K-means clustering optimization of various quantum dots and nanoparticles-added biofuels for engine performance, emission, vibration, and noise characteristics

Abstract

The utilization of biodiesel as a bio-lubricant, combined with various types of nanoparticles, and quantum dots presents an innovative approach towards enhancing the performance of diesel engines. In this study, biodiesel was blended with different nanoparticles to improve its lubricating properties, and the k-means clustering method was applied to identify the optimal nanoparticle that maximizes the performance of the diesel engine. The experiment involved synthesizing biodiesel-based lubricants infused with nanoparticles of varying compositions, including but not limited to carbon nanotubes, graphene oxide, and metal oxides. The successful implementation of the Entropy-k-means hybrid model facilitated the identification of the optimal bio-lubricant for diesel engines. The experimentation uncertainty came out to be 4 % which lies in an acceptable range. Additionally, a strong Pearson r correlation was observed between Brake Thermal Efficiency (BTE) and Sound, highlighting their interrelationship. Moreover, the prioritization analysis indicated that NOx obtained the highest priority at 0.45, contrasting with the lowest priority attributed to sound at 0.01. Furthermore, the clustering analysis conclusively identified magnetic bio-lubricant as the top-performing option with minimum centroidal distance of 1.2. The optimal outcomes are BTE (30.5 %), Brake Specific Fuel Consumption (191.5 g/kWh), NOx (88 ppm), CO (7.1 g/km), Vibration (68.5 Hz), and Sound (74.5 dB). The application of k-means clustering facilitated the identification of the most effective nanoparticle for enhancing engine performance, thereby contributing to the advancement of sustainable lubrication technologies in the automotive industry.