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<p>In this study, tests were done to see what would happen if hydrogen (H<sub>2</sub>) and lemon grass oil (LO) were used for a lone-cylinder compression ignition engine as a partial diesel replacement. After starting the trial with pure diesel, the engine was modified to run on two fuels by adding H<sub>2</sub> through the inlet pipe and LO as direct injection (DI) gasoline. In this study, the energy fraction of H<sub>2</sub> ranged from 5 to 10%. A 3% volume mixture of water (W) and diesel-LO mixtures was used to study how NO<sub>X</sub> emissions were affected. Using a common back- transmission method, an Artificial Neural Network model was created to forecast the correlation between engine output reactions and input variables (load, LO, and H<sub>2</sub>). The goal of response surface methodology is for improve the motor input settings for low pollution and high heat efficiency. The trials were designed using response surface methodology (RSM), and the optimization procedure was conducted using RSM. An artificial neural network (ANN) was used to predict the test engine’s emission patterns and performance. The results showed that ANN and RSM were both very good and accurate modeling strategies. When 10% H<sub>2</sub> + LO25 was used, the BTE rose to 31%, up from 20% for clean LO and 28% for 12% H<sub>2</sub> + LO25 + 2%W means. Compared to clean LO operation, the cylinder pressure raised in 15% H<sub>2</sub> mode by up to 63&#xa0;psi. The measured value for the HRR, under H<sub>2</sub> premixed combustion framework was 81&#xa0;k Joule/CA deg in 10% H<sub>2</sub>, that is significantly greater than the value for every LO processes. The highest NO<sub>X</sub> release for 10% H<sub>2</sub> + LO 25% was discovered at 690&#xa0;ppm peak load, while it dropped to 645&#xa0;ppm in gasoline that had been emulsified in water.</p>

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Implementation of ANN and response surface method for dual-fuel CI engine optimization and prediction using water infusion and biofuel

  • 期刊论文

  • 发表日期：

    2025年1月25日

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  • Sabyasachi Pramanik

摘要

In this study, tests were done to see what would happen if hydrogen (H2) and lemon grass oil (LO) were used for a lone-cylinder compression ignition engine as a partial diesel replacement. After starting the trial with pure diesel, the engine was modified to run on two fuels by adding H2 through the inlet pipe and LO as direct injection (DI) gasoline. In this study, the energy fraction of H2 ranged from 5 to 10%. A 3% volume mixture of water (W) and diesel-LO mixtures was used to study how NOX emissions were affected. Using a common back- transmission method, an Artificial Neural Network model was created to forecast the correlation between engine output reactions and input variables (load, LO, and H2). The goal of response surface methodology is for improve the motor input settings for low pollution and high heat efficiency. The trials were designed using response surface methodology (RSM), and the optimization procedure was conducted using RSM. An artificial neural network (ANN) was used to predict the test engine’s emission patterns and performance. The results showed that ANN and RSM were both very good and accurate modeling strategies. When 10% H2 + LO25 was used, the BTE rose to 31%, up from 20% for clean LO and 28% for 12% H2 + LO25 + 2%W means. Compared to clean LO operation, the cylinder pressure raised in 15% H2 mode by up to 63 psi. The measured value for the HRR, under H2 premixed combustion framework was 81 k Joule/CA deg in 10% H2, that is significantly greater than the value for every LO processes. The highest NOX release for 10% H2 + LO 25% was discovered at 690 ppm peak load, while it dropped to 645 ppm in gasoline that had been emulsified in water.