The Methodology Development for Improving Energy Utilization and Reducing Fluoride Pollution of the Electroslag Remelting Process: A Review

2020 ◽  
Vol 91 (7) ◽  
pp. 1900634 ◽  
Author(s):  
Sheng‐Chao Duan ◽  
Han-Jie Guo
Keyword(s):  
Electroslag Remelting ◽  
Energy Utilization ◽  
Methodology Development ◽  
Fluoride Pollution

A Review of Methodology Development for Controlling Loss of Alloying Elements During the Electroslag Remelting Process

2019 ◽  
Vol 50 (6) ◽  
pp. 3055-3071 ◽  
Author(s):  
Sheng-Chao Duan ◽  
Xiao Shi ◽  
Fei Wang ◽  
Man-Cang Zhang ◽  
Ye Sun ◽  
...  
Keyword(s):  
Electroslag Remelting ◽  
Alloying Elements ◽  
Methodology Development

Towards Systematization

1977 ◽  
Vol 16 (03) ◽  
pp. 125-130 ◽  
Author(s):  
P. L. Reichertz
Keyword(s):  
Clinical Medicine ◽  
Medical Science ◽  
User Involvement ◽  
Empirical Science ◽  
Target System ◽  
General Strategy ◽  
System Construction ◽  
Formal Approach ◽  
Methodology Development ◽  
Planning Development

Data processing has become an important tool in theoretical and clinical medicine. The main categories of applications are : information analysis, (bio)signal processing and the field of information logistics (information systems).The problems encountered lie in the discrepancy of the basic methods of a formal approach to an empirical science, the complexity of the target system and the system ecology, i.e. the involvement of the user and the system environment during system construction and utilization.Possible solutions to these problems are the application of system techniques, inductive planning, development of medical methodology, development of methods and techniques for user involvement and assessment of motivation and education and educational planning.The necessary general strategy in the development in medical informatics is seen in the continuing systematization of the theoretical and practical approach. It is estimated that this will eventually contribute to the systematization of medical science and practice.

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Recovering waste energy of the combined gas turbine system using paraffin melting

2020 ◽  
Vol 14 (4) ◽  
pp. 7481-7497
Author(s):  
Yousef Najjar ◽  
Abdelrahman Irbai
Keyword(s):  
Melting Process ◽  
Payback Period ◽  
Energy Utilization ◽  
Raw Material ◽  
Heat Transfer Fluid ◽  
Layered System ◽  
Exhaust Gas ◽  
Gas Temperature ◽  
Power Cycle ◽  
Waste Energy

This work covers waste energy utilization of the combined power cycle by using it in the candle raw material (paraffin) melting process and an economic study for this process. After a partial utilization of the burned fuel energy in a real bottoming steam power generation, the exhaust gas contains 0.033 of the initially burned energy. This tail energy with about 128 ºC is partly driven in the heat exchanger of the paraffin melting system. Ansys-Fluent Software was used to study the paraffin wax melting process by using a layered system that utilizes an increased interface area between the heat transfer fluid (HTF) and the phase change material (PCM) to improve the paraffin melting process. The results indicate that using 47.35 kg/s, which is 5% of the entire exhaust gas (881.33 kg/s) from the exit of the combined power cycle, would be enough for producing 1100 tons per month, which corresponds to the production quantity by real candle's factories. Also, 63% of the LPG cost will be saved, and the payback period of the melting system is 2.4 years. Moreover, as the exhaust gas temperature increases, the consumed power and the payback period will decrease.

1924-P: Praliciguat, a Clinical-Stage sGC Stimulator, Improves Insulin Sensitivity, Lipid Tolerance, and Energy Utilization in a Mouse Diet-Induced Obesity Model Housed at Thermoneutrality

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1924-P ◽  
Author(s):  
CHAD SCHWARTZKOPF ◽  
JOHN HADCOCK ◽  
JULIEN ROUX ◽  
GUANG LIU ◽  
COURTNEY SHEA ◽  
...  
Keyword(s):  
Insulin Sensitivity ◽  
Clinical Stage ◽  
Energy Utilization ◽  
Diet Induced Obesity
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