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首页> 外文期刊>Energy Conversion & Management >Artificial intelligence assisted MOPSO strategy for discerning the exergy efficiency potential of a methanol induced RCCI endeavour through GA coupled multi-attribute decision making approach
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Artificial intelligence assisted MOPSO strategy for discerning the exergy efficiency potential of a methanol induced RCCI endeavour through GA coupled multi-attribute decision making approach

机译：人工智能辅助MOPSO策略辨别甲醇诱导的RCCI努力通过GA耦合多属性决策方法

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The present study undertakes a comprehensive effort to explore the exergy efficiency-emissions-stability-combustion quality characteristics of premixed methanol with diesel reactivity controlled operation. The com-bustion phasing of the dual fuel operation primarily depends on the methanol participation rate, wherein the peak in-cylinder pressure and heat release rate decreases with increased methanol injection duration. Though simultaneous reductions of NOx and soot were observed in this study, the stability of the operations deteriorates along with unburned hydrocarbon (UHC) and carbon monoxide (CO) with increasing methanol participations resulting in a trade-off situation. Besides, the severe instability of the operation at 50% load causes misfire due to excessive dilution of the charge at higher methanol participation. The study further explores the potential of Gene Expression Programming assisted meta-model coupled Multi-objective Particle Swarm optimization (MOPSO) algorithm based multi-objective optimization endeavour to explore the optimal operational design space considering the multiple responses of exergy efficiency, NOx, PM, UHC, CO, Coefficient of Variance of indicated mean effective pressure (COVIMEP), lowest normalized value (LNV) of indicated mean effective pres-sure. In this present case of study, the optimization endeavor has yielded 350 numbers of Pareto solutions, while only 26 numbers of Pareto solutions were observed in the experimental counterpart. Moreover, the experimental domain of the present study has produced only single set of experiment which can satisfy the respective emission limits of NHC and PM altogether, whereas 13 sets were evident in the optimization study to maintain the NHC and PM footprint under the emission constraints simultaneously. The overall analysis of the Pareto solutions evolved in the optimization study has revealed that to attain the minimum NHC and PM footprints, the penalty of exergy efficiency and CO emissions must be incurred. The overall minimum of NHC footprint in the optimization study was recorded as 4.19 g/kWhr, compared to 6.58 g/kWhr as observed in the experimental endeavor. Similarly, the footprint of minimum PM was discovered as 0.13 g/kWhr in the optimization regime, which was 27% lower than the experimental counterpart. However, an imperceptible penalty of 1% was incurred in exergy efficiency, despite of significant lowering of overall minimum CO emissions by 84% in the optimization endeavor compared to the experimental counterpart.
机译：本研究开展全面努力探讨预混合甲醇的高效率排放 - 柴油反应性控制操作。双重燃料操作的分数阶段主要取决于甲醇参与率，其中圆柱体压力和热释放率的峰值随着甲醇注射持续时间的增加而降低。虽然在本研究中观察到NOx和烟灰同时减少，但操作的稳定性与未燃烧的烃（UHC）和一氧化碳（CO）脱落，随着甲醇参与的增加，导致权衡情况。此外，由于在较高的甲醇参与下，由于过度稀释电荷，因此在50％负荷下的操作的严重不稳定性会导致失火。该研究进一步探讨了基于基因表达编程的潜力，基于基于多目标优化的多目标粒子群耦合多目标粒子群优化（MOPSO）算法，以探讨了考虑到过度效率，NOx，PM的多响应的最佳运营设计空间， UHC，CO，指示平均有效压力（CoVIMEP）的变异系数，指示平均有效预肯定的最低标准化值（LNV）。在本例的研究中，优化致力于产生350个帕累托溶液，而在实验对应物中仅观察到26个帕乙酶溶液。此外，本研究的实验结构域仅生产了一组实验，该实验只能完全满足NHC和PM的各自排放限制，而13套在优化研究中是显而易见的，以同时在排放约束下维持NHC和PM占地面积。帕累托解决方案的总体分析在优化研究中演变，已揭示了达到最低NHC和PM占地面积，必须产生高效率和共同排放的惩罚。优化研究中的NHC足迹的总体最小值记录为4.19g / kWhr，而实验努力中观察到的6.58g / kWhr。类似地，在优化制度中发现最小PM的足迹在优化方案中被发现为0.13g / kWhr，比实验对应物低27％。然而，尽管在优化努力与实验同行相比，尽管总体最低共同排放量显着降低了84％，但在优化努力中的总体最低共同排放量显着降低，因此难以察觉的刑罚。

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