Soft computing and fractal theory for industrial applications

2005 ◽  
Author(s):  
O. Castillo ◽  
P. Melin
Keyword(s):  
Soft Computing ◽  
Fractal Theory ◽  
Industrial Applications

Two-Way Coupling Model for Fractal-Like Agglomerate-Laden Multiphase Flow

2009 ◽  
Author(s):  
Mingzhou Yu ◽  
Jianzhong Lin ◽  
Kai Zhang ◽  
Gaohui Zhang
Keyword(s):  
Ultrafine Particles ◽  
Particle System ◽  
Fractal Theory ◽  
Ultrafine Particle ◽  
Coupling Model ◽  
Industrial Applications ◽  
Particle Coagulation ◽  
Governing Equations ◽  
Dilute Gas ◽  
General Dynamic

The disturbing of ultrafine particles on carrying phase is usually ignored in studies on dilute ultrafine particle systems. In a dense fractal-like agglomerate system, however, the effect of particle phase on carrier phase should be considered due to its unique heat and mass transfer while the corresponding mathematical model for this problem has not been established. By taking the agglomerate-laden suspension as a single pseudo fluid, we propose a two-way coupling model in which the developed governing equations for suspension and particle general dynamic equation (PGDE) for particle coagulation and breakage due to turbulence are simultaneously solved. The Taylor-expansion moment method has been applied to solve fractal-like aggregate process and dilute gas-to-particle conversion, and in this study it is further extended to close the PGDE equation by invoking fractal theory. The newly coupling model is not limited in dilute particle system, and thus it is expected to play important roles in studying dense fractal-like agglomerate synthesis or other particulate industrial applications.

scholarly journals Towards Robustness in Neural Network Based Fault Diagnosis

2008 ◽  
Vol 18 (4) ◽  
pp. 443-454 ◽  
Author(s):  
Krzysztof Patan ◽  
Marcin Witczak ◽  
Józef Korbicz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Soft Computing ◽  
Analytical Techniques ◽  
Industrial Applications ◽  
Nonlinear Behaviour ◽  
Design Problems ◽  
Modelling Uncertainty ◽  
Soft Computing Techniques

Towards Robustness in Neural Network Based Fault DiagnosisChallenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as neural networks become more and more popular in industrial applications of fault diagnosis. Taking into account the two crucial aspects, i.e., the nonlinear behaviour of the system being diagnosed as well as the robustness of a fault diagnosis scheme with respect to modelling uncertainty, two different neural network based schemes are described and carefully discussed. The final part of the paper presents an illustrative example regarding the modelling and fault diagnosis of a DC motor, which shows the performance of the proposed strategy.

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