{"title":"利用 AHP-PROMETHEE II 混合决策模型优化 CI 发动机乙醇燃料诱导技术","authors":"Mazar A Shaikh, Vimal R Patel","doi":"10.12944/cwe.18.3.12","DOIUrl":null,"url":null,"abstract":"Ethanol along with nanoparticles stands out as a promising alternative in the pursuit of environmentally sustainable fuel options, offering a potential solution to the dual challenge of curbing NOx and PM/soot emissions while optimizing engine performance in compliance with stringent pollution regulations for compression ignition (CI) engines. The research study aims to optimize ethanol fuel induction techniques for CI engines. It utilizes a hybrid decision-making approach that integrates the analytic hierarchy process- AHP- for problem structuring and the derivation of preference weights. Subsequently, the preference ranking organization method for enrichment evaluations-PROMETHEE II is applied to assess and rank the existing alternatives. The study entails a methodical assessment of diverse ethanol induction methods across varying engine load ranges, considering multiple criteria including engine performance, emissions, combustion behavior, and exhaust after-treatment efficiency. Hybrid AHP-PROMETHEE II model provides criteria weights and ranks ethanol induction techniques and fuel blends across low, medium, and high engine loads for decision-making. It ensures that the method chosen aligns with goals, such as reducing NOx and soot emissions, optimizing engine performance, enhancing combustion, and minimizing exhaust after-treatment costs for CI engines. According to the research findings, the hybrid AHP-PROMETHEE II model identifies the CI engine operating at medium load with ethanol blending (DE10) and without the use of nanoparticles as the preferred choice. Additionally, AHP-PROMETHEE II (AHP derived criteria weights) and PROMETHEE II (direct rating derived criteria weights) models, suggested DE10 with nanoparticle (DE10_NP) using blending technique at low load and combined blending-fumigation technique with nanoparticles at high load. However, at medium load, PROMETHEE II recommends DE10_NP, while AHP-PROMETHEE II recommends DE10 blending technique. To assess the performance and reliability of this model, the consistency ratio and Spearman's rank correlation coefficient indices were computed, yielding values of 0.05 and 0.59, respectively. Both indices fall below the predetermined threshold limits, indicating a high level of consistency of the model.","PeriodicalId":10878,"journal":{"name":"Current World Environment","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing CI Engine Ethanol Fuel Induction Techniques Using the AHP-PROMETHEE II Hybrid Decision Model\",\"authors\":\"Mazar A Shaikh, Vimal R Patel\",\"doi\":\"10.12944/cwe.18.3.12\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ethanol along with nanoparticles stands out as a promising alternative in the pursuit of environmentally sustainable fuel options, offering a potential solution to the dual challenge of curbing NOx and PM/soot emissions while optimizing engine performance in compliance with stringent pollution regulations for compression ignition (CI) engines. The research study aims to optimize ethanol fuel induction techniques for CI engines. It utilizes a hybrid decision-making approach that integrates the analytic hierarchy process- AHP- for problem structuring and the derivation of preference weights. Subsequently, the preference ranking organization method for enrichment evaluations-PROMETHEE II is applied to assess and rank the existing alternatives. The study entails a methodical assessment of diverse ethanol induction methods across varying engine load ranges, considering multiple criteria including engine performance, emissions, combustion behavior, and exhaust after-treatment efficiency. Hybrid AHP-PROMETHEE II model provides criteria weights and ranks ethanol induction techniques and fuel blends across low, medium, and high engine loads for decision-making. It ensures that the method chosen aligns with goals, such as reducing NOx and soot emissions, optimizing engine performance, enhancing combustion, and minimizing exhaust after-treatment costs for CI engines. According to the research findings, the hybrid AHP-PROMETHEE II model identifies the CI engine operating at medium load with ethanol blending (DE10) and without the use of nanoparticles as the preferred choice. Additionally, AHP-PROMETHEE II (AHP derived criteria weights) and PROMETHEE II (direct rating derived criteria weights) models, suggested DE10 with nanoparticle (DE10_NP) using blending technique at low load and combined blending-fumigation technique with nanoparticles at high load. However, at medium load, PROMETHEE II recommends DE10_NP, while AHP-PROMETHEE II recommends DE10 blending technique. To assess the performance and reliability of this model, the consistency ratio and Spearman's rank correlation coefficient indices were computed, yielding values of 0.05 and 0.59, respectively. 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引用次数: 0
摘要
乙醇和纳米颗粒是追求环境可持续燃料选择的一种有前途的替代品,可为遏制氮氧化物和可吸入颗粒物/烟尘排放的双重挑战提供潜在的解决方案,同时优化发动机性能,以符合压燃式(CI)发动机的严格污染法规。这项研究旨在优化用于 CI 发动机的乙醇燃料诱导技术。它采用了一种混合决策方法,将层次分析法(AHP)用于问题结构化和偏好权重的推导。随后,应用用于丰富评价的偏好排序组织方法--PROMETHEE II,对现有替代方案进行评估和排序。这项研究需要对不同发动机负荷范围内的各种乙醇诱导方法进行有条不紊的评估,并考虑到发动机性能、排放、燃烧行为和尾气后处理效率等多个标准。混合 AHP-PROMETHEE II 模型提供了标准权重,并对低、中、高发动机负荷下的乙醇诱导技术和混合燃料进行了排序,以供决策。该模型可确保所选方法与目标相一致,如减少氮氧化物和烟尘排放、优化发动机性能、增强燃烧以及最大限度地降低 CI 发动机的尾气后处理成本。根据研究结果,AHP-PROMETHEE II 混合模型确定了在中等负荷下工作、掺入乙醇(DE10)且不使用纳米颗粒的 CI 发动机为首选。此外,AHP-PROMETHEE II 模型(AHP 导出标准权重)和 PROMETHEE II 模型(直接评级导出标准权重)建议,在低负荷时使用混合技术的 DE10 与纳米颗粒(DE10_NP),在高负荷时使用混合-熏蒸技术与纳米颗粒相结合。然而,在中等负荷时,PROMETHEE II 建议使用 DE10_NP,而 AHP-PROMETHEE II 建议使用 DE10 混合技术。为评估该模型的性能和可靠性,计算了一致性比率和斯皮尔曼等级相关系数指数,结果分别为 0.05 和 0.59。这两个指数都低于预定的临界值,表明该模型具有较高的一致性。
Optimizing CI Engine Ethanol Fuel Induction Techniques Using the AHP-PROMETHEE II Hybrid Decision Model
Ethanol along with nanoparticles stands out as a promising alternative in the pursuit of environmentally sustainable fuel options, offering a potential solution to the dual challenge of curbing NOx and PM/soot emissions while optimizing engine performance in compliance with stringent pollution regulations for compression ignition (CI) engines. The research study aims to optimize ethanol fuel induction techniques for CI engines. It utilizes a hybrid decision-making approach that integrates the analytic hierarchy process- AHP- for problem structuring and the derivation of preference weights. Subsequently, the preference ranking organization method for enrichment evaluations-PROMETHEE II is applied to assess and rank the existing alternatives. The study entails a methodical assessment of diverse ethanol induction methods across varying engine load ranges, considering multiple criteria including engine performance, emissions, combustion behavior, and exhaust after-treatment efficiency. Hybrid AHP-PROMETHEE II model provides criteria weights and ranks ethanol induction techniques and fuel blends across low, medium, and high engine loads for decision-making. It ensures that the method chosen aligns with goals, such as reducing NOx and soot emissions, optimizing engine performance, enhancing combustion, and minimizing exhaust after-treatment costs for CI engines. According to the research findings, the hybrid AHP-PROMETHEE II model identifies the CI engine operating at medium load with ethanol blending (DE10) and without the use of nanoparticles as the preferred choice. Additionally, AHP-PROMETHEE II (AHP derived criteria weights) and PROMETHEE II (direct rating derived criteria weights) models, suggested DE10 with nanoparticle (DE10_NP) using blending technique at low load and combined blending-fumigation technique with nanoparticles at high load. However, at medium load, PROMETHEE II recommends DE10_NP, while AHP-PROMETHEE II recommends DE10 blending technique. To assess the performance and reliability of this model, the consistency ratio and Spearman's rank correlation coefficient indices were computed, yielding values of 0.05 and 0.59, respectively. Both indices fall below the predetermined threshold limits, indicating a high level of consistency of the model.