Performance Analysis of Biomass Energy using Machine and Deep Learning Approaches

Nowadays, fossil fuels such as petroleum, diesel and coal are being used as an energy source in every modern machinery but these are non-renewable and available in certain domain of nature only. Additionally, excess use of such fuels can cause environmental pollutions, damage of human inhaling process and increase the dependency on other oil rich countries. These challenges could be avoided by using Biomass Energy, which is clean and renewable. Precisely, Biomass Energy is based on hydrocarbon materials which could come from both animal and plant derivatives. There are three forms of Biomass Energy: (i) Gases-methane, ethane etc.; (ii) Liquid-ethanol, biodiesel etc., and (iii) Solid-biochar and activated carbon. These energies are acknowledged for cost effectiveness, renewable nature and less emerging pollutants as compared to fossil fuels. At the present time, these renewable Biomass Energies are useful to operate large number of advanced machines, along with which, the challenge isto estimate the production of Biomass Energy from the available biomass sources without hampering the biodiversity. Therefore, in this study Machine and Deep Learning algorithms are used to calculate the Biomass Energy. Moreover, this work introduces number of Machine and Deep Learning approaches to compute the Biomass Energy production along with Machine Learning tools to analyze the performance of Biomass Energy.