Prediction of MSW pyrolysis products based on a deep artificial neural network
Copyright © 2024 Elsevier Ltd. All rights reserved.
| Veröffentlicht in: | Waste management (New York, N.Y.). - 1999. - 176(2024) vom: 15. Feb., Seite 159-168 |
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| Weitere Verfasser: | , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | Waste management (New York, N.Y.) |
| Schlagworte: | Journal Article Artificial neural network Database MSW Products prediction Pyrolysis Solid Waste |
| Zusammenfassung: | Copyright © 2024 Elsevier Ltd. All rights reserved. Pyrolysis is a promising method for recovering resources and energy products from municipal solid waste (MSW). Predicting MSW pyrolysis products is crucial for establishing an efficient pyrolysis system for resource recovery. In this study, a database was established based on MySQL to record relevant information on MSW pyrolysis, which includes the MSW ultimate analysis results, proximate analysis results, parameters of pyrolysis operation and yields of pyrolysis products, etc. Based on the database and with help of a deep artificial neural network (ANN) which contains 10 hidden layers, a prediction model was successfully established to predict the yield of char, liquid and gas products from MSW pyrolysis. The results showed that the coefficients of determination for predicting the yields of char, liquid and gas from the MSW pyrolysis are 0.841, 0.84, and 0.85, respectively; these values demonstrate an accuracy comparable to that achieved for product prediction from single biomass, indicating a successful model performance. The results also show that ash content and temperature are the most important input factors influencing the outputs, namely, yields of char, liquid and gas. The results of this study can help to achieve a more efficient design of the pyrolysis system and improve the recovery of the desired pyrolysis products |
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| Beschreibung: | Date Completed 14.02.2024 Date Revised 14.02.2024 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1879-2456 |
| DOI: | 10.1016/j.wasman.2024.01.026 |