Multiobjective Optimization of Diesel Particulate Filter Regeneration Conditions Based on Machine Learning Combined with Intelligent Algorithms

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Intelligent Systems Pub Date : 2024-04-01 DOI:10.1155/2024/7775139
Yuhua Wang, Jinlong Li, Guiyong Wang, Guisheng Chen, Qianqiao Shen, Boshun Zeng, Shuchao He
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Abstract

To reduce diesel emissions and fuel consumption and improve DPF regeneration performance, a multiobjective optimization method for DPF regeneration conditions, combined with nondominated sorting genetic algorithms (NSGA-III) and a back propagation neural network (BPNN) prediction model, is proposed. In NSGA-III, DPF regeneration temperature (T4 and T5), O2, NOx, smoke, and brake-specific fuel consumption (BSFC) are optimized by adjusting the engine injection control parameters. An improved seagull optimization algorithm (ISOA) is proposed to enhance the accuracy of BPNN predictions. The ISOA-BP diesel engine regeneration condition prediction model is established to evaluate fitness. The optimized fuel injection parameters are programmed into the engine’s electronic control unit (ECU) for experimental validation through steady-state testing, DPF active regeneration testing, and WHTC transient cycle testing. The results demonstrate that the introduced ISOA algorithm exhibits faster convergence and improved search abilities, effectively addressing calculation accuracy challenges. A comparison between the SOA-BPNN and ISOA-BPNN models shows the superior accuracy of the latter, with reduced errors and improved R2 values. The optimization method, integrating NSGA-III and ISOA-BPNN, achieves multiobjective calibration for T4 and T5 temperatures. Steady-state testing reveals average increases of 3.14%, 2.07%, and 10.79% in T4, T5, and exhaust oxygen concentrations, while NOx, smoke, and BSFC exhibit average decreases of 8.68%, 12.07%, and 1.03%. Regeneration experiments affirm the efficiency of the proposed method, with DPF regeneration reaching 88.2% and notable improvements in T4, T5, and oxygen concentrations during WHTC transient testing. This research provides a promising and effective solution for calibrating the regeneration temperature of DPF, thus reducing emissions and fuel consumption of diesel engines while ensuring safe and efficient DPF regeneration.

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基于机器学习与智能算法相结合的柴油机微粒过滤器再生条件多目标优化技术
为了减少柴油排放和燃料消耗,提高柴油微粒滤清器(DPF)的再生性能,提出了一种结合非支配排序遗传算法(NSGA-III)和反向传播神经网络(BPNN)预测模型的柴油微粒滤清器(DPF)再生条件多目标优化方法。在 NSGA-III 中,通过调整发动机喷油控制参数来优化 DPF 再生温度(T4 和 T5)、O2、NOx、烟雾和制动油耗(BSFC)。为提高 BPNN 预测的准确性,提出了一种改进的海鸥优化算法(ISOA)。建立了 ISOA-BP 柴油发动机再生条件预测模型来评估适应性。将优化后的燃油喷射参数编程到发动机的电子控制单元(ECU)中,通过稳态测试、柴油微粒滤清器主动再生测试和 WHTC 瞬态循环测试进行实验验证。结果表明,引入的 ISOA 算法收敛速度更快,搜索能力更强,能有效解决计算精度难题。SOA-BPNN 模型和 ISOA-BPNN 模型之间的比较表明,后者的精度更高,误差更小,R2 值更高。集成了 NSGA-III 和 ISOA-BPNN 的优化方法实现了 T4 和 T5 温度的多目标校准。稳态测试显示,T4、T5 和排气氧浓度分别平均增加了 3.14%、2.07% 和 10.79%,而氮氧化物、烟雾和 BSFC 分别平均减少了 8.68%、12.07% 和 1.03%。再生实验证实了所提方法的高效性,在 WHTC 瞬态测试中,DPF 的再生率达到 88.2%,T4、T5 和氧浓度也有显著改善。这项研究为校准柴油微粒滤清器的再生温度提供了一种前景广阔的有效解决方案,从而在确保柴油微粒滤清器安全高效再生的同时,减少柴油发动机的排放和油耗。
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来源期刊
International Journal of Intelligent Systems
International Journal of Intelligent Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
14.30%
发文量
304
审稿时长
9 months
期刊介绍: The International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal explores several fascinating editorials written by today''s experts in the field. Because new developments are being introduced each day, there''s much to be learned — examination, analysis creation, information retrieval, man–computer interactions, and more. The International Journal of Intelligent Systems uses charts and illustrations to demonstrate these ground-breaking issues, and encourages readers to share their thoughts and experiences.
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