The study examined the dual-fuel engine performance employing acetylene gas as primary fuel and diesel as pilot fuel. The engine's operational parameters were adjusted using the Box-Behnken design, and the results were recorded. The best operating settings were yielded as 81.25 % engine load, 4.48 lpm acetylene gas flow rate and the compression ratio were 18. At this optimized setting the BTE was 27.1 % and the engine emitted 360 ppm of NOx, 56.2 ppm of HC, 104 ppm of CO. The experimental data at optimized setting was compared to the optimized results, and the percentage of errors was within 7 %. Two advanced machine learning methods, LightGBM and Tweedie, were used to predict engine efficiency and emissions. Tweedie-based models had an R2 value of 0.89–1, while LightGBM-based models had an R2 value of 0.38–1. The mean squared error was 0.24–45.04 for Tweedie-based models and 8.5 to 153.89 for LightGBM-based models. On the basis of R2 and MSE, it was observed that Tweedie was superior at making predictions than LightGBM. The study demonstrated the efficient functioning of a dual-fuel engine using acetylene as an alternative fuel for increased performance and lower emissions.

中文翻译：

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使用乙炔作为替代燃料的双燃料柴油机的基于需求的优化

该研究检查了采用乙炔气作为主要燃料、柴油作为引燃燃料的双燃料发动机的性能。使用 Box-Behnken 设计调整发动机的运行参数，并记录结果。最佳运行设置为 81.25 % 发动机负载、4.48 lpm 乙炔气体流量和压缩比为 18。在此优化设置下，BTE 为 27.1 %，发动机排放 360 ppm 的 NOx、56.2 ppm HC、104 ppm将优化设置下的实验数据与优化结果进行比较，误差百分比在7%以内。 LightGBM 和 Tweedie 两种先进的机器学习方法用于预测发动机效率和排放。基于 Tweedie 的模型的 R2 值为 0.89–1，而基于 LightGBM 的模型的 R2 值为 0.38–1。基于 Tweedie 的模型的均方误差为 0.24–45.04，基于 LightGBM 的模型的均方误差为 8.5 到 153.89。根据 R2 和 MSE，观察到 Tweedie 在预测方面优于 LightGBM。该研究证明了使用乙炔作为替代燃料的双燃料发动机的高效运转，可提高性能并降低排放。