Enhancing co-gasification gas yield prediction in downdraft gasifiers through statistical correction strategy

Abstract

Compared to typical gasification methods, the co-gasification method can change syngas composition by adjusting feedstocks and mixing ratio, but predicting gas yield in co-gasification is often more difficult. As a typical gasification equipment, the downdraft gasifier is commonly used to conduct experimental studies on co-gasification. For this reason, a novel co-gasification gas yield prediction method combined with the statistical correction strategy is proposed utilizing a downdraft gasifier as the gasification equipment. The statistical correction strategy is implemented by the statistically based error correction method and uncertainty classification of gasification conditions. The co-gasification dataset of the downdraft gasifier was built to test the proposed method. The results demonstrate that the statistical correction strategy is effective, and the proposed method can forecast co-gasification gas yields accurately, with prediction models achieving R2 of 0.9418, 0.9842, 0.8952, and 0.9822 for H2, CH4, CO, and CO2 yields, respectively.