  The production process involved in the creation of biofuels consists of a
number of operations and steps that require a meticulous understanding of the
parameters and metrics. The production techniques again differ depending on the
pre-treatment systems, source material, the methods used for extraction, types
of nutrients used, cell cultures employed, time undertaken and temperature. Due
to the strategic and crucial role that bioethanol holds in supporting the
energy demands of the future, it becomes important to run such processes to a
highly optimized extent. One of the frontiers of leading such optimized designs
is by studying the data from the production processes, formulating design
experiments from said data and correlating the results with the parameters
using analytical tools. While the case examples analyzed relate to bioethanol
mostly, an additional analysis has been performed for data on biodiesel.
Coupled with confirmatory methods such as Principal Component Analysis,
researchers can help narrow down the extent or degree to which the parameters
affect the final outcome and even configure inputs that may not play a
definitive role in greater outputs. The project first tackles through some
conventional case studies involving biofuel production using an FIS(Fuzzy
Interface System) and provides certain insights into the ways in which fuel
yields can be enhanced depending on the particular cases. For the purpose of
analysis, tools such as MATLAB, Python and WEKA have been employed. Python and
WEKA have been used extensively in building principal component analysis
reviews for the purpose of this project while MATLAB has been used for building
the FIS models.
