Investigating Impacts of Various Operational Conditions on Fuel Consumption and Stop Penalty at Signalized Intersections

The possible volume reduction and stabilization of the sewage sludge associated with the melting process are expected to be greater than with the incineration process. In addition, melted slag can be utilized. However, since the melting process requires a very high temperature to melt inorganics (ash) in the sludge, the technologies to minimize energy consumption, to establish system operation and to prolong durability of facilities should be developed. This paper discusses the auxiliary fuel consumption as follows.(1)Preparation of a model that provides the auxiliary fuel consumption of the melting system on the basis of the mass and heat balances.(2)Evaluation of the auxiliary fuel consumption in the above model using the cake moisture content, the volatile solids of the cake, the dried cake moisture content and the melting temperature as parameters.(3)Examination of the operational conditions for an energy saving melting system based on the results of (1) and (2) above.

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Trajectory Planning Method for Mixed Vehicles Considering Traffic Stability and Fuel Consumption at the Signalized Intersection

Journal of Advanced Transportation ◽

10.1155/2020/1456207 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Shan Fang ◽

Lan Yang ◽

Tianqi Wang ◽

Shoucai Jing

Keyword(s):

Fuel Consumption ◽

Trajectory Planning ◽

Signalized Intersections ◽

Planning Method ◽

Signalized Intersection ◽

Driver Model ◽

Connected Vehicles ◽

Connected Vehicle ◽

Traffic Lights ◽

Trigonometric Model

Traffic lights force vehicles to stop frequently at signalized intersections, which leads to excessive fuel consumption, higher emissions, and travel delays. To address these issues, this study develops a trajectory planning method for mixed vehicles at signalized intersections. First, we use the intelligent driver car-following model to analyze the string stability of traffic flow upstream of the intersection. Second, we propose a mixed-vehicle trajectory planning method based on a trigonometric model that considers prefixed traffic signals. The proposed method employs the proportional-integral-derivative (PID) model controller to simulate the trajectory when connected vehicles (equipped with internet access) follow the optimal advisory speed. Essentially, only connected vehicle trajectories need to be controlled because normal vehicles simply follow the connected vehicles according to the Intelligent Driver Model (IDM). The IDM model aims to minimize traffic oscillation and ensure that all vehicles pass the signalized intersection without stopping. The results of a MATLAB simulation indicate that the proposed method can reduce fuel consumption and NOx, HC, CO2, and CO concentrations by 17%, 22.8%, 17.8%, 17%, and 16.9% respectively when the connected vehicle market penetration is 50 percent.

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Idling fuel consumption and emissions of air pollutants at selected signalized intersections in Delhi

Journal of Cleaner Production ◽

10.1016/j.jclepro.2018.11.275 ◽

2019 ◽

Vol 212 ◽

pp. 8-21 ◽

Cited By ~ 7

Author(s):

Niraj Sharma ◽

PV Pradeep Kumar ◽

Rajni Dhyani ◽

Ch Ravisekhar ◽

K. Ravinder

Keyword(s):

Fuel Consumption ◽

Air Pollutants ◽

Signalized Intersections ◽

Fuel Consumption And Emissions

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Dynamic Eco-Driving Speed Guidance at Signalized Intersections: Multivehicle Driving Simulator Based Experimental Study

Journal of Advanced Transportation ◽

10.1155/2018/6031764 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Peng Chen ◽

Cong Yan ◽

Jian Sun ◽

Yunpeng Wang ◽

Shenyang Chen ◽

...

Keyword(s):

Fuel Consumption ◽

Driving Simulator ◽

Fuel Efficiency ◽

Signalized Intersections ◽

Specific Power ◽

Programming Approach ◽

Time Signal ◽

Connected Vehicle ◽

Dynamic Programming Approach ◽

Driving Speed

Variations in vehicle fuel consumption and gas emissions are usually associated with changes in cruise speed and the aggressiveness of drivers’ acceleration/deceleration, especially at traffic signals. In an attempt to enhance vehicle fuel efficiency on arterials, this study developed a dynamic eco-driving speed guidance strategy (DESGS) using real-time signal timing and vehicle positioning information in a connected vehicle (CV) environment. DESGS mainly aims to optimize the fuel/emission speed profiles for vehicles approaching signalized intersections. An optimization-based rolling horizon and a dynamic programming approach were proposed to track the optimal guided velocity for individual vehicles along the travel segment. In addition, a vehicle specific power (VSP) based approach was integrated into DESGS to estimate the fuel consumption and CO2 emissions. To evaluate the effectiveness of the overall strategy, 15 experienced drivers were recruited to participate in interactive speed guidance experiments using multivehicle driving simulators. It was found that compared to vehicles without speed guidance, those with DESGS had a significantly reduced number of stops and approximately 25% less fuel consumption and CO2 emissions.

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Safe and Fuel-Efficient Voyage Planning for the Northeast Passage by Combining Reliable Ship Performance, Weather and Ice Forecast Models

Volume 7: Polar and Arctic Sciences and Technology ◽

10.1115/omae2020-18025 ◽

2020 ◽

Author(s):

Zhiyuan Li ◽

Jonas W. Ringsberg ◽

Li Ding ◽

Fransisco Rita ◽

Nicolas Fournier ◽

...

Keyword(s):

Fuel Consumption ◽

Risk Mitigation ◽

Forecast Model ◽

Energy System ◽

The Arctic ◽

Operational Conditions ◽

Energy System Model ◽

Forecast Models ◽

Maritime Operations ◽

Ship Performance

Abstract The Northeast Passage in the Arctic between Europe and Asia offers a significantly shorter voyage compared to the Southern route through the Suez Canal. In 2017, the EU research project “Safe maritime operations under extreme conditions: the Arctic case (SEDNA)” was established to perform a comprehensive analysis of Arctic transit shipping and to promote technical solutions for this purpose. This paper is based on the deliverables of the SEDNA project. A voyage planning tool (VPT) for Arctic applications was developed to plan the optimal route regarding ship’s fuel consumption and safety. One of the most advanced metocean and ice forecast model is utilized to provide comprehensive environmental conditions that are synchronized and will be updated frequently during the voyage. The ship energy system model takes into account the various environmental variables as well as ship’s operational conditions to compute the ship performance in both open and ice infested waters. For Arctic operations, specific ice resistance models are implemented in the VPT, and a user has the options of either relying on icebreaker assistance or going for unassisted navigation in part of the entire Arctic passage. Case study voyages of different ship types, route options, staring time, home/destination ports are simulated to demonstrate how various optimal routes are planned and how the transit time and fuel consumption vary. This information is considered being crucial for ship owners for planning their voyages in advance. The continuously updated voyage information from the VPT is particularly helpful for the ship crew if there are specific ship operations and risk mitigation actions that need to be taken care of during the voyage. In addition, this study underlines that a safe and fuel-efficient Arctic passage requires viable voyage planning tools that combine reliable ship performance with weather and ice forecasts.

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Deep Reinforcement Learning Agent with Varying Actions Strategy for Solving the Eco-Approach and Departure Problem at Signalized Intersections

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120931848 ◽

2020 ◽

Vol 2674 (8) ◽

pp. 119-131

Author(s):

Saleh R. Mousa ◽

Sherif Ishak ◽

Ragab M. Mousa ◽

Julius Codjoe ◽

Mohammed Elhenawy

Keyword(s):

Reinforcement Learning ◽

Control Problem ◽

Fuel Consumption ◽

Red Light ◽

Signalized Intersections ◽

Red Light Running ◽

Fuel Savings ◽

Learning Agent ◽

Target Network ◽

Experience Replay

Eco-approach and departure is a complex control problem wherein a driver’s actions are guided over a period of time or distance so as to optimize fuel consumption. Reinforcement learning (RL) is a machine learning paradigm that mimics human learning behavior, in which an agent attempts to solve a given control problem by interacting with the environment and developing an optimal policy. Unlike the methods implemented in previous studies for solving the eco-driving problem, RL does not require prior knowledge of the environment to be learned and processed. This paper develops a deep reinforcement learning (DRL) agent for solving the eco-approach and departure problem in the vicinity of signalized intersections for minimization of fuel consumption. The DRL algorithm utilizes a deep neural network for the RL. Novel strategies such as varying actions, prioritized experience replay, target network, and double learning were implemented to overcome the expected instabilities during the training process. The results revealed the significance of the DRL algorithm in reducing fuel consumption. Interestingly, the DRL algorithm was able to successfully learn the environment and guide vehicles through the intersection without red light running violation. On average, the DRL provided fuel savings of about 13.02% with no red light running violations.

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Environmental Impact of Freight Signal Priority with Connected Trucks

Sustainability ◽

10.3390/su11236819 ◽

2019 ◽

Vol 11 (23) ◽

pp. 6819

Author(s):

Sangjun Park ◽

Kyoungho Ahn ◽

Hesham A. Rakha

Keyword(s):

Fuel Consumption ◽

Signalized Intersections ◽

Simulation Software ◽

Traffic Signal ◽

Connected Vehicle ◽

Transit Signal Priority ◽

Passenger Cars ◽

Microscopic Traffic Simulation ◽

Operational Technique ◽

Fuel Consumption And Emissions

Traffic signal priority is an operational technique employed for the smooth progression of a specific type of vehicle at signalized intersections. Transit signal priority is the most common type of traffic signal priority, and it has been researched extensively. Conversely, the impacts of freight signal priority (FSP) has not been widely investigated. Hence, this study aims to evaluate the energy and environmental impacts of FSP under connected vehicle environment by utilizing a simulation testbed developed for the multi-modal intelligent transportation signal system. The simulation platform consists of VISSIM microscopic traffic simulation software, a signal request messages distributor program, an RSE module, and an Econolite ASC/3 traffic controller emulator. The MOVES model was employed to estimate the vehicle fuel consumption and emissions. The simulation study revealed that the implementation of FSP significantly reduced the fuel consumption and emissions of connected trucks and general passenger cars; the network-wide fuel consumption was reduced by 11.8%, and the CO2, HC, CO, and NOX emissions by 11.8%, 28.3%, 24.8%, and 25.9%, respectively. However, the fuel consumption and emissions of the side-street vehicles increased substantially due to the reduced green signal times on the side streets, especially in the high truck composition scenario.

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Developing an Eco-Driving Application for Semi-Actuated Signalized Intersections and Modeling the Market Penetration Rates of Eco-Driving

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119839960 ◽

2019 ◽

Vol 2673 (5) ◽

pp. 466-477 ◽

Cited By ~ 1

Author(s):

Saleh R. Mousa ◽

Sherif Ishak ◽

Ragab M. Mousa ◽

Julius Codjoe

Keyword(s):

Greenhouse Gas Emissions ◽

Greenhouse Gas ◽

Fuel Consumption ◽

Signalized Intersections ◽

Market Penetration ◽

Automated Vehicles ◽

Gas Emissions ◽

Traffic Demand ◽

The Impact ◽

Connected Automated Vehicles

Eco-driving is one of the most effective techniques for making the transportation sector more sustainable in relation to fuel consumption and greenhouse gas emissions. Eco-driving applications guide drivers approaching signalized intersections to optimize the fuel consumption and reduce greenhouse gas emissions. Unlike pre-timed traffic signals, developing eco-driving applications for semi-actuated signals is more challenging because of variations in cycle length as a result of fluctuations in traffic demand. This paper presents a framework for developing an eco-driving application for connected/automated vehicles passing through semi-actuated signalized intersections. The proposed algorithm takes into consideration the queue effects because of traditional and connected/automated vehicles. Results showed that the fuel consumption for vehicles controlled by the developed model was 29.2% less than for the case with no control. A sensitivity analysis for the impact of market penetration (MP) indicated that the savings in fuel consumption increase with higher MP. Furthermore, when MP is greater than 50%, the model provides appreciable savings in travel times. In addition, the estimated acceleration noise for the vehicles controlled by the algorithms was 21.9% less than for the case with no control. These reductions in fuel consumption and acceleration noise demonstrate the ability of the algorithm to provide more environmentally sustainable semi-actuated signalized intersections.

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Development of a Cassava Harvester

International Journal of Mechanical and Civil Engineering ◽

10.52589/ijmce-twfgvkx1 ◽

2021 ◽

Vol 4 (1) ◽

pp. 12-21

Author(s):

Isinkaye O.D. ◽

Koyenikan O.O. ◽

Osadare T.

Keyword(s):

Fuel Consumption ◽

Field Capacity ◽

Raw Material ◽

Root Damage ◽

Total Cost ◽

Operational Conditions ◽

Standard Procedures ◽

Field Efficiency ◽

Digging Efficiency

Cassava is a major source of food and raw material for domestic and industrial uses in Nigeria. Consequently, the technologies involved in its cultivation from planting to harvesting require proper development. This paper reports the development of a labour-saving technology for harvesting cassava using standard procedures for designing soil engaging implements. Locally available materials were also used in the fabrication of the harvester. Results of trial tests indicate a digging efficiency of 58.9%, fuel consumption of 16 l/ha, field capacity of 0.11 ha/hr, field efficiency of 67.9% and root damage of 43.03%. The total cost of fabricating the machine was 184,000 naira only. Further tests under various soil and operational conditions for improvement and optimization were recommended for the purpose of patenting for commercialization.

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RESEARCH OF ENERGY EFFICIENCY AND REDUCTION OF ENVIRONMENTAL POLLUTION IN FREIGHT RAIL TRANSPORTATION

Transport ◽

10.3846/16484142.2016.1230888 ◽

2016 ◽

Vol 32 (3) ◽

pp. 291-301 ◽

Cited By ~ 5

Author(s):

Sergejus Lebedevas ◽

Stasys Dailydka ◽

Virgilijus Jastremskas ◽

Paulius Rapalis

Keyword(s):

Energy Efficiency ◽

Environmental Pollution ◽

Fuel Consumption ◽

Diesel Engines ◽

Computer Modelling ◽

Freight Transportation ◽

Railway Network ◽

Operational Conditions ◽

Harmful Emissions ◽

Modelling Studies

The publication presents the results arising from the experimental and mathematical modelling studies, which mainly aimed to investigate the selection and optimization of the rational operation modes particular to diesel engines of freight locomotives in the possession of the JSC ‘Lithuanian Railways’ (AB ‘Lietuvos geležinkeliai’). The goal of the optimization is to increase the energy efficiency, reduce fuel consumption and emissions of harmful air pollutants to the environment from diesel engines of locomotives during freight transportation via the main lines of the Lithuanian railway network. A complex energy efficiency and environmental pollution assessment criterion KE–E adjusted for diesel engines of freight locomotives has been suggested. The use of KE–E on the basis of the conducted complex experimental mathematical computer modelling studies has determined that the reserves reducing fuel consumption, harmful emissions and greenhouse gas (CO2) emissions constitute 6÷15% on an average, and in the case of individual railway network lines they go up to 30%. The comparative emission of harmful components per fuel mass (NOx, CO, CH, PM) e´NOx, e´CO, e´CH, e´PM when carrying freight via the main lines of the railway network by trains weighing 3000÷7500 t has been singled out. The results constitute sufficient grounds to organise control of harmful emissions and provide an opportunity to solve practical tasks with a slight tolerance (3÷7%) taking account of diesel engines of locomotives used for transportation. It has been proposed to measure the freight transportation efficiency according to the complex criterion KE–E, indicates the energy efficiency and the assessment of environmental pollution originating from diesel engine of locomotives. The reserves for increasing the efficiency of energy usage have been studied according to the variational mathematical computer modelling data. A methodology for measuring the freight transportation rationality and estimating the optimal indicators has been proposed, using the values of the KE–E criterion for the railways freight transportation, as well as the technology for controlling the traction characteristics as well as energy and ecology indicators of diesel engines of locomotives in operational conditions.

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