Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach
| العنوان: | Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach |
|---|---|
| المؤلفون: | Christiaan Richter, Sahar Safarian, Seyed Mohammad Ebrahimi Saryazdi, Runar Unnthorsson |
| المساهمون: | Iðnaðarverkfræði-, vélaverkfræði- og tölvunarfræðideild (HÍ), Faculty of Industrial Eng., Mechanical Eng. and Computer Science (UI), Verkfræði- og náttúruvísindasvið (HÍ), School of Engineering and Natural Sciences (UI), Háskóli Íslands, University of Iceland |
| المصدر: | Fermentation, Vol 7, Iss 71, p 71 (2021) Fermentation; Volume 7; Issue 2; Pages: 71 |
| بيانات النشر: | MDPI AG, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Artificial neural network, Downdraft, biomass gasification, artificial neural network, hydrogen production, downdraft, simulation, Mean squared error, Fermentation industries. Beverages. Alcohol, 020209 energy, Biomass, Biomass gasificatio, 02 engineering and technology, Plant Science, 010501 environmental sciences, 01 natural sciences, Biochemistry, Genetics and Molecular Biology (miscellaneous), Lífmassi, 0202 electrical engineering, electronic engineering, information engineering, Range (statistics), Biohydrogen, Gaskennd efni, 0105 earth and related environmental sciences, Hydrogen production, TP500-660, Wood gas generator, Hermilíkön, Orkuframleiðsla, Volumetric flow rate, Environmental science, Biological system, Simulation, Food Science |
| الوصف: | In order to accurately anticipate the proficiency of downdraft biomass gasification linked with a water–gas shift unit to produce biohydrogen, a model based on an artificial neural network (ANN) approach is established to estimate the specific mass flow rate of the biohydrogen output of the plant based on different types of biomasses and diverse operating parameters. The factors considered as inputs to the models are elemental and proximate analysis compositions as well as the operating parameters. The model structure includes one layer for input, a hidden layer and output layer. One thousand eight hundred samples derived from the simulation of 50 various feedstocks in different operating situations were utilized to train the developed ANN model. The established ANN in the case of product biohydrogen presents satisfactory agreement with input data: absolute fraction of variance (R2) is more than 0.999 and root mean square error (RMSE) is lower than 0.25. In addition, the relative impact of biomass properties and operating parameters on output are studied. At the end, to have a comprehensive evaluation, variations of the inputs regarding hydrogencontent are compared and evaluated together. The results show that almost all of the inputs show a significant impact on the smhydrogen output. Significantly, gasifier temperature, SBR, moisture content and hydrogen have the highest impacts on the smhydrogen with contributions of 19.96, 17.18, 15.3 and 10.48%, respectively. In addition, other variables in feed properties, like C, O, S and N present a range of 1.28–8.6% and proximate components like VM, FC and A present a range of 3.14–7.67% of impact on smhydrogen. This paper was a part of the project funded by Icelandic Research Fund (IRF), (in Icelandic: Rannsoknasjodur) and the grant number is 196458-051. |
| وصف الملف: | application/pdf |
| تدمد: | 2311-5637 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::20f528f952ed85970e89c122de560196 https://doi.org/10.3390/fermentation7020071 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....20f528f952ed85970e89c122de560196 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23115637 |
|---|