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Energy
Article . 2022 . Peer-reviewed
License: CC BY NC ND
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Energy
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Mathematical modelling of gasification process of sewage sludge in reactor of negative CO2 emission power plant

Authors: Kamil Stasiak; Milad Amiri; Halina Kruczek; Paweł Ziółkowski; Paweł Madejski; Lukasz Niedzwiecki; Janusz Badur; +3 Authors

Mathematical modelling of gasification process of sewage sludge in reactor of negative CO2 emission power plant

Abstract

Abstract Sewage sludge is a residue of wastewater processing that is biologically active and consists of water, organic matter, including dead and alive pathogens, as well as organic and inorganic contaminants such as polycyclic aromatic hydrocarbons (PAHs) and heavy metals. Due to the nature of sewage sludge and its possible influence on human health and wellbeing, it is a subject of various regulations. Currently, sewage sludge is considered as biomass, according to the new Polish act on renewable energy sources of February 20, 2015 and its novel version of July 19, 2019. This study presents a novel model, along with a comparison with experimental results. The model could be used for sewage sludge gasification modelling for accurate assessment of the performance of novel concepts bioenergy with carbon capture and storage (BECCS) installations, using sewage sludge as a fuel. The composition of the dry produced gas, determined experimentally, yields: XCO = 0.093, XCO2 = 0.264, XCH4 = 0.139, XCxHy = 0.035, and XH2 = 0.468. Performed modifications to the original Deringer-with-Gumz-modification gasification model allowed to obtain good agreement with the experimental results, reaching XCO = 0.071, XCO2 = 0.243, XCH4 = 0.139, XC3H8 = 0.035, and XH2 = 0.512. The main novelty in the formulas of the internal model was due to propane inclusion, which was not found in the literature before. Additionally, sulphur dioxide was applied in exchange for other sulphur components presented in the original model. Equilibrium constants were adjusted to suit the experimental model. For ease of calculation, the own code was used to iterate multiple temperatures. Included was the energy balance equation that is essential for verification.

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
32
Top 10%
Top 10%
Top 10%
hybrid