Optimization of Diesel Engine Performance and Emissions Characteristics with Tomato Seed Blends and EGR Using Response Surface Methodology

International journal of automotive science and technology Pub Date : 2023-09-30 DOI:10.30939/ijastech..1326036

Kumaran P, Natarajan S, Sudesh Kumar M P, Mohamed RASHİD, Nithish S

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Abstract

The impact of load, Tomato Methyl Ester (TME), and Exhaust Gas Recirculation (EGR) enriched diesel on engine performance and exhaust gas emissions was examined in this study using the Response Surface Methodology (RSM) optimization technique. TME blend biodiesel (20, 40, and 60%) and EGR (10, 20 and 30%) were selected to maximize BTE and minimize BSFC, NOx, CO, smoke, and HC. The engine was operated using the RSM technique by load (0–100%). The results showed that the response variables were significantly influenced by load, TME and EGR concentration of engine. The results of the constructed quadratic models' analysis of variance (ANOVA) suggested that each model was an appropriate fit. Additionally, an optimal was found by optimizing the user-defined historical design of an experiment. The responses corresponding to optimal study factors were load 100%, TME Blend 20 and EGR 10% gives maximum BTE of 32.5%. The fuel consumption 0.2 kg/kW.h is reduced when load 50%, TME Blend 60 and EGR 10%. Smoke and NOx emissions were decreased by 15.09% and 49.04%, while CO and HC emissions were increased by 27.54% and 21.76%

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基于响应面法的番茄种子混合物和EGR柴油机性能和排放特性优化

采用响应面法(RSM)优化技术，研究了负荷、番茄甲酯(TME)和废气再循环(EGR)富集柴油对发动机性能和废气排放的影响。选择TME混合生物柴油(20%、40%和60%)和EGR(10%、20%和30%)来最大化BTE，最小化BSFC、NOx、CO、烟雾和HC。发动机采用负载(0-100%)RSM技术运行。结果表明，负荷、发动机TME和EGR浓度对响应变量有显著影响。所构建的二次元模型方差分析结果表明，各模型均具有较好的拟合性。此外，通过优化用户自定义的实验历史设计，找到了最优方案。最佳研究因子为负载100%、TME Blend 20和EGR 10%，最大BTE为32.5%。负荷50%、TME Blend 60、EGR 10%时，油耗降低0.2 kg/kW.h。烟气和氮氧化物排放量分别下降了15.09%和49.04%，CO和HC排放量分别增加了27.54%和21.76%

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International journal of automotive science and technology

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