scholarly journals Effect of Packing Material Composition on the Aerodynamic Processes in a Wavy Lamellar Plate-Type Biofilter

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 625
Author(s):  
Pranas Baltrėnas ◽  
Tomas Januševičius ◽  
Jonas Kleiza
Keyword(s):  
Mathematical Modeling ◽  
Energy Consumption ◽  
Pressure Drop ◽  
Wood Fiber ◽  
Packing Material ◽  
Experimental Results ◽  
The Other ◽  
Biomass Growth ◽  
Capillary System ◽  
Plate Type

Reducing the pressure drop in biofilters is important for the reduction of the energy consumption of these devices. Usually, the pressure drop increases with time due to the biomass growth within the packing material. The aim of this study was to evaluate the aerodynamic processes in a laboratory-scale wavy lamellar (WL) plate-type biofilter equipped with a capillary system for humidifying the packing material. The packing material of a designed biofilter consisted of wavy polymer plates (WPP) vertically arranged next to each other. The pattern of arrangement of the plates allowed sufficiently large spaces, and therefore, the use of such structure had an impact on a decrease in the pressure drop of the biofilter. WPP were coated with three different kinds of materials, namely (I) wood fiber (WF), (II) non-woven caulking material (NWCM) and WF, and (III) linen material (LM) and WF. The results showed that the composition of the packing material influenced pressure drop of the biofilter. The packing material, which consisted of WPP covered with WF, had the lowest pressure drop compared with the other two packing material compositions. In this study, the experimental results were also compared with the results of the performed mathematical modeling of airflow movement.

scholarly journals DV-DVFS: merging data variety and DVFS technique to manage the energy consumption of big data processing

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hossein Ahmadvand ◽  
Fouzhan Foroutan ◽  
Mahmood Fathy
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Processing Time ◽  
Experimental Results ◽  
The Other ◽  
Data Sets ◽  
Multiple Sources ◽  
Evaluation Phase ◽  
Dynamic Voltage ◽  
Processing Resources

AbstractData variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in the consumption of processing resources such as CPU consumption. This issue has been overlooked in previous works. To overcome the mentioned problem, in the present work, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider two types of deadlines as our constraint. Before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. In the evaluation phase, we have used a set of data sets and applications. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.

scholarly journals DV-DVFS: Merging Data Variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing

2021 ◽  
Author(s):  
Hossein Ahmadvand ◽  
Fouzhan Foroutan ◽  
Mahmood Fathy
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Processing Time ◽  
Experimental Results ◽  
The Other ◽  
Data Sets ◽  
Multiple Sources ◽  
Evaluation Phase ◽  
Dynamic Voltage ◽  
Processing Resources

Abstract Data variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in the consumption of processing resources such as CPU consumption. This issue has been overlooked from previous work. To overcome the mentioned problem, in the present work, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider two types of deadlines as our constraint. Before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. In the evaluation phase, we have used a set of data sets and applications. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.

scholarly journals DV-DVFS: Merging Data variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing

2020 ◽  
Author(s):  
Hossein Ahmadvand ◽  
Fouzhan Foroutan ◽  
Mahmood Fathy
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Processing Time ◽  
Experimental Results ◽  
The Other ◽  
Data Sets ◽  
Multiple Sources ◽  
Evaluation Phase ◽  
Dynamic Voltage ◽  
Processing Resources

Abstract Data variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in the consumption of processing resources such as CPU consumption. This issue has been overlooked from previous work. To overcome the mentioned problem, in the present work, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider two types of deadlines as our constraint. Before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. In the evaluation phase, we have used a set of data sets and applications. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.

Experiment Research of Electric Analogy for Multi-Lateral Wells Considering Frictional Pressure Drop in Wellbore

2011 ◽  
Vol 402 ◽  
pp. 698-701
Author(s):  
Ming Zhu ◽  
Xiao Dong Wu ◽  
Guo Qing Han ◽  
Yu Yuan
Keyword(s):  
Pressure Drop ◽  
Oil Production ◽  
Experimental Results ◽  
The Other ◽  
Experiment Research ◽  
Frictional Pressure Drop ◽  
Similarity Principle ◽  
Electric Analogy

A series of electric analogy experiments are designed based on the water and electricity similarity principle and frictional pressure drop in wellbore. The contribution of every branch of the multi-lateral well on oil production and the impacts of the number and the angle of branches on productivity has been studied. The results show that the productivity increases as the number and the angle of branch increases, but the increasing tendency slows down. The interference between the horizontal hole and the branch on the side with fewer branches is less than that of the other side. Experimental results provide the basis for optimizing morphology of the branch wells.

scholarly journals DV-DVFS: Merging Data variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing

2020 ◽  
Author(s):  
Hossein Ahmadvand ◽  
Fouzhan Foroutan ◽  
Mahmood Fathy
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Processing Time ◽  
Experimental Results ◽  
The Other ◽  
Data Sets ◽  
Multiple Sources ◽  
Evaluation Phase ◽  
Dynamic Voltage ◽  
Processing Resources

Abstract Data variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in the consumption of processing resources such as CPU consumption. This issue has been overlooked from previous work. To overcome the mentioned problem, in the present work, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider two types of deadlines as our constraint. Before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. In the evaluation phase, we have used a set of data sets and applications. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.

scholarly journals DV-DVFS: Merging Data variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing

2020 ◽  
Author(s):  
Hossein Ahmadvand ◽  
Fouzhan Foroutan Foroutan ◽  
Mahmood Fathy
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Processing Time ◽  
Experimental Results ◽  
The Other ◽  
Data Sets ◽  
Multiple Sources ◽  
Evaluation Phase ◽  
Dynamic Voltage ◽  
Processing Resources

Abstract Data variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in consumption of processing resources such as CPU consumption. In this paper, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider a deadline as our constraint and before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. We have used a set of data sets and applications in the evaluation phase. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.

Mathematical Modeling of the Process of Dehydration of Ethanol to Ethylene over Alumina Ring Catalyst in a Tube Reactor

2018 ◽  
Vol 18 (5) ◽  
pp. 76-84 ◽  
Author(s):  
E. V. Ovchinnikova ◽  
S. P. Banzaraktsaeva ◽  
E. A. Kalugina ◽  
V. A. Chumachenko
Keyword(s):  
Mathematical Modeling ◽  
Pressure Drop ◽  
Contact Time ◽  
Internal Surface ◽  
The Other ◽  
Suggested Approach ◽  
Tube Reactor ◽  
Different Dimensions ◽  
The Given ◽  
2D Model

The process of dehydration of ethanol to ethylene at varied dimensions of an alumina ring catalyst was studied using the mathematical 2D model of the tube reactor to determine the equivalent size Req of the catalyst grain that, in turn, determine the efficiency η of the internal surface of the catalyst. The method was suggested for assigning grains with different dimensions to four structure groups depending on the procedure for preparation of samples with identical Req. The system of criteria for identification of catalyst grains with the best characteristics for the given conditions was developed based on the suggested approach. Grain dimensions and the other parameters providing the highest ethylene yield at the lowest pressure drop and shortest contact time were determined.

The Influence of Water Regime on the Performance of Aquatic Plants

1994 ◽  
Vol 29 (4) ◽  
pp. 127-132 ◽  
Author(s):  
Naomi Rea ◽  
George G. Ganf
Keyword(s):  
Deep Water ◽  
Aquatic Plants ◽  
Water Regime ◽  
Optimal Performance ◽  
Experimental Results ◽  
The Other ◽  
Full Potential ◽  
Influence Of Water ◽  
Emergent Species

Experimental results demonstrate bow small differences in depth and water regime have a significant affect on the accumulation and allocation of nutrients and biomass. Because the performance of aquatic plants depends on these factors, an understanding of their influence is essential to ensure that systems function at their full potential. The responses differed for two emergent species, indicating that within this morphological category, optimal performance will fall at different locations across a depth or water regime gradient. The performance of one species was unaffected by growth in mixture, whereas the other performed better in deep water and worse in shallow.

scholarly journals Study on the Variable-Temperature Drying Process of Corn Drying in an Industrial Corn-Drying System Equipped with a Self-Adaptive Control Heat Exchanger

2021 ◽  
Vol 11 (6) ◽  
pp. 2772
Author(s):  
Bin Li ◽  
Zhiheng Zeng ◽  
Xuefeng Zhang ◽  
Ye Zhang
Keyword(s):  
Adaptive Control ◽  
Energy Consumption ◽  
Heat Loss ◽  
Thermal Performance ◽  
Variable Temperature ◽  
The Other ◽  
Drying Process ◽  
Drying Kinetic ◽  
Drying System ◽  
Self Adaptive

To realize energy-saving and efficient industrial grain drying, the present work studied the variable-temperature drying process of corn drying in a novel industrial corn-drying system with a heat recycling and self-adaptive control function. The drying kinetics, thermal performance, heat-loss characteristics and the heat-recycling performance of the drying system under different allocations between flue gas and hot air were investigated, and the optimized drying process was proposed and compared with two constant drying processes. The results showed that the optimized drying process exhibited better drying kinetic and thermal performance than the two constant drying processes. More specifically, the total heat loss, total energy consumption and specific energy consumption of the optimized drying process were ascertained to be 36,132.85 MJ, 48,803.99 MJ and 7290.27 kJ/kg, respectively, which were lower than those of the other two processes. On the other hand, the thermal efficiency of the drying chamber for the optimized drying process was ascertained to be varied within the range of 6.81–41.71%. Overall, the validation results showed that the optimized drying process can significantly improve the drying performance of the drying system.

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