Key points are not available for this paper at this time.
This research utilized a combined hydrodynamic cavitation reactor to produce biodiesel. The reactor worked automatically with the help of a controller designed by LabVIEW. For this purpose, rapeseed oil (0.5 L per experiment) and methanol alcohol with the sodium hydroxide catalyst were used for biodiesel production. The important factors of the study were: 1.pump flow rate (three levels of 1.4, 2 and 2.6 L/min); 2.the molar ratio of methanol to oil (4:1, 6:1 and 8:1); 3.the rotational speed of the reactor (8000, 12000 and 16000 rpm), and 4.circulation time (2, 4 and 6 min). The study analyzed the energy ratio (output energy/input energy) of the produced biodiesel to evaluate the system and modeled the performance of the system to obtain the best-operating conditions of the reactor. In this respect the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and response surface methodology (RSM) methods were employed. The average energy ratio was obtained 1.205, and the R2 of the best ANFIS, ANN and RSM models were 0.989, 0.966, and 0.990, respectively, and MSE was calculated at 0.0005, 0.0015 and 0.00003. The results revealed that the RSM and ANFIS models were preferred to the neural network model in terms of better performance, simplicity, and high processing speed. In general, the RSM model functioned better than the ANFIS model. Accordingly, the best reactor settings to obtain the maximum energy ratio (1.35) and biodiesel yield (91.87 %) was when the circulation time, the rotational speed, the pump flow rate and the molar ratio were set at 2 min, 8000 rpm, 1.4 L/min and 4, respectively.
Leila Naderloo (Fri,) studied this question.