Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

Browse

Filters

2012 - 201912020 - 20211

Settings

Subject: search.filters.subject.Bottom ash

Search Results

Now showing 1 - 2 of 2
  • Article
    Citation - WoS: 25
    Citation - Scopus: 27
    Characteristic Properties and Recyclability of the Aluminium Fraction of Mswi Bottom Ash
    (Elsevier, 2021) Gökelma, Mertol; Vallejo-Olivares, Alicia; Tranell, Gabriella
    The increasing use of aluminimum in packaging applications results in many different aluminium-based products ending up in consumer mixed-waste bins. This waste is typically incinerated, generating an aluminium-containing bottom ash. The current work investigates the recyclability of the aluminium fraction in the bottom ash from waste incineration plants in the USA, UK and Denmark. Incinerated Al samples from different size fractions (2-6 mm, 6-12 mm and 12-30 mm) were characterized in terms of inherent oxide thickness, re-melting yield/coagulation and composition. The measured average oxide thickness on Al particles was 68 mm (SD=100), with the metal yield and coagulation efficiency measured to between 76 and 92% and 87-99% respectively. Larger particle size fractions resulted in a higher metal yield due to their higher mass to surface ratio. A simplified model correlating metal yield and particle size was proposed. The aluminium content of the melted material was determined to between 95.6 and 98.5% with main impurities being Fe, Si, Mn, Zn, Mg and Cu, corresponding to major aluminium alloying elements and waste charge components. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
  • Article
    Citation - WoS: 37
    Citation - Scopus: 38
    Prediction of the Bottom Ash Formed in a Coal-Fired Power Plant Using Artificial Neural Networks
    (Elsevier Ltd., 2012) Bekat, Tuğçe; Erdoğan, Muharrem; İnal, Fikret; Genç, Ayten
    he amount of bottom ash formed in a pulverized coal-fired power plant was predicted by artificial neural network modeling using one-year operating data of the plant and the properties of the coals processed. The model output was defined as the ratio of amount of bottom ash produced to amount of coal burned (Bottom ash/Coal burned). The input parameters were the moisture contents, ash contents and lower heating values of the coals. The total 653 data were divided into two groups for the training (90% of the data) and the testing (10% of the data) of the network. A three-layer, feed-forward type network architecture with back-propagation learning was used in the modeling study. The activation function was sigmoid function. The best prediction performance was obtained for a one hidden layer network with 29 neurons. The learning rate and the tolerance value were 0.2 and 0.05, respectively. R2 (coefficient of determination) values between the actual (Bottom ash/Coal burned) ratios and the model predictions were 0.988 for the training set and 0.984 for the testing set. In addition, the sensitivity analysis indicated that the ash content of coals was the most effective parameter for the prediction of the ratio of bottom ash to coal burned.