ANN model of co-gasification process in bubbling fluidized bed: Contribution ratio analysis

Abstract

Co-gasification of coal and biomass in a bubbling fluidized bed may offer an effective and economical method to partially replace coal to produce hydrogen, thereby addressing the objectives of achieving a carbon dioxide emissions peak and carbon neutrality. An Artificial neural network (ANN) model was utilized to simulate the hydrogen and other performance of co-gasification in the bubbling fluidized bed. Five types of ANN architectures were tested, including FFBP, CFBP, EFBP, LR, and NARX. Five optimization algorithms were also applied, including L-M, GA, GD, GDX, and PSO, to identify the optimal model to give a more accurate predicate the hydrogen production. The input data were derived from the ultimate analysis of the mixed coal and biomass, the proximate analyzes of mixed coal and biomass, and the operational conditions. The output data consisted of gas composition, carbon conversion, gasification efficiency, total gas yield, syngas yield, low and high heating values of the gas. The LR-PSO with 6 hidden neurons (R2 = 0.9995 and MSE = 0.1357) outperformed other ANN types and optimization algorithms. To further understand the performance of co-gasification, the influences of biomass to coal ratio, equivalence ratio, steam to carbon ratio and temperature on ratio of H2 to CO and syngas yield were predicted. The highest ratio of H2 to CO for the work of Li et al. (2010) [26], Song et al. (2013) [27] and Valdés et al. (2015) [28] were 0.7, 1.2 and 0.88, respectively. And those for syngas yield were 0.85, 1.09 and 1.55 m3/kg, respectively. To reveal the important factor on the ratio of H2 to CO, a detailed analyzed of the contribution ratio was conducted. The most critical factor affecting co-gasification performance in the work of Song et al. (2013) [27] were identified as SC ratio and temperature, which was higher than those of W and ER. The effect of SC exceeded that of T at SC ratio of 0.4, while it came to a reverse at temperature above 700 °C. These findings may serve as a valuable tool for adjusting the various factors to achieve the desired performance in the co-gasification of coal and biomass.