An artificial intelligence approach to model and optimize biodiesel production from used cooking oil using CaO incorporated zeolite catalyst

The current work investigated the possibility of employing chicken eggshell-zeolite composite as a cheap and recyclable heterogeneous catalyst for used cooking oil (UCO) conversion into its corresponding methyl ester (UCOME) via methanolysis process. Various catalysts were formulated by loading eggshell on the zeolite and calcined at different temperatures to obtain CaO incorporated zeolite (CaO-Zel) catalyst. The catalyst sample calcined at 800 °C for 4 h (CaO-Zel-800) exhibited best activity for methanolysis process and was analyzed using various techniques, including BET, surface basicity, TPD-CO2, TGA/DTA, SEM, XRD and FTIR. The transesterification process was modeled using artificial intelligence approach viz. artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) while optimization of the operating variables (temperature, catalyst loading, time and molar ratio) was performed by interfacing the developed models with ant colony optimization (ACO) algorithm. The closeness of coefficient of determination (R2) to unity and low mean square error (MSE) indicated that the methanolysis of UCO was adequately described by the developed models with ANFIS model (R2 = 0.9997 and MSE = 0.1271) superior to ANN model (R2 = 0.9953 and MSE = 2.0762). The highest UCOME yield of 99.45 ± 0.51 wt% was achieved with ANFIS-ACO under the condition; reaction temperature (72.97 °C), methanol/UCO molar ratio (7.14:1), reaction time (131.97 min) and catalyst dosage (5.39 wt%). The results of the sensitivity analysis revealed that all the operating variables influence UCOME yield, and none could be discarded. The CaO-Zel-800 catalyst was reused for five consecutive reaction cycles, and its activity only decreased by about 12%.