Investigation of enhanced H 2 production from municipal solid waste gasification via artificial neural network with data on tar compounds.

Autor: Jamro IA; School of Environmental Science and Engineering / Tianjin Key Lab of Biomass-wastes Utilization, Tianjin University, Tianjin, 300072, China., Raheem A; Department of Electrical Engineering, Sukkur IBA University, Sindh, Pakistan., Khoso S; School of Engineering, The University of Toledo, Ohio, USA., Baloch HA; School of Engineering RMIT University, Melbourne, Victoria, 3000, Australia., Kumar A; School of Environmental Science and Engineering / Tianjin Key Lab of Biomass-wastes Utilization, Tianjin University, Tianjin, 300072, China., Chen G; School of Environmental Science and Engineering / Tianjin Key Lab of Biomass-wastes Utilization, Tianjin University, Tianjin, 300072, China., Bhagat WA; School of Space and Environment, Beihang University, Beijing, 100191, China., Wenga T; Department of Soil Science and Environment, Faculty of Agriculture Environment and Food Systems, University of Zimbabwe, P.O. Box MP167 Mt Pleasant, Harare, Zimbabwe., Ma W; School of Environmental Science and Engineering / Tianjin Key Lab of Biomass-wastes Utilization, Tianjin University, Tianjin, 300072, China. Electronic address: mawc916@tju.edu.cn.
Jazyk: angličtina
Zdroj: Journal of environmental management [J Environ Manage] 2023 Feb 15; Vol. 328, pp. 117014. Date of Electronic Publication: 2022 Dec 13.
DOI: 10.1016/j.jenvman.2022.117014
Abstrakt: 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 H 2 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 (R 2 ) 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 H 2 production of 42.1 vol% was produced at the higher reaction temperature of 900 °C with LHV of 11.2 MJ/Nm 3 . According to the tar analysis, the dominant compounds were aromatics (17 compounds) followed by polycyclic aromatic, phenyl, aliphatic, aromatic heterocyclic, polycyclic, and aromatic ketone compounds.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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