Sooting propensity of gasoline/diesel-ether blends: Experimental assessment and artificial neural network modeling

IF 6.2 2区工程技术 Q2 ENERGY & FUELS

Journal of The Energy Institute Pub Date : 2025-10-01 DOI:10.1016/j.joei.2025.102311

Mohammed Ameen Ahmed Qasem , Eid M Al Mutairi , Abdul Gani Abdul Jameel

{"title":"Sooting propensity of gasoline/diesel-ether blends: Experimental assessment and artificial neural network modeling","authors":"Mohammed Ameen Ahmed Qasem , Eid M Al Mutairi , Abdul Gani Abdul Jameel","doi":"10.1016/j.joei.2025.102311","DOIUrl":null,"url":null,"abstract":"<div><div>This study investigated the impact of ethers as oxygenated fuel additives, on reducing soot emissions from gasoline and diesel combustion. Soot formation, a significant environmental challenge, is heavily influenced by the molecular structure of fuels, necessitating a thorough assessment of the sooting tendencies of diesel/gasoline-ether blends to better understand and mitigate particulate matter (PM) emissions. The study employed measurements of the smoke point (SP), oxygen-extended sooting index (OESI), and threshold sooting index (TSI) to evaluate the sooting tendencies of these blends. Artificial intelligence (AI) models were developed using Artificial Neural Network (ANN) tools, based on SP measurements from forty blends with varying ether percentages in diesel/gasoline mixtures. Various features, such as functional groups, molecular weight, branching index, density, and molar ratios, were used as inputs, while the measured SPs, TSIs, and OESIs served as target outputs.</div><div>Although SP and TSI are widely used to evaluate soot formation, they have limitations in capturing the role of oxygen in combustion chemistry. To address this gap, the OESI—an index that explicitly incorporates the effect of fuel-borne oxygen—was employed in this study to evaluate soot formation in ether-based blends with gasoline and diesel. Moreover, ANNs were applied to predict soot formation in untested blends with similar molecular structures, providing a robust predictive framework that complements experimental analysis.</div><div>The results revealed a strong correlation between predicted and experimental indices, with correlation coefficients (R) of 0.96 for SP, 0.99 for TSI, and 0.98 for OESI, indicating high model accuracy. The respective mean absolute errors were 1.16, 1.00, and 4.92, confirming the reliability of the AI approach. Key molecular characteristics, such as aromaticity, branching, and molar ratios, were found to significantly influence sooting behavior. This study highlights the potential of AI-driven models in accurately predicting soot formation trends in fuel blends containing ethers, offering valuable insights for the design of cleaner and more sustainable fuels.</div></div>","PeriodicalId":17287,"journal":{"name":"Journal of The Energy Institute","volume":"123 ","pages":"Article 102311"},"PeriodicalIF":6.2000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of The Energy Institute","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1743967125003393","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}

引用次数: 0

Abstract

This study investigated the impact of ethers as oxygenated fuel additives, on reducing soot emissions from gasoline and diesel combustion. Soot formation, a significant environmental challenge, is heavily influenced by the molecular structure of fuels, necessitating a thorough assessment of the sooting tendencies of diesel/gasoline-ether blends to better understand and mitigate particulate matter (PM) emissions. The study employed measurements of the smoke point (SP), oxygen-extended sooting index (OESI), and threshold sooting index (TSI) to evaluate the sooting tendencies of these blends. Artificial intelligence (AI) models were developed using Artificial Neural Network (ANN) tools, based on SP measurements from forty blends with varying ether percentages in diesel/gasoline mixtures. Various features, such as functional groups, molecular weight, branching index, density, and molar ratios, were used as inputs, while the measured SPs, TSIs, and OESIs served as target outputs.

Although SP and TSI are widely used to evaluate soot formation, they have limitations in capturing the role of oxygen in combustion chemistry. To address this gap, the OESI—an index that explicitly incorporates the effect of fuel-borne oxygen—was employed in this study to evaluate soot formation in ether-based blends with gasoline and diesel. Moreover, ANNs were applied to predict soot formation in untested blends with similar molecular structures, providing a robust predictive framework that complements experimental analysis.

The results revealed a strong correlation between predicted and experimental indices, with correlation coefficients (R) of 0.96 for SP, 0.99 for TSI, and 0.98 for OESI, indicating high model accuracy. The respective mean absolute errors were 1.16, 1.00, and 4.92, confirming the reliability of the AI approach. Key molecular characteristics, such as aromaticity, branching, and molar ratios, were found to significantly influence sooting behavior. This study highlights the potential of AI-driven models in accurately predicting soot formation trends in fuel blends containing ethers, offering valuable insights for the design of cleaner and more sustainable fuels.

查看原文本刊更多论文

汽油/柴油-醚混合燃料的燃烧倾向：实验评估和人工神经网络建模

本研究考察了醚作为含氧燃料添加剂对减少汽油和柴油燃烧时烟尘排放的影响。油烟的形成是一项重大的环境挑战，受到燃料分子结构的严重影响，因此有必要对柴油/汽油-醚混合物的油烟倾向进行彻底评估，以更好地了解和减少颗粒物（PM）的排放。本研究采用烟点（SP）、增氧烟尘指数（OESI）和阈值烟尘指数（TSI）的测量来评价这些混合物的烟尘倾向。利用人工神经网络（ANN）工具开发了人工智能（AI）模型，该模型基于40种柴油/汽油混合物中不同醚百分比的SP测量结果。各种特征，如官能团、分子量、分支指数、密度和摩尔比，被用作输入，而测量的SPs、tsi和OESIs作为目标输出。虽然SP和TSI被广泛用于评价烟尘的形成，但它们在捕捉氧气在燃烧化学中的作用方面存在局限性。为了解决这一差距，本研究采用了oesi -一种明确包含燃料携带氧影响的指数-来评估汽油和柴油的醚基混合物中烟灰的形成。此外，人工神经网络被用于预测具有相似分子结构的未测试混合物中的烟灰形成，提供了一个强大的预测框架，补充了实验分析。结果表明，SP、TSI和OESI的相关系数(R)分别为0.96、0.99和0.98，模型精度较高。平均绝对误差分别为1.16、1.00和4.92，证实了人工智能方法的可靠性。关键的分子特征，如芳香性，分支和摩尔比，发现显着影响熏烟行为。这项研究强调了人工智能驱动的模型在准确预测含醚燃料混合物中烟灰形成趋势方面的潜力，为设计更清洁、更可持续的燃料提供了有价值的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of The Energy Institute 工程技术-能源与燃料

CiteScore

10.60

自引率

5.30%

发文量

166

审稿时长

16 days

期刊介绍： The Journal of the Energy Institute provides peer reviewed coverage of original high quality research on energy, engineering and technology.The coverage is broad and the main areas of interest include: Combustion engineering and associated technologies; process heating; power generation; engines and propulsion; emissions and environmental pollution control; clean coal technologies; carbon abatement technologies Emissions and environmental pollution control; safety and hazards; Clean coal technologies; carbon abatement technologies, including carbon capture and storage, CCS; Petroleum engineering and fuel quality, including storage and transport Alternative energy sources; biomass utilisation and biomass conversion technologies; energy from waste, incineration and recycling Energy conversion, energy recovery and energy efficiency; space heating, fuel cells, heat pumps and cooling systems Energy storage The journal''s coverage reflects changes in energy technology that result from the transition to more efficient energy production and end use together with reduced carbon emission.