Optimizing Biodiesel Yield and Fuel Properties from Waste Avocado Oil: A Comparative Study of RSM and ANFIS Learning Models

The quest for alternative energy sources, driven by the challenges of fossil fuels, has led to the development of biofuels. This study focuses on producing biodiesel from waste avocado oil through transesterification. Initially, oil was extracted from the peels and seed of avocado pear using an extraction technique. The extracted oil was then pre-treated with methanol and sulfuric acid (H₂SO₄) to reduce its free fatty acid content to less than ۱.۰ wt%. This study compares two expert systems, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Response Surface Methodology (RSM), for modeling and optimizing biodiesel production from avocado oil. The performance of these optimization tools was evaluated using statistical indices. The results showed that ANFIS outperformed RSM with a low error value, the Standard Error of Prediction (SEP)=۰.۷۶۵۳, the Mean Absolute Error (MAE)=۰.۱۴۱۳, the Root Mean Squared Error (RMSE)=۰.۴۱۰۳, the Average Absolute Deviation (AAD)=۰.۲۹۵۵%, the Mean Squared Error (MSE)=۰.۱۶۸۳, and a high coefficient of determination (R² = ۰.۹۹۷۶). Both models predicted high biodiesel yields (>۸۵%), with ANFIS achieving a slightly higher yield (۸۸.۲۱%) compared to RSM (۸۶.۲۰%). The properties of the biodiesel produced under optimized conditions were compared with American Society for Testing and Materials (ASTM) D۶۷۵۱ and European Norm (EN) ۱۴۲۱۴ standards and were found to be within acceptable limits, indicating the fuel’s suitability.

کلیدواژه ها:

Biodiesel ، RSM ، ANFIS ، Waste oil ، Fuel