Optimization of biodiesel production parameters for hybrid oil using RSM and ANN technique and its effect on engine performance, combustion, and emission characteristics

Abstract

The increased need for renewable energy sources and growing concerns about environmental pollution has driven the need for alternative fuels (particularly biodiesel) in the transportation industry. The most used technique for producing biodiesel is transesterification, which involves the oil reacting with alcohol in the presence of a catalyst. This study looks at the usage of artificial neural network (ANN) and response surface methodology (RSM) to optimize biodiesel synthesis factors for a hybrid oil feedstock (30% cottonseed and 70% spirulina microalgae oil). The parameters that were optimized were the methanol/oil ratio, reaction time, and catalyst concentration. Cottonseed oil was combined with spirulina microalgae oil to minimize its higher free fatty acid content to less than 1% and prevent soap formation during the transesterification process. The optimal biodiesel production was 94.37%, with 106.4 minutes of reaction time, 1.612% catalyst loading, and a methanol/oil ratio of 20:1. The optimized findings, when confirmed with experiments, gave a biodiesel yield of 91.03% with an error of 3.6%. To provide vital insights into the potential environmental benefits of using this alternative fuel, the study further investigates how the optimized biodiesel blend influences engine performance and emission characteristics. Testing was done at 1500 RPM and 17.5 CR for maximum loading conditions, diesel, HBD20, and HBD fuel produced 0.2746, 0.2805, and 0.288 kg/kWh of specific fuel consumption (SFC), respectively. The CO2 emissions of HBD and HBD20 were 2.49% and 8.30% lower, respectively, than pure diesel. NOx emissions increased by 14% and 18.3%. The highest cylinder pressure was 66.87 at 363 °C.