• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

使用人工神经网络（ANN）和响应面法（RSM）对配备乙醇汽油混合燃料的火花点火（SI）发动机性能和排放进行预测建模与优化。

Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM.

作者信息

Shaik Farooq, Kumar D Vinay, Naik N Channa Keshava, Krishna G Radha, Khan T M Yunus, Shaik Abdul Saddique, Buradi Abdulrajak, Emma Addisu Frinjo

机构信息

Department of Mechanical Engineering, Vignan's Foundation for Science Technology and Research, Vadlamudi, Andhra Pradesh, India.

Department of Mechanical Engineering, BGS College of Engineering and Technology, Bangalore, 560086, India.

出版信息

Sci Rep. 2025 Feb 7;15(1):4585. doi: 10.1038/s41598-025-88486-3.

DOI:10.1038/s41598-025-88486-3
PMID:39920296
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11805895/
Abstract

The study employed an Artificial Neural Network (ANN) to predict the performance and emissions of a single-cylinder SI engine using blends of Gasoline, Ethanol, and Methanol (GEM) ranging from E10 to E50 equivalence, achieving less than 5% error compared to experimental values. Furthermore, Response Surface Methodology (RSM) was utilized to optimize the engine's performance, identifying the optimal operating conditions of 2992.9 rpm engine speed and an E20-equivalent GEM blend. Under these conditions, the engine exhibited a brake thermal efficiency (B_The) of 34.63%, a brake specific fuel consumption (BSFC) of 243.7 g/kW-hr, and minimal emissions of 1.5% CO, 108.13 ppm HC, and 1211.8 ppm NO, with an overall desirability of 0.820, indicating a highly favorable combination of performance and emissions characteristics.

摘要

该研究采用人工神经网络（ANN）来预测单缸火花点火发动机使用从E10到E50当量的汽油、乙醇和甲醇混合燃料（GEM）时的性能和排放，与实验值相比误差小于5%。此外，利用响应面方法（RSM）优化发动机性能，确定了发动机转速为2992.9转/分钟和E20当量GEM混合燃料的最佳运行条件。在这些条件下，发动机的制动热效率（B_The）为34.63%，制动比油耗（BSFC）为243.7克/千瓦·时，一氧化碳排放最低为1.5%，碳氢化合物排放为108.13 ppm，氮氧化物排放为1211.8 ppm，综合可取性为0.820，表明性能和排放特性的组合非常理想。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/bd1df4180224/41598_2025_88486_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/2e29a17ab317/41598_2025_88486_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/8d7017b37115/41598_2025_88486_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/2414574401de/41598_2025_88486_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/1371fa341af9/41598_2025_88486_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/ef4812752843/41598_2025_88486_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/acf9451b458c/41598_2025_88486_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/a534a86b0549/41598_2025_88486_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/5c635917df67/41598_2025_88486_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/3059e282e673/41598_2025_88486_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/912ccd337df2/41598_2025_88486_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/31c69a367c74/41598_2025_88486_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/6b6ed299d55e/41598_2025_88486_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/85c7df39f359/41598_2025_88486_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/80ddc635f354/41598_2025_88486_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/bd1df4180224/41598_2025_88486_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/2e29a17ab317/41598_2025_88486_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/8d7017b37115/41598_2025_88486_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/2414574401de/41598_2025_88486_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/1371fa341af9/41598_2025_88486_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/ef4812752843/41598_2025_88486_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/acf9451b458c/41598_2025_88486_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/a534a86b0549/41598_2025_88486_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/5c635917df67/41598_2025_88486_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/3059e282e673/41598_2025_88486_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/912ccd337df2/41598_2025_88486_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/31c69a367c74/41598_2025_88486_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/6b6ed299d55e/41598_2025_88486_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/85c7df39f359/41598_2025_88486_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/80ddc635f354/41598_2025_88486_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c941/11805895/bd1df4180224/41598_2025_88486_Fig15_HTML.jpg

相似文献

1
Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM.使用人工神经网络（ANN）和响应面法（RSM）对配备乙醇汽油混合燃料的火花点火（SI）发动机性能和排放进行预测建模与优化。
Sci Rep. 2025 Feb 7;15(1):4585. doi: 10.1038/s41598-025-88486-3.
2
Response surface methodology application on lubricant oil degradation, performance, and emissions in SI engine: A novel optimization of alcoholic fuel blends.响应面法在 SI 发动机润滑油降解、性能和排放中的应用：醇燃料混合物的新型优化。
Sci Prog. 2023 Jan-Mar;106(1):368504221148342. doi: 10.1177/00368504221148342.
3
Combined Effect of Operating Parameters and Nanoparticles on the Performance of a Diesel Engine: Response Surface Methodology-Coupled Genetic Algorithm Approach.运行参数与纳米颗粒对柴油机性能的联合影响：响应面法耦合遗传算法
ACS Omega. 2023 Jun 30;8(27):24586-24600. doi: 10.1021/acsomega.3c02782. eCollection 2023 Jul 11.
4
Application of ANN to predict performance and emissions of SI engine using gasoline-methanol blends.人工神经网络在预测使用汽油-甲醇混合燃料的火花点火发动机性能和排放方面的应用。
Sci Prog. 2021 Jan-Mar;104(1):368504211002345. doi: 10.1177/00368504211002345.
5
Enhancement of combustion effect leading to improved performance and exhaust emissions of an SI engine with ferrous oxide and graphite nanoparticles.利用氧化亚铁和石墨纳米颗粒增强燃烧效果，从而改善火花点火式发动机的性能和尾气排放。
Environ Sci Pollut Res Int. 2023 Aug 8. doi: 10.1007/s11356-023-28556-z.
6
Multi-objective optimization of diesel engine performance and emissions fueled with diesel-biodiesel-fusel oil blends using response surface method.使用响应面法对柴油-生物柴油-航空煤油混合燃料的柴油机性能和排放进行多目标优化。
Environ Sci Pollut Res Int. 2018 Dec;25(35):35429-35439. doi: 10.1007/s11356-018-3459-z. Epub 2018 Oct 22.
7
Comparison of machine learning algorithms for predicting diesel/biodiesel/iso-pentanol blend engine performance and emissions.用于预测柴油/生物柴油/异戊醇混合燃料发动机性能和排放的机器学习算法比较
Heliyon. 2023 Oct 21;9(11):e21365. doi: 10.1016/j.heliyon.2023.e21365. eCollection 2023 Nov.
8
Multi-objective optimization of ternary blends of Algal biodiesel-diesel-1-decanol to mitigate environmental pollution in powering a diesel engine using RSM, ANOVA, and artificial bee colony.使用响应曲面法（RSM）、方差分析（ANOVA）和人工蜂群算法对藻类生物柴油-柴油-1-癸醇三元混合燃料进行多目标优化，以减轻柴油机运行时的环境污染。
Environ Sci Pollut Res Int. 2024 Dec;31(60):67664-67677. doi: 10.1007/s11356-023-30948-0. Epub 2023 Nov 21.
9
Split injection strategy control map development through prediction-based calibration approach to improve the biodiesel-fuelled diesel engine characteristics.通过基于预测的标定方法开发分体喷射策略控制图，以改善生物柴油燃料的柴油机特性。
Environ Sci Pollut Res Int. 2024 Sep;31(41):53895-53919. doi: 10.1007/s11356-023-29905-8. Epub 2023 Sep 21.
10
Influence of fuel system variations on performance and emission characteristics of combined air-wall-guided mode modified GDI engine with alcoholic fuels and exhaust gas recirculation.燃料系统变化对采用酒精燃料和废气再循环的组合式气壁引导模式改进型汽油直喷发动机性能和排放特性的影响
Environ Sci Pollut Res Int. 2023 May;30(22):61234-61245. doi: 10.1007/s11356-022-21875-7. Epub 2022 Jul 11.

引用本文的文献

1
Thermodynamics based modeling of various in-cylinder processes and spark ignition engine cycle with a focus on sub-models assessment.基于热力学的各种缸内过程和火花点火发动机循环建模，重点在于子模型评估。
Sci Rep. 2025 Aug 19;15(1):30345. doi: 10.1038/s41598-025-15819-7.