Bi-level optimization model applications in managing air emissions from ships: A review

Qi, Jingwen; Wang, Shuaian; Psaraftis, Harilaos N.

doi:10.1016/j.commtr.2021.100020

Cited by 41 publications

(17 citation statements)

References 9 publications

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“…For instance, the average speed decelerated to less than 12 knots while approaching the Port of Kaohsiung. Speed reduction has been historically used to reduce fuel consumption and is currently used to control ship emissions 35 – 37 .…”

Section: Resultsmentioning

confidence: 99%

Real-time energy consumption and air pollution emission during the transpacific crossing of a container ship

Yeh¹,

Lin

Shen³

et al. 2022

Sci Rep

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This study presents the real-time energy consumption of a container ship’s generator engine on two round-trips from the West Coast of the US to the East Asian ports and analyzes the ship’s PM10, PM2.5, NOx, SOx, CO, and HC emissions, shore power usage, and factors affecting energy consumption. The average total energy consumption and air emissions for the two round trips were 1.72 GWh and 42.1 tons, respectively. The transpacific crossing segment had the highest average energy consumption (2848 ± 361 kWh) and pollutant emission rate (78.9 ± 10.0 kg h−1). On the other hand, the West Coast of the US had the least energy consumption due to shore power adoption. Furthermore, switching from heavy fuel oil (HFO) to ultra-low-sulfur fuel oil (ULSFO) greatly reduced the emissions of PM and SOx by > 96% and NOx by 17.0%. However, CO and HC increased by 16.9% and 36.1%, respectively, implying incomplete combustion. In addition, the energy consumption was influenced by the number of reefers and wind. Therefore, this study recommends further research on energy-efficient reefers, generator engine optimization, and shore power adoption to reduce emissions from container ships.

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Section: Resultsmentioning

confidence: 99%

Real-time energy consumption and air pollution emission during the transpacific crossing of a container ship

Yeh¹,

Lin

Shen³

et al. 2022

Sci Rep

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show abstract

“…Several different applications of the bi-level optimization model in managing ship emissions are discussed in an article [146]. These applications include the Energy Efficiency Design Index, the Emissions Control Area, the Market-Based Measure, the Carbon Intensity Indicator, and the Vessel Speed Reduction Incentive Program.…”

Section: Related Surveysmentioning

confidence: 99%

“…In order to lower the carbon footprint of data centers, this research focused on decreasing brown energy and increasing renewable energy consumption. The authors [7] proposed a self-adaptive architecture based on microservices and renewable energy for interactive and batch workloads [140][141][142][143][144][145][146].…”

Section: Related Surveysmentioning

confidence: 99%

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

et al. 2022

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Global warming is one of the most compelling environmental threats today, as the rise in energy consumption and CO2 emission caused a dreadful impact on our environment. The data centers, computing devices, network equipment, etc., consume vast amounts of energy that the thermal power plants mainly generate. Primarily fossil fuels like coal and oils are used for energy generation in these power plants that induce various environmental problems such as global warming ozone layer depletion, which can even become the cause of premature deaths of living beings. The recent research trend has shifted towards optimizing energy consumption and green fields since the world recognized the importance of these concepts. This paper aims to conduct a complete systematic mapping analysis on the impact of high energy consumption in cloud data centers and its effect on the environment. To answer the research questions identified in this paper, one hundred nineteen primary studies published until February 2022 were considered and further categorized. Some new developments in green cloud computing and the taxonomy of various energy efficiency techniques used in data centers have also been discussed. It includes techniques like VM Virtualization and Consolidation, Power-aware, Bio-inspired methods, Thermal-management techniques, and an effort to evaluate the cloud data center’s role in reducing energy consumption and CO2 footprints. Most of the researchers proposed software level techniques as with these techniques, massive infrastructures are not required as compared with hardware techniques, and it is less prone to failure and faults. Also, we disclose some dominant problems and provide suggestions for future enhancements in green computing.

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“…Although governments make policies, the effectiveness of the policies depends on the actions of the citizens, who choose their housing locations and influence the traffic demand. Given this characteristic, bilevel optimization models have been widely adopted to study these types of problem, such as housing allocation (Boyce & Mattsson, 1999;Lin et al, 2021;Yin et al, 2017), highway road pricing (labbé et al, 1998), and ship air emissions (Qi et al, 2021). In these bilevel models, the lower level subprogram is a traffic equilibrium problem that provides the network conditions (e.g., travel time) to the upper level subprogram, which addresses the housing allocation, such that the planning objectives are optimized (e.g., total disutility of travelers is minimized).…”

Section: Introductionmentioning

confidence: 99%

Bilevel optimization of a housing allocation and traffic emission problem in a predictive dynamic continuum transportation system

Yang

Wong

et al. 2023

Computer aided Civil Eng

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In recent decades, the effects of vehicle emissions on urban environments have raised increasing concerns, and it has been recognized that vehicle emissions affect peoples’ choice of housing location. Additionally, housing allocation patterns determine people's travel behavior and thus affect vehicle emissions. This study considers the housing allocation problem by incorporating vehicle emissions in a city with a single central business district (CBD) into a bilevel optimization model. In the lower level subprogram, under a fixed housing allocation, a predictive dynamic continuum user‐optimal (PDUO‐C) model with a combined departure time and route choice is used to study the city's traffic flow. In the upper level subprogram, the health cost is defined and minimized to identify the optimal allocation of additional housing units to update the housing allocation. A simulated annealing algorithm is used to solve the housing allocation problem. The results show that the distribution of additional housing locations is dependent on the distance and direction from the CBD. Sensitivity analyses demonstrate the influences of various factors (e.g., budget and cost of housing supply) on the optimized health cost and travel demand pattern.

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Bi-level optimization model applications in managing air emissions from ships: A review

Cited by 41 publications

References 9 publications

Real-time energy consumption and air pollution emission during the transpacific crossing of a container ship

Real-time energy consumption and air pollution emission during the transpacific crossing of a container ship

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

Bilevel optimization of a housing allocation and traffic emission problem in a predictive dynamic continuum transportation system

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