Acceso abierto

Modeling of Environmental-Energy Efficiency of the Biogas Installation with Heat Supplying of the Biomass Fermentation Process

Georgiy RATUSHNIAK

Georgiy RATUSHNIAK

PhD, Prof.; Department of Engineering Systems in Construction, Vinnytsia National Technical UniversityUkraine
Buscar este autor en
Sciendo|Google Scholar
RATUSHNIAK, Georgiy
, 
Yuriy BIKS

Yuriy BIKS

PhD, Assosiated Prof.; Department of Construction, Architecture and Municipal Economy, Vinnytsia National Technical UniversityUkraine
Buscar este autor en
Sciendo|Google Scholar
BIKS, Yuriy
, 
Olena LYALYUK

Olena LYALYUK

PhD, Assosiated Prof.; Department of Construction, Architecture and Municipal Economy, Vinnytsia National Technical UniversityUkraine
Buscar este autor en
Sciendo|Google Scholar
LYALYUK, Olena
, 
Olga RATUSHNYAK

Olga RATUSHNYAK

PhD, Assosiated Prof.; Department of Enterprise Economics and Production Management, Vinnytsia National Technical University, Khmelnyts’ke shosseUkraine
Buscar este autor en
Sciendo|Google Scholar
RATUSHNYAK, Olga
 y 
Andriy LYALYUK

Andriy LYALYUK

Master’s student; Department of Construction, Architecture and Municipal Economy, Vinnytsia National Technical UniversityUkraine
Buscar este autor en
Sciendo|Google Scholar
LYALYUK, Andriy
  
27 ene 2021
Modeling of Environmental-Energy Efficiency of the Biogas Installation with Heat Supplying of the Biomass Fermentation Process's Cover Image
Acerca de este artículo
Artículo anterior
Artículo siguiente
Cite
Descargar portada
Categoría del artículo: research-article
Publicado en línea: 27 ene 2021
Páginas: 115 - 124
Recibido: 03 may 2020
Aceptado: 09 oct 2020
DOI: https://doi.org/10.21307/acee-2020-036
Palabras clave
© 2020 Georgiy Ratushniak et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Extent of fuzzy based research in renewable energy modeling. “x” axis – denotes year; the origin is [2006,0]. “y” axis – denotes the techniques in increasing complexity. The techniques are 1 – fuzzy regression, 2 – neuro-fuzzy, ANFIS, 3 – fuzzy AHP, ANP, 4 – fuzzy clustering, 5 – fuzzy optimization, 6 – neuro-fuzzy DEA, 7 – fuzzy GA, 8 – neuro-fuzzy GA, 9 – fuzzy expert, 10 – neuro-fuzzy expert, 11 – fuzzy MCDM, 12 – fuzzy TOPSIS, VIKOR, 13 – fuzzy PSO, 14 – fuzzy honey bee optimization, 15 – fuzzy PSO, QPSO, Cuckoo optimization. Note: the number inside the bubble indicates the technique and size of the bubble indicates the no. of research publication
Extent of fuzzy based research in renewable energy modeling. “x” axis – denotes year; the origin is [2006,0]. “y” axis – denotes the techniques in increasing complexity. The techniques are 1 – fuzzy regression, 2 – neuro-fuzzy, ANFIS, 3 – fuzzy AHP, ANP, 4 – fuzzy clustering, 5 – fuzzy optimization, 6 – neuro-fuzzy DEA, 7 – fuzzy GA, 8 – neuro-fuzzy GA, 9 – fuzzy expert, 10 – neuro-fuzzy expert, 11 – fuzzy MCDM, 12 – fuzzy TOPSIS, VIKOR, 13 – fuzzy PSO, 14 – fuzzy honey bee optimization, 15 – fuzzy PSO, QPSO, Cuckoo optimization. Note: the number inside the bubble indicates the technique and size of the bubble indicates the no. of research publication

Figure 2.

Biogas installation with combined system of heat supply for thermal stabilization of the bioconversion process [3]
Biogas installation with combined system of heat supply for thermal stabilization of the bioconversion process [3]

Figure 3.

The simplified model structure of complex assessment of the environmental-energy efficiency of a biogas installation
The simplified model structure of complex assessment of the environmental-energy efficiency of a biogas installation

Figure 4.

Inference tree for assessing the environmental-energy efficiency of a biogas installation
Inference tree for assessing the environmental-energy efficiency of a biogas installation

Classification of influence factors on the environmental- energy efficiency and term values as linguistic variables

Influence factors Designation and name of the linguistic variable Terms for evaluating linguistic variables
Environme ntal, X 1 The effect of reducing carbon monoxide emissions in exergy units, x 11 Low (L)Average (A)High (H)
The effect of preventing environmental pollution in exergy units, x 12 Low (L)Average (A)High (H)
The effect of using biofuels in exergy units, x 13 Low (L)Average (A)High (H)
The effect of the use of organic fertilizers in exergy units, x 14 Low (L)Average (A)High (H)
Influence factors Designation and name of the linguistic variable Terms for evaluating linguistic variables
Exergy effect from receiving additional thermal energy from traditional sources, x 21 Low (L)Average (A)High (H)
Energy, X 2 Exergy effect of obtaining additional solar energy, x 22 Low (L)Average (A)High (H)
Exergy effect of receiving additional thermal energy from the heat pump, x 23 Low (L)Average (A)High (H)
Fermentation temperature regimes, X 3 Exergy effect at: Thermophilic mode, x 31 Mesophilic mode, x 32 Cryophilic mode, x 33 Low (L)Average (A)High (H)
Idioma:
Inglés
Calendario de la edición:
4 veces al año
Temas de la revista:
Arquitectura y diseño, Arquitectura, Arquitectos, edificios
RSS Feed de revista