Neural-Network-Inspired Correlation (N2IC) Model for Estimating Biodiesel Conversion in Algal Biodiesel Units

Abstract

Algal biodiesel is of growing interest in reducing carbon emissions to the atmosphere. The production of biodiesel is affected by many process parameters. Although many research works have been conducted, the influence of each parameter on biodiesel production is not well understood when considering a complete system. Therefore, the experimental data from literature sources related to types of algae, methanol-to-algal-oil ratio, temperature, and time on the biodiesel production rate were reviewed and introduced into a neural-network-inspired correlation (N2IC) model to study the rate of transesterification. The developed N2IC model optimized for biodiesel production is based on the studied variables, specifically reaction time, temperature, methanol-to-algal-oil ratio, and type of algae. It was found from ANN analysis that the reaction time is the most significant parameter with 87% importance, followed by temperature (85%), alcohol-to-oil-molar ratio (75%), and type of algae (62%). Using error analysis, the results from the proposed N2IC model show excellent agreement with the experimentally obtained values with an overall 5% error. The results show that the N2IC model can be utilized effectively to solve the problem of industrial biodiesel production when various operating data are readily available.