Measuring Bulk Product Transportation Fuel Efficiency

Baumel, C. Phillip

doi:10.5399/osu/jtrf.50.1.2666

Cited by 4 publications

(4 citation statements)

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“…This is particularly true for non-electrified lines, where consumption is normally controlled simply by measuring the fuel level at the tank after each service. 3 However, even in electrified lines the most common trend regarding energy supply is for electric companies to measure (and charge) the energy provided from their substations to railway companies, which includes not only the energy used for traction, but also that of auxiliary services, infrastructure, power losses, etc. Consequently, the exact energy consumed by trains in real time is usually unknown.…”

Section: Introductionmentioning

confidence: 99%

Neural networks for modelling the energy consumption of metro trains

Fernández

Zuriaga

Sanchís

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part F:

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This paper presents the application of machine learning systems based on neural networks to model the energy consumption of electric metro trains, as a first step in a research project that aims to optimise the energy consumed for traction in the Metro Network of Valencia (Spain). An experimental dataset was gathered and used for training. Four input variables (train speed and acceleration, track slope and curvature) and one output variable (traction power) were considered. The fully trained neural network shows good agreement with the target data, with relative mean square error around 21%. Additional tests with independent datasets also give good results (relative mean square error = 16%). The neural network has been applied to five simple case studies to assess its performance – and has proven to correctly model basic consumption trends (e.g. the influence of the slope) – and to properly reproduce acceleration, holding and braking, although it tends to slightly underestimate the energy regenerated during braking. Overall, the neural network provides a consistent estimation of traction power and the global energy consumption of metro trains, and thus may be used as a modelling tool during further stages of research.

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Section: Introductionmentioning

confidence: 99%

Neural networks for modelling the energy consumption of metro trains

Fernández

Zuriaga

Sanchís

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part F:

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“…In this manner, all three modes were evaluated on the same scale, allowing the use of the DEA for benchmarking routes. For the calculation of fuel consumption, Baumel (2011) and Baumel et al (2015) compared several methods, including the proposed by TTI ( 2007), and proved that the best way to estimate fuel consumption for bulk freight transportation is specifically collecting data from each route. However, due to the purpose of this paper, it was not physically possible to locally collect data.…”

Section: Bij 252mentioning

confidence: 99%

Benchmarking freight transportation corridors and routes with data envelopment analysis (DEA)

Melo

Alves

Périco

et al. 2018

BIJ

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Purpose The purpose of this paper is to collectively measure and compare the efficiency of Brazilian and American soybean transport corridors, from farmers to export ports, using the data envelopment analysis (DEA). Design/methodology/approach This paper aims to determine routes from main producing micro-regions to main export ports, specifically using slack-based measure and variables that represent the three pillars of sustainability (economic, social, and environmental). The choice of variables was guided by literature review and analyzed through the principal component analysis. After the application of the model, the quantitative tiebreaking method of the composite index is applied. Findings The findings are coherent with a global report that compares soybean transportation in both countries (Brazil and USA). Efficient routes and corridors tend to present short distance truck trips and long distance train or barge trips. The efficiency of the inland waterway trips depends on how many barges are used in the same expedition. Routes with more than three modes tend to be inefficient which suggest that there is a limit for multimodality. Originality/value Corridor benchmarking is a rare topic in the literature and previous works normally focus on some specific and limited corridor performance characteristics, such as cost. The main contribution of this research is that it expands the discussion regarding corridor benchmarking and it focuses on efficiency as a whole. The paper also proposes a method that can be applied in different logistics contexts, like expanding the study to different countries. More specifically, this method could be used in infrastructure investments programs.

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“…In fact, in many railway networks it is still common for operators to ignore the actual, segregated energy consumption (and associated costs) of their trains: only overall consumption is known. This is especially true for diesel trains, whose energy consumption is often controlled simply by measuring the fuel level in the tank before and after each service (Baumel, 2011). In the case of electrified lines, usually the electric companies charge the energy supplied from each substation to the railway network, and thus the railway operator does not know which fraction is used for traction and which for heating, lighting, etc.…”

Section: Introductionmentioning

confidence: 99%