Fuzzy-theory-based determination model for researching airport passenger flow radiative zone

2016 ◽  
pp. 973-979
Author(s):  
Wei-Hua Zhang ◽  
Lin Zhou ◽  
Hai-Jian Bai ◽  
Qiang-Qiang Li
Keyword(s):  
Fuzzy Theory ◽  
Passenger Flow ◽  
Radiative Zone

A Novel Fuzzy Theory-Based Differential Protection Scheme for Transmission Lines

2020 ◽  
Vol 12 (8) ◽  
Author(s):  
Mahyar Abasi ◽  
◽  
Ahmad Torabi Farsani ◽  
Arash Rohani ◽  
Arsalan Beigzadeh ◽  
...  
Keyword(s):  
Transmission Lines ◽  
Fuzzy Theory ◽  
Differential Protection ◽  
Protection Scheme

Statistical Analysis of Operation Data and Automatic Control on Fluidized Bed Incineration Plant with Dryer

1993 ◽  
Vol 28 (11-12) ◽  
pp. 341-345
Author(s):  
Shigeki Minami ◽  
Hidekazu Nagasawa ◽  
Yoshinori Saito ◽  
Motoharu Yamagishi ◽  
Masakatsu Hiraoka ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Fluidized Bed ◽  
Fuzzy Theory ◽  
Exhaust Gas ◽  
Furnace Temperature ◽  
Incineration Plant ◽  
Commercial Plant ◽  
Dried Sludge ◽  
Fluidized Bed Incineration ◽  
Plant Control

Continuous operation data were obtained on a fluidized bed incineration plant with dryers, and two autoregressive models were then prepared through statistical analysis of the data. Based on the results, an automatic plant control system using fuzzy theory was designed. An incinerator system of this type is characterized by energy efficiency, for which optimum and stable moisture control of the dried sludge is important. The large difference in time constants between incinerator and dryers makes energy saving difficult. Based on these analyses and design, control operations at a commercial plant with a capacity of 150 wet-tons/day were studied. It was confirmed that reduction of auxiliary fuel consumption and reduction of CO and NOx in the exhaust gas were optimized, while the moisture content of dried sludge and the furnace temperature were kept stable.

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 916-921
Author(s):  
Yuan Yuan ◽  
Wenjun Meng ◽  
Xiaoxia Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fault Location ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Belt Conveyor ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Network Fault

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

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