Hydrodynamic studies in fluidized bed with internals and modeling using ANN and ANFIS

被引:18
|
作者
Krishna, M. Sai Vijaya [1 ]
Begum, K. M. Meera Sheriffa [1 ]
Anantharaman, N. [1 ]
机构
[1] Natl Inst Technol Trichy, Dept Chem Engn, Tiruchirappalli 620015, Tamil Nadu, India
关键词
Fluidized bed; Internals; Pressure drop; Fluidization index; ANN; ANFIS; GAS; FLUCTUATION; EXPANSION; BAFFLES;
D O I
10.1016/j.powtec.2016.11.012
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
The hydrodynamics of gas-solid fluidized beds were studied covering a range of hydrodynamic parameters such as inlet air flow rate, bed height, spacing of internal and column diameter with a particular percentage open area of internal. Cross type internals with constant percentage open area were used. The influence of the above mentioned parameters on pressure drop, expansion ratio (R), fluctuation ratio (r), minimum fluidization velocity and Fluidization index (FI) were studied. The pressure drop was found to increase with increase in bed height regardless of presence or absence of internals. But in presence of internals (more numbers or less spacing) pressure drop initially decreases but beyond certain level the resistance offered by internals is more and it caused slight increase in pressure drop. The expansion ratio reduces significantly with decrease in the spacing of internals. The effect of internals on quality of fluidization has been studied. By the introduction of internals, the quality of fluidization in terms of FI had improved. Fluctuation ratio was found to reduce With the use of internals and also decrease with decrease in internal spacing. Correlations for pressure drop and expansion ratio have been developed using significant dimensionless groups. Correlated values of pressure drop and expansion ratio have been compared with their respective experimental values and were found to be in good agreement. The collected experimental data was used in the development of a model using Expert systems such as Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and a comparison was made. Error analysis made for both ANFIS and ANN shows that ANFIS has better ability to model under non-linearity conditions over ANN. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:37 / 45
页数:9
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