Optimizing the effect of using novel hydrogen enriched nano particles added emulsified waste mango seed biodiesel in diesel engine

•In the Response Surface Methodology (RSM), the experimental design was planned with Box Behnken Design (BBD).•The input parameters are optimized using RSM and Deep Recurrent Neural Network based Honey Badger Algorithm (DRNN-HBA).•The optimized input parameters are load 58 %, engine speed 1800 rpm, and NP concentration in biodiesel 75 %. Restricted fossil fuel resources, increased liquid fuel prices, and the pollution range of conventional fuel direct the research into novel technology. The application of biodiesel enhances engine characteristics. In this work, different concentrations of hybrid nanoparticles, namely Aluminum oxide and Titanium oxide (25 ppm, 50 ppm, and 75 ppm), were introduced as nanoadditives. In addition, 10 % share of hydrogen gas by volume was given to analyze the engine characteristics of hydrogen-fueled single cylinder diesel engine. The entire testing was conducted on a constant water content (10 % vol.) emulsified Mango Seed Methyl Ester (MSME) as base fuel blend. The experiment has been planned in Response Surface Methodology (RSM) based on Box Behnken Design (BBD) approach. The variable input parameters, engine load, speed and nanoparticles (NP) concentrations are optimized using RSM and Deep Recurrent Neural Network based Honey Badger Algorithm (DRNN-HBA). The addition of nanoparticles improved the thermal property of the fuel blend, thereby increasing Brake Thermal Efficiency (BTE) for the B20W10 fuel blend with nanoparticles. A maximum BTE of 32 % is achieved by adding 75 ppm nanoparticle to the fuel blend with some counter effect on NOx emission at maximum load. The optimized outcomes are 58 % engine load, 1800 rpm engine speed and 75 ppm NP concentration (B20W10NP75). The proposed DRNN-HBA prediction model achieved better regression correlation (above 0.99) for combustion and emission results with very lesser Root Mean Square Error (RMSE ≤ 6.6) compared to conventional DRNN and RSM.