel.
"As a result of the work done, the most important parameters of the transesterification process were classified in terms of their maximum impact on the properties of biodiesel fuel," said Vadim Bolshev.