scholarly journals The design of an optimal running curve for train operation based on a novel parameterization method aiming to minimize the total energy consumption

2014 ◽  
Author(s):  
V. D. Doan
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Total Energy Consumption ◽  
Parameterization Method ◽  
Train Operation

scholarly journals Research on the Utilization of Metro Regenerative Braking Energy Based on an Improved Differential Evolution Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Di Liu ◽  
Song-Qing Zhu ◽  
Yun-Rui Bi ◽  
Kun Liu ◽  
You-Xiong Xu
Keyword(s):  
Energy Consumption ◽  
Integer Programming ◽  
Differential Evolution ◽  
Total Energy ◽  
Differential Evolution Algorithm ◽  
Programming Models ◽  
Regenerative Braking ◽  
Total Energy Consumption ◽  
Train Operation ◽  
Improved Differential Evolution Algorithm

Urban metro trains have the characteristics of short running distance between stations and frequent starting and braking. A large amount of regenerative braking energy is generated during the braking process. The effective utilization of the regenerative braking energy can substantially reduce the total energy consumption of train operation. In this paper, we establish two integer programming models of train operation that maximize the overlap time between train traction and braking in peak hours and nonpeak hours. On this basis, an improved differential evolution (IDE) algorithm is developed for solving the two integer programming models. The results demonstrate that the overlap time increases by 51.44% after optimization using the IDE algorithm when the headway is set to 154 s in peak hours. The overlap time is further increased by 14.87% by optimizing the headway. In nonpeak hours, the overlap time of traction and braking of the trains in opposite directions at the same station is increased by optimizing the bidirectional departure interval, thereby reducing the total energy consumption of the system.

Energy savings potential of new aeration system: Full scale trials

2012 ◽  
Vol 7 (4) ◽  
Author(s):  
A. Lazić ◽  
V. Larsson ◽  
Å. Nordenborg
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Total Energy ◽  
Energy Savings ◽  
Treatment Plant ◽  
Full Scale ◽  
Total Energy Consumption ◽  
Aeration System ◽  
Fine Bubble ◽  
Aeration Control

The objective of this work is to decrease energy consumption of the aeration system at a mid-size conventional wastewater treatment plant in the south of Sweden where aeration consumes 44% of the total energy consumption of the plant. By designing an energy optimised aeration system (with aeration grids, blowers, controlling valves) and then operating it with a new aeration control system (dissolved oxygen cascade control and most open valve logic) one can save energy. The concept has been tested in full scale by comparing two treatment lines: a reference line (consisting of old fine bubble tube diffusers, old lobe blowers, simple DO control) with a test line (consisting of new Sanitaire Silver Series Low Pressure fine bubble diffusers, a new screw blower and the Flygt aeration control system). Energy savings with the new aeration system measured as Aeration Efficiency was 65%. Furthermore, 13% of the total energy consumption of the whole plant, or 21 000 €/year, could be saved when the tested line was operated with the new aeration system.

scholarly journals Energy Recovery Potential in Industrial and Municipal Wastewater Networks Using Micro-Hydropower in Spain

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 691
Author(s):  
Aida Mérida García ◽  
Juan Antonio Rodríguez Díaz ◽  
Jorge García Morillo ◽  
Aonghus McNabola
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Energy Recovery ◽  
Energy Production ◽  
Municipal Wastewater ◽  
Distribution Networks ◽  
Total Power ◽  
Energy Potential ◽  
Total Energy Consumption ◽  
Recovery Potential

The use of micro-hydropower (MHP) for energy recovery in water distribution networks is becoming increasingly widespread. The incorporation of this technology, which offers low-cost solutions, allows for the reduction of greenhouse gas emissions linked to energy consumption. In this work, the MHP energy recovery potential in Spain from all available wastewater discharges, both municipal and private industrial, was assessed, based on discharge licenses. From a total of 16,778 licenses, less than 1% of the sites presented an MHP potential higher than 2 kW, with a total power potential between 3.31 and 3.54 MW. This total was distributed between industry, fish farms and municipal wastewater treatment plants following the proportion 51–54%, 14–13% and 35–33%, respectively. The total energy production estimated reached 29 GWh∙year−1, from which 80% corresponded to sites with power potential over 15 kW. Energy-related industries, not included in previous investigations, amounted to 45% of the total energy potential for Spain, a finding which could greatly influence MHP potential estimates across the world. The estimated energy production represented a potential CO2 emission savings of around 11 thousand tonnes, with a corresponding reduction between M€ 2.11 and M€ 4.24 in the total energy consumption in the country.

scholarly journals Linear Weighted Regression and Energy-Aware Greedy Scheduling for Heterogeneous Big Data

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 554
Author(s):  
Suresh Kallam ◽  
Rizwan Patan ◽  
Tathapudi V. Ramana ◽  
Amir H. Gandomi
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Total Energy ◽  
Optimal Solution ◽  
Data Locality ◽  
Weighted Regression ◽  
Heterogeneous Environments ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Greedy Scheduling

Data are presently being produced at an increased speed in different formats, which complicates the design, processing, and evaluation of the data. The MapReduce algorithm is a distributed file system that is used for big data parallel processing. Current implementations of MapReduce assist in data locality along with robustness. In this study, a linear weighted regression and energy-aware greedy scheduling (LWR-EGS) method were combined to handle big data. The LWR-EGS method initially selects tasks for an assignment and then selects the best available machine to identify an optimal solution. With this objective, first, the problem was modeled as an integer linear weighted regression program to choose tasks for the assignment. Then, the best available machines were selected to find the optimal solution. In this manner, the optimization of resources is said to have taken place. Then, an energy efficiency-aware greedy scheduling algorithm was presented to select a position for each task to minimize the total energy consumption of the MapReduce job for big data applications in heterogeneous environments without a significant performance loss. To evaluate the performance, the LWR-EGS method was compared with two related approaches via MapReduce. The experimental results showed that the LWR-EGS method effectively reduced the total energy consumption without producing large scheduling overheads. Moreover, the method also reduced the execution time when compared to state-of-the-art methods. The LWR-EGS method reduced the energy consumption, average processing time, and scheduling overhead by 16%, 20%, and 22%, respectively, compared to existing methods.

Optimizing the production scheduling of a single machine to minimize total energy consumption costs

2014 ◽  
Vol 67 ◽  
pp. 197-207 ◽  
Author(s):  
Fadi Shrouf ◽  
Joaquin Ordieres-Meré ◽  
Alvaro García-Sánchez ◽  
Miguel Ortega-Mier
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Single Machine ◽  
Production Scheduling ◽  
Total Energy Consumption
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