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

Aamer Bilal Asghar, Saad Farooq, Muhammad Shahzad Khurram, Mujtaba Hussain Jaffery and Krzysztof Ejsmont
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Aamer Bilal Asghar: Department of Electrical and Computer Engineering, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
Saad Farooq: Department of Electrical and Computer Engineering, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
Muhammad Shahzad Khurram: Department of Chemical Engineering, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
Mujtaba Hussain Jaffery: Department of Electrical and Computer Engineering, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
Krzysztof Ejsmont: Faculty of Mechanical and Industrial Engineering, Warsaw University of Technology, 02-524 Warsaw, Poland

Energies, 2021, vol. 15, issue 1, 1-17

Abstract: 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 ).

Keywords: artificial neural network (ANN); adaptive neuro fuzzy inference system (ANFIS); circulating fluidized bed combustion (CFBC) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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