Application of general regression neural network to model a novel integrated fluidized bed gasifier. (15th March 2018)

• Record Type: Journal Article
• Authors: Li, Guang; Liu, Zheyu; Li, Junguo; Fang, Yitian; Liu, Tao; Mei, Yangang; Wang, Zhiqing
• Is Part Of: International journal of hydrogen energy. Volume 43:Number 11(2018)
• Page Start: 5512
• Page End: 5521
• Publication Date: 2018-03-15
• Subjects: General regression neural network -- Multivariate nonlinear regression -- Integrated fluidized bed -- Coal gasification -- Hydrogen; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2018.01.130
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: In order to better understand gasification performance, a general regression neural network (GRNN) was developed to model a novel integrated fluidized bed (IFB) gasifier to research the correlative relationship between the input and output parameters of the IFB gasifier. Additionally, the prediction accuracy of the GRNN model was compared with the multivariate nonlinear regression (MNR) method. The performances of the two methods were evaluated using the mean relative error (MRE), the root mean square error (RMSE) and the coefficient of determination (R 2 ). The GRNN model simulated the IFB gasifier with a higher R 2, a lower RMSE and a lower MRE demonstrating the prediction accuracy of the GRNN model over the MNR method. Furthermore, the effects of the oxygen to coal ratio, the steam to coal ratio, the oxygen to fly ash ratio and the steam to fly ash ratio on gasification performance were analyzed using the proposed GRNN model. Highlights: A GRNN model was firstly applied to research the IFB gasifier. Prediction values of the GRNN model matched well with the experimental data. The GRNN model outperformed the MNR method. The effects of operating conditions were investigated using the GRNN model.