4.5 Article

Estimation of the Solid Circulation Rate in Circulating Fluidized Bed System Using Adaptive Neuro-Fuzzy Algorithm

期刊

ENERGIES
卷 15, 期 1, 页码 -

出版社

MDPI
DOI: 10.3390/en15010211

关键词

artificial neural network (ANN); adaptive neuro fuzzy inference system (ANFIS); circulating fluidized bed combustion (CFBC)

资金

  1. Polish National Agency for Academic Exchange

向作者/读者索取更多资源

Circulating Fluidized Bed gasifiers are commonly used to convert solid fuel into liquid fuel. This study employs Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System to estimate the solid circulation rate in the gasifier, and experimental results demonstrate the superiority of the Adaptive Neuro-Fuzzy Inference System.
Circulating Fluidized Bed gasifiers are widely used in industry to convert solid fuel into liquid fuel. The Artificial Neural Network and neuro-fuzzy algorithm have immense potential to improve the efficiency of the gasifier. The main focus of this article is to implement the Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System modeling approach to estimate solid circulation rate at high pressure in the Circulating Fluidized Bed gasifier. The experimental data is obtained on a laboratory scale prototype in the Chemical Engineering laboratory at COMSATS University Islamabad. The Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System use four input features-pressure, single mean diameter, total valve opening and riser dp-and one output feature mass flow rate with multiple neurons in the hidden layers to estimate the flow of solid particles in the riser. Both Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System model worked on 217 data samples and output results are compared based on their Mean Square Error, Regression analysis, Mean Absolute Error and Mean Absolute Percentage Error. The experimental results show the effectiveness of Adaptive Neuro-Fuzzy Inference System (Mean Square Error is 0.0519 and Regression analysis R-2=1.0000), as it outperformed Artificial Neural Network in terms of accuracy (Mean Square Error is 1.0677 and Regression analysis R-2=0.9806).

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据