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

Aamer Bilal Asghar,Saad Farooq,Muhammad Shahzad Khurram,Krzysztof Ejsmont,Mujtaba Hussain Jaffery

doi:10.3390/en15010211

Aamer Bilal Asghar, Saad Farooq + Show 3 more

Open Access

https://doi.org/10.3390/en15010211

Copy DOI

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 R2=1.0000), as it outperformed Artificial Neural Network in terms of accuracy (Mean Square Error is 1.0677 and Regression analysis R2=0.9806).

Highlights

With the discovery of fossil fuels, they have become the most utilized medium to produce energy very rapidly
TheThe error between actual andand testing results areare compared andand shown in form of mean squared errorerror (MSE)
We found that proposed adaptive neuro fuzzy inference system (ANFIS) model gives more accurate results and value of MSE error is very low and regression analysis is approaching to 1

Summary

Introduction

With the discovery of fossil fuels, they have become the most utilized medium to produce energy very rapidly. The relentless consumption of fossil fuels has been causing many problems including global warming, energy crises, air and water pollutions and many other environmental hazards, noticed and warned by many international organizations. Renewable energy, which includes biomass energy, wind energy, hydro energy and solar energy among many others, is a reasonable approach to solve above mentioned problems [1,2]. In all these alternative energy sources, the biomass energy entices attention for its numerous benefits like neutralization of CO2 which makes environment clean. There are various forms of energy, categorized as renewable energy, non-renewable energy and nuclear energy

Methods

Results

Conclusion