scholarly journals Application of fuzzy logic to moisture control in fluidized bed granulation.

1995 ◽  
Vol 28 (3) ◽  
pp. 282-287 ◽  
Author(s):  
Satoru Watano ◽  
Yoshinobu Sato ◽  
Kei Miyanami
Keyword(s):  
Fuzzy Logic ◽  
Fluidized Bed ◽  
Moisture Control

scholarly journals Heat Transfer Performance in a Superheater of an Industrial CFBC Using Fuzzy Logic-Based Methods

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 919 ◽  
Author(s):  
Krzywanski
Keyword(s):  
Heat Transfer ◽  
Fuzzy Logic ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Fluidized Bed ◽  
Circulating Fluidized Bed ◽  
Practical Significance ◽  
Overall Heat Transfer Coefficient ◽  
Bed Temperature ◽  
The Heat Transfer Coefficient

The heat transfer coefficient in the combustion chamber of industrial circulating flidized bed (CFB) boilers depends on many parameters as it is a result of multifactorial mechanisms proceeding in the furnace. Therefore, the development of an effective modeling tool, which allows for predicting the heat transfer coefficient is interesting as well as a timely subject, of high practical significance. The present paper deals with an innovative application of fuzzy logic-based (FL) method for the prediction of a heat transfer coefficient for superheaters of fluidized-bed boilers, especially circulating fluidized-bed combustors (CFBC). The approach deals with the modeling of heat transfer for the Omega Superheater, incorporated into the reaction chamber of an industrial 670 t/h CFBC. The height above the grid, bed temperature and voidage and temperature, gas velocity, and the boiler’s load constitute inputs. The developed Fuzzy Logic Heat (FLHeat) model predicts the local overall heat transfer coefficient of the Omega Superheater. The model is in good agreement with the measured data. The highest overall heat transfer coefficient is equal 220 W/(m2K) and can be achieved by the SH I superheater for the following inputs l = 20 m, tb = 900 °C, v = 0.95, u = 7 m/s, M-C-R = 100%. The proposed technique is an effective strategy and an option for other procedures of heat transfer coefficient evaluation.

Control of a Fluidized Bed Combustor Using Fuzzy Logic

1996 ◽  
Vol 118 (1) ◽  
pp. 204-209 ◽  
Author(s):  
S. J. Koffman ◽  
R. C. Brown ◽  
R. R. Fullmer
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Control ◽  
Fluidized Bed ◽  
Fuzzy Logic Control ◽  
Fuzzy Controller ◽  
Experimental Results ◽  
Pi Control ◽  
Logic Control ◽  
And Performance ◽  
Fluidized Bed Combustor

Application of fuzzy logic control to a fluidized bed combustor (FBC) is examined. Major aspects of fuzzy control are reviewed, and design of a fuzzy controller for the FBC is described. Selected experimental results are presented, and performance of the fuzzy controller is evaluated through comparisons to results from classical PI control of the combustor.

Fuzzy Logic Control of a Fluidized Bed Combustor

1989 ◽  
Author(s):  
Stephen J. Koffman ◽  
R. Rees Fullmer ◽  
Robert C. Brown
Keyword(s):  
Fuzzy Logic ◽  
Fluidized Bed ◽  
Fuzzy Logic Control ◽  
Logic Control ◽  
Fluidized Bed Combustor

Performance Comparison of Moisture Control in Paper Industry Using Soft Computing Techniques

2014 ◽  
Vol 573 ◽  
pp. 322-327
Author(s):  
M. Senthil Kumar ◽  
K. Mahadevan
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Dynamic Performance ◽  
Paper Industry ◽  
Performance Comparison ◽  
Peak Time ◽  
Moisture Control ◽  
Soft Computing Techniques ◽  
Tuning Methods ◽  
Fuzzy Logic Technique

In this paper, Genetic Algorithm (GA) method has been applied in the moisture control system for auto tuning (PID) parameters. Proportional – Integral – Derivatives control scheme is used to provide an efficient and quiet easier in control engineering applications. Most of the PID tuning methods are used as manually which is difficult and time consuming. Genetic Algorithm which leads to improve the efficiency of tuning of process. The proposed algorithm is used to tune the PID parameters and its performance has been compared with Fuzzy logic techniques.Compare to the fuzzy logic technique dynamic performance specfications such as rise time, peak time and peak overshoot optimal values produced by GA. The plant model represented by the transfer function is obtained by the system identification tool box.

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