期刊
JOURNAL OF ENVIRONMENTAL MANAGEMENT
卷 328, 期 -, 页码 -出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jenvman.2022.117014
关键词
Hydrogen (H2); Municipal solid waste (MSW); Artificial neural network (ANN); Gasification; Gasification products; Tar
An artificial neural network (ANN) was used to assess the gasification of municipal solid waste (MSW) and enhance H2 production. The experiments were conducted under different parameters and the input and output variables were tested and trained using the back-propagation algorithm. The results of the training and validation datasets were satisfactory.
An artificial neural network (ANN) is a biologically inspired computational technique that imitates the behavior and learning process of the human brain. In this study, ANN technique was applied to assess the gasification of municipal solid waste (MSW) with the aim of enhancing the H2 production. The experiments were conducted using a horizontal tube reactor under different parameters: temperatures, MSW loadings, residence times, and equivalence ratios. The input and output variables (released gases) were tested and trained using back -propagation algorithm, and the data distribution by K-fold contrivance. The values of the training (80% data) and validation (20% data) dataset were found satisfactory. The values of regression coefficient (R2) for the training phase were lied between 0.9392 and 0.9991, and 0.9363 and 0.993824 for the testing phase. Whereas; the values of root mean square error (RSME) for the training phase were lied between 0.4111 and 0.8422, and between 0.1476 and 0.7320 for the testing phase. Higher H2 production of 42.1 vol% was produced at the higher reaction temperature of 900 degrees C with LHV of 11.2 MJ/Nm3. According to the tar analysis, the dominant com-pounds were aromatics (17 compounds) followed by polycyclic aromatic, phenyl, aliphatic, aromatic hetero-cyclic, polycyclic, and aromatic ketone compounds.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据