Multi-objective optimization of a dual fuel CI engine powered with syngas and pilot diesel using TLBO algorithm: A metaheuristic approach

Samar Das, S. K. Tamang
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Abstract

The thermochemical conversion of biomass into producer gas presents an attractive alternative fuel option for compression ignition (CI) engines, making biomass gasification a critical driver for achieving sustainable development goals. Considering the application of producer gas (PG) in CI engine, the most potential gases include H2 and CO as main fuel compounds and it is crucial to comprehensively understand the impact of these two gas components on the engine behaviour. Nowadays, artificial intelligence-powered models are frequently applied for simulating engines that run on a single type of fuel. However, their usage is not as common when it comes to modeling dual-fuel CI engines run on synthetic producer gas or syngas. The present study explores the feasibility of optimizing operational parameters, such as engine load and syngas composition, in improving the efficiency and lowering the levels of pollutants emitted by a 3.5 kW CI engine operated under dual fuel (DF) mode using syngas as primary fuel and diesel as pilot fuel. The performance and emission characteristics of syngas (H2:CO) is examined by studying its behaviour in four different combinations. The compositions of syngas are prepared based on the volumetric percentage of the H2 and CO and is inducted into the combustion chamber using a novel venturi-type air-gas mixer. In the present study, an intelligent metaheuristics-based optimization algorithm i.e., Teaching–Learning Based Optimization (TLBO) is developed and introduced, to develop a predictive model within constrained range of engine operating conditions. Further, the algorithm is used to estimate multiple engine performance characteristics simultaneously viz., brake thermal efficiency (BTE), unburned hydrocarbons (HC), and carbon monoxide (CO). The resultant findings identify the optimal engine load of 68.87% and the ideal syngas composition of 63.9% H2 and 49.5% CO as key parameters for maximizing engine efficiency while minimizing exhaust emission. At these optimized operating condition, 19.49% BTE is observed, while HC and CO emission was found to be 384.6 ppm and 445.33 ppm respectively. This shows the effective and efficiency of the proposed algorithm.
使用 TLBO 算法对以合成气和先导柴油为动力的双燃料 CI 发动机进行多目标优化:元启发式方法
将生物质热化学转化为生产气体为压燃(CI)发动机提供了一种极具吸引力的替代燃料选择,使生物质气化成为实现可持续发展目标的关键驱动力。考虑到生产气(PG)在 CI 发动机中的应用,最有潜力的气体包括作为主要燃料化合物的 H2 和 CO,因此全面了解这两种气体成分对发动机性能的影响至关重要。目前,人工智能模型经常被用于模拟使用单一类型燃料的发动机。然而,在对使用合成生产气或合成气的双燃料 CI 发动机进行建模时,人工智能模型的应用并不普遍。本研究探讨了优化发动机负荷和合成气成分等运行参数的可行性,以提高以合成气为主要燃料、柴油为先导燃料的双燃料(DF)模式下运行的 3.5 千瓦 CI 发动机的效率并降低其污染物排放水平。通过研究合成气(H2:CO)在四种不同组合下的表现,考察了其性能和排放特性。合成气的成分是根据 H2 和 CO 的体积百分比制备的,并使用新型文丘里式空气-气体混合器将其导入燃烧室。在本研究中,开发并引入了一种基于元启发式的智能优化算法,即基于教学-学习的优化算法(TLBO),用于在受限的发动机运行条件范围内开发预测模型。此外,该算法还用于同时估算多种发动机性能特征,即制动热效率 (BTE)、未燃碳氢化合物 (HC) 和一氧化碳 (CO)。结果发现,68.87% 的最佳发动机负荷和 63.9% 的 H2 和 49.5% 的 CO 的理想合成气成分是最大化发动机效率同时最小化废气排放的关键参数。在这些优化运行条件下,BTE 为 19.49%,HC 和 CO 排放量分别为 384.6 ppm 和 445.33 ppm。这表明了所提算法的有效性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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