Optimizing the Rail Profile for High-Speed Railways Based on Artificial Neural Network and Genetic Algorithm Coupled Method

Jiang, Hanwen; Gao, Liang

doi:10.3390/su12020658

Cited by 15 publications

(5 citation statements)

References 27 publications

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“…It is well known that there are many methods for solving optimization models. For highly complex and large-scale optimization models, an optimization algorithm is often used, such as the neural network algorithm [34][35][36], tabu search algorithm [22], genetic algorithm [9,14,18,25,36,37], or simulated annealing algorithm [11,21,24]. However, the process of improving the applicability of the algorithm to multi-objective optimization problems remains to be further studied.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Optimization for High-Speed Railway Network Based on “Demand–Supply–Management” Model

Hu,

Bian,

Lin

et al. 2023

Applied Sciences

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This paper develops a multi-objective optimization model for national high-speed railway network planning. Three objectives are proposed from the macro, meso, and micro perspectives, and these three objectives are considered simultaneously. Using real data, case studies are conducted to optimize China’s “four east–west and four north–south railway lines” network, which includes Beijing, Shanghai, Wuhan, Guangzhou, and Zhengzhou as main hubs. The results show that, on the one hand, the optimization model reduces the overlap between long-distance passengers and short-distance passengers on the high-speed railway line, facilitates the travel of passengers, and improves the line service capability; on the other hand, optimization of the network shortens the travel time of the passengers, reduces the cost of the railway, and improves the operation efficiency of the high-speed railway line network. The results show that the total travel time of all high-speed railway passengers in the optimization model is reduced by 18.4%, while the benefit rate of the operator increased by 21.99%.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Optimization for High-Speed Railway Network Based on “Demand–Supply–Management” Model

Hu,

Bian,

Lin

et al. 2023

Applied Sciences

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“…This method can be classified as direct neuro-control [34]. In this direct-neuro controller control method, the set-point, in the form of the phenol and flavonoid parameters, is optimized using the ANN model and a genetic algorithm [31,35,36] to obtain new extraction temperature and time. The input in the form of temperature and extraction time is the new set-point that must be maintained by the extractor.…”

Section: Concept Of Applying the Model Into The Control Systemmentioning

confidence: 99%

Machine Learning Model for Quality Parameters Prediction and Control System Design in the Kecombrang Flower (Etlingera elatior) Extraction Process

et al. 2022

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Kecombrang flowers have bioactive components that can be used as food additives. The development of the kecombrang functional food industry for the production of food additives requires information on production parameters. The extraction process for kecombrang to obtain bioactive components, especially phenols and flavonoids, requires maximum temperature treatment and extraction time. This study aims to determine the standard for the kecombrang flower extraction process, create a machine learning model to estimate the quality parameters of the extraction results (phenol, flavonoid, pH, color, and viscosity), and design a strategy for controlling the extraction machine work to maintain the quality of the extraction, especially of phenols and flavonoids. This research was conducted at extraction temperatures of 60 °C, 65 °C, 70 °C, and 75 °C. During the extraction process, the quality of the material was checked by measuring phenol and flavonoid contents, as well as color, pH, and viscosity. Sampling was carried out at 5 min intervals. The data on the quality parameters during the extraction process were analyzed for trends. A machine learning model, which is an artificial neural network, was developed using a 2–6–1 architecture for each quality parameter. The two inputs of ANN were temperature of extraction and extraction time (duration). The output was the quality parameters of the products (phenols, flavonoids, pH, viscosity, and color), which were evaluated separately. The results show a good correlation between the model and the experimental data, with both the training dataset and the testing dataset. These results were then used to formulate a strategy for controlling the extraction process. A neuro-control system was used as a strategy. This control system was adaptive to changes that occurred during the extraction process so that phenols and flavonoids could be maintained.

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“…Küresel talep ve demiryolu ağlarının kullanımındaki artış, toplam yük/yolcu kapasitesinin artması ile aks ağırlıklarında ve tren hızlarında artışı beraberinde getirmektedir [2,3]. Bu da kaçınılmaz olarak ray bileşenlerinin ömründe azalmaya ve ray bakım maliyetlerinde artışa yol açmaktadır [4][5][6]. Artan ray tekerlek temas döngüleri, aşınma ve yorgunluk kusurlarının daha hızlı ve daha sık görülmesi ile servis ömrünün azalması tren işletilmesinde riski arttıran önemli bir faktördür [7][8][9].…”

Section: Introductionunclassified

Metalografik İncelemelerle Ray Yüzeyindeki Soyulmaların Nedeninin Araştırılması

Uğur¹,

Davut²,

Bacaci³

2020

European Journal of Science and Technology

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Railways are increasingly used for passenger and freight transportation worldwide. This increased traffic causes more quick and frequent formation of defects on train tracks. In this study, rail sets used in Bursa province, that had been reported to experience rapid wear and scaling, were examined. The rail sets were manufactured from R260 steel and the problem occurs more frequently on curves. For comparison, rail sets from straight lines were also examined. After visual examination, specimens from gauge and field corner regions were taken and prepared for metallographic examination. The microstructure of the specimens was examined under optical and scanning electron (SEM) microscopes. Moreover, Vickers micro-hardness tests were also performed. Micro-chemical analyses were performed using an energy dispersive X-ray spectroscopy (EDS) system attached to the SEM. For macro-chemical analysis, an optical emission spectrometer (OES) was used. The cross section of the rails was macro-etched for macro-examination. The results show that the microstructure of the rails is composed of 100% pearlite. On the other hand, the pearlitic structure degenerates at parts of the rail which are under loading. Due to severe plastic deformation, the pearlitic structure changes into a fiber-like structure composed of sub-micron ferrite and cementite. Those degenerated pearlitic regions have higher hardness due to strain hardening and cause crack initiation. Those cracks can then grow into inner sections of the rail easily due to the presence of non-metallic inclusions in the microstructure. Iron oxide particles were found in the vicinity of cracks; which indicate that cracks do not grow rapidly, they rather grow in time according to usage and atmospheric conditions. The examinations revealed that the wear, cracks and scaling problems were formed due to rolling contact fatigue (RCF). The presence of inclusions enhanced the growth of RCF-cracks and later cause "shelling". In order to delay or prevent this problem, using higher strength rails at critical locations such as curves, controlling and rating the non-metallic inclusions in accordance with the international standards, checking the lubrication and maintenance strategies of rails and wheels, working on optimization of rail-wheel contact characteristics are recommended. The last recommendation involves developing a sustainable model by identifying technical limitations of the line and cruise depending on vehicle characteristics.

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Optimizing the Rail Profile for High-Speed Railways Based on Artificial Neural Network and Genetic Algorithm Coupled Method

Cited by 15 publications

References 27 publications

Multi-Objective Optimization for High-Speed Railway Network Based on “Demand–Supply–Management” Model

Multi-Objective Optimization for High-Speed Railway Network Based on “Demand–Supply–Management” Model

Machine Learning Model for Quality Parameters Prediction and Control System Design in the Kecombrang Flower (Etlingera elatior) Extraction Process

Metalografik İncelemelerle Ray Yüzeyindeki Soyulmaların Nedeninin Araştırılması

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