Optimizing ultrasonic reactor operating variables using intelligent soft computing models for increased biodiesel production

Abstract

Extensive benefits of biodiesel amalgamation with diesel engine have prompted several researches towards suitable optimization of operating parameters of production process. Ultrasonic reactors are acclaimed instruments in generating biodiesel from raw oil. The contemporary research has varied the operating parameters of the ultrasonic reactor for maximum yield with the aid of artificially intelligent software’s. Eucalyptus oil combined with ethanol and sulphuric acid were used as reactants to generate biodiesel. Prime input factors considered in this study comprises of reaction time, molar ratio, frequency, power and temperature. The study’s results are analysed and compared with models created using intelligent hybrid prediction approaches including adaptive neuro-fuzzy inference system (ANFIS), response surface methodology (RSM) - genetic algorithm (GA). The parameters were varied and optimized for maximum biodiesel yield by employing best operating conditions for ultrasonic reactor, furnished by ANFIS and GA in MATLAB software and RSM in Minitab software. All the models performed exceptionally well, with ANFIS performing slightly better with RSME value of 0.0017 while RSM achieved a RSME value of 0.0023. Combining the precision of ANFIS’s prediction with the efficiency of GA-optimization gives a reliable and thorough evaluation. Enhancing the efficiency of biodiesel production can decrease the world’s fuel consumption by reducing the reliance on fossil fuels and concurrently reducing the carbon footprint by vehicles.