scholarly journals Daily activity evaluated from total energy consumption in the chronic stage of myocardial infarction and limiting factors in elder patients.

1991 ◽  
Vol 28 (2) ◽  
pp. 172-181
Author(s):  
Masayoshi Sakakibara ◽  
Manabu Kamegai ◽  
Fumihiko Miyake ◽  
Masahiro Murayama ◽  
Jiro Sugai
Keyword(s):  
Myocardial Infarction ◽  
Energy Consumption ◽  
Total Energy ◽  
Daily Activity ◽  
Limiting Factors ◽  
Total Energy Consumption ◽  
Chronic Stage ◽  
Elder Patients

scholarly journals Using new computer based techniques to optimise energy consumption in agricultural land levelling

2021 ◽  
Author(s):  
Salim Almaliki ◽  
Nasim Monjezi
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Fossil Fuels ◽  
Agricultural Land ◽  
Limiting Factors ◽  
Total Energy Consumption ◽  
Soil Preparation ◽  
Computer Based ◽  
Energy Optimisation ◽  
Optimisation Methods

Land levelling is one of the most energy-demanding steps in soil preparation. There are many limiting factors for a specific land levelling operation, such as fertile topsoil conservation, limited allowed slope, specific cut to fill ratio, etc. These limitations make optimisation problems of land levelling even more complicated. In this research, three computational and evolutionary methods including ICA, PSO, GA along with MLS were utilised as optimisation methods to minimise the soil cut and fill volumes and to determine the preferred levelling plane. The results indicated that ICA had the most efficient solution for the energy optimisation in the land levelling among the other investigated methods by saving 29% (17 GJ) of the total energy consumption compared with MLS. This study deals with optimising the energy consumption during land levelling projects using new computer-based techniques and compares them to the MLS method as a benchmark. All in all, ICA, PSO, and GA performed much better than MLS by saving 29, 17, and 10% of the total energy consumption in their best model (number 1 models), respectively. Nonetheless, with these great capacities for saving energy in developing countries, unfortunately, the lack of education and excess subsidies on fossil fuels nullify these potentials.

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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