Abstract
The current study aims at introducing a fast and precise method for analyzing the operation of renewable and sustainable energy systems. Accordingly, ultrasound assisted transesterification as a novel method of biodiesel synthesis and biodiesel synthesis using mechanical stirring were selected as the two main systems for renewable energy production. It is necessary to analyze the parameters which are the most influential on transesterification yield estimation and prediction in order to assess transesterification yield. ANFIS (adaptive neuro-fuzzy inference system) was used in this study for selecting the most influential parameters based on five input parameters (operational variables). The effectiveness of the proposed strategy was verified with the simulation results. Experiments were conducted to extract training data for the ANFIS network. Furthermore, RSM (response surface methodology) was used to design the experiments and analyze the interactive and individual effects of the five independent variables in order to evaluate the results predicted by ANFIS. The obtained results clearly demonstrated the effects of operational variables on the final transesterification yield.
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