Analysis of Approaches to Group Authentication in Large-Scale Industrial Systems

2019 ◽  
Vol 53 (8) ◽  
pp. 879-882
Author(s):  
E. B. Aleksandrova ◽  
A. V. Yarmak ◽  
M. O. Kalinin
Keyword(s):  
Large Scale ◽  
Industrial Systems ◽  
Group Authentication

scholarly journals Application of the Thermodynamic Cycle to Assess the Energy Efficiency of Amine-Based Absorption of Carbon Capture

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2504 ◽  
Author(s):  
Yaofeng Xu ◽  
Shuai Deng ◽  
Li Zhao ◽  
Xiangzhou Yuan ◽  
Jianxin Fu ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Performance Analysis ◽  
Carbon Capture ◽  
Large Scale ◽  
Variable Parameter ◽  
Thermodynamic Cycle ◽  
Co2 Concentration ◽  
Parameter Analysis ◽  
Industrial Systems ◽  
Efficiency Performance

The thermodynamic cycle, as a significant tool derived from equilibrium, could provide a reasonable and rapid energy profile of complicated energy systems. Such a function could strongly promote an in-depth and direct understanding of the energy conversion mechanism of cutting-edge industrial systems, e.g., carbon capture system (CCS) However, such applications of thermodynamics theory have not been widely accepted in the carbon capture sector, which may be one of the reasons why intensive energy consumption still obstructs large-scale commercialization of CCS. In this paper, a kind of thermodynamic cycle was developed as a tool to estimate the lowest regeneration heat (Qre) of a benchmark solvent (MEA) under typical conditions. Moreover, COPCO2, a new assessment indicator, was proposed firstly for energy-efficiency performance analysis of such a kind of CCS system. In addition to regeneration heat and second-law efficiency (η2nd), the developed COPCO2 was also integrated into the existing performance analysis framework, to assess the energy efficiency of an amine-based absorption system. Through variable parameter analysis, the higher CO2 concentration of the flue gas, the higher COPCO2, up to 2.80 in 16 vt% and the Qre was 2.82 GJ/t, when Rdes = 1 and ΔTheat-ex = 10 K. The η2nd was no more than 30% and decreased with the rise of the desorption temperature, which indicates the great potential of improvements of the energy efficiency.

Study on the Particle Behavior in a Screw Conveyer System by the Discrete Element Method

2011 ◽  
Author(s):  
Yusuke Shigeto ◽  
Mikio Sakai ◽  
Shin Mizutani ◽  
Seiichi Koshizuka ◽  
Shuji Matsusaka
Keyword(s):  
Discrete Element Method ◽  
Large Scale ◽  
Discrete Element ◽  
Dem Simulation ◽  
Memory Space ◽  
Industrial Systems ◽  
Simulation Techniques ◽  
Conveyer System ◽  
Grain Model ◽  
Element Method

Large amount of particles are used in the industrial systems. Numerical analyses of these systems are expected to reduce designing cost. However the numerical analysis of powder is not used practically, because it requires high calculation cost which grows up with the number of particles. Besides, there are memory consumption problem which is required for calculation space. In this paper, the parallel simulation techniques of the Discrete Element Method (DEM) on multi-core processors are described. In the present study, it is shown that the algorithm enables all the processes of the DEM to be executed parallel. Moreover, a new algorithm which makes the memory space usage effectively and accelerates the calculation speed is proposed for multi-thread parallel computing of the DEM. In the present study, the memory space usage is shown to be reduced drastically by introducing this algorithm. In addition, the coarse grain model which emulates original particles with less calculation particles is applied in order to reduce calculation cost. For the practical usage of the DEM in industries, the simulation is performed in a large-scale powder system which possesses a complicated drive unit. In the current study, it is shown that the large scale DEM simulation of practical systems is enabled to be executed by our proposing algorithms.

scholarly journals Risk Quantification and Analysis of Coupled Factors Based on the DEMATEL Model and a Bayesian Network

2019 ◽  
Vol 10 (1) ◽  
pp. 317 ◽  
Author(s):  
Jian Jiao ◽  
Mengwei Wei ◽  
Yuan Yuan ◽  
Tingdi Zhao
Keyword(s):  
Risk Factors ◽  
Bayesian Network ◽  
Large Scale ◽  
Assessment Model ◽  
Relationship Matrix ◽  
Industrial Systems ◽  
Large Scale Systems ◽  
Indirect Coupling ◽  
Coupling Characteristic ◽  
The Relationship

With the developing of high integrations in large scale systems, such as aircraft and other industrial systems, there are new challenges in safety analysis due to the complexity of the mission process and the more complicated coupling characteristic of multi-factors. Aiming at the evaluation of coupled factors as well as the risk of the mission, this paper proposes a combined technology based on the Decision Making Trial and Evaluation Laboratory (DEMATEL) model and the Bayesian network (BN). After identifying and classifying the risk factors from the perspectives of humans, machines, the environment, and management, the DEMATEL technique is adopted to assess their direct and/or indirect coupling relationships to determine the importance and causality of each factor; moreover, the relationship matrix in the DEMATEL model is used to generate the BN model, including its parameterization. The inverse reasoning theory is then implemented to derive the probability, and the risk of the coupled factors is evaluated by an assessment model integrating the probability and severity. Furthermore, the key risk factors are identified based on the risk radar diagram and the Pareto rule to support the preventive measurements. Finally, an application of the take-off process of aircraft is provided to demonstrate the proposed method.

Multiobjective Optimization of a Biofuel Supply Chain Using Random Matrix Generators

2020 ◽  
pp. 206-232 ◽  
Author(s):  
Timothy Ganesan ◽  
Pandian Vasant ◽  
Igor Litvinchev
Keyword(s):  
Supply Chain ◽  
Random Matrix ◽  
Large Scale ◽  
Cuckoo Search ◽  
Optimization Techniques ◽  
Computational Results ◽  
Matrix Approach ◽  
Industrial Systems ◽  
Biofuel Supply Chain ◽  
Effective Design

As industrial systems become more complex, various complexities and uncertainties come into play. Metaheuristic-type optimization techniques have become crucial for effective design, maintenance, and operations of such systems. However, in highly complex industrial systems, conventional metaheuristics are still plagued by various drawbacks. Strategies such as hybridization and algorithmic modifications have been the focus of previous efforts to improve the performance of conventional metaheuristics. This work tackles a large-scale multi-objective (MO) optimization problem: biofuel supply chain. Due to the scale and complexity of the problem, the random matrix approach was employed to modify the stochastic generator segment of the cuckoo search (CS) technique. Comparative analysis was then performed on the computational results produced by the conventional CS technique and the improved CS variants.

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