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

Georgiy RATUSHNIAK
Georgiy RATUSHNIAK
, 
Yuriy BIKS
Yuriy BIKS
, 
Olena LYALYUK
Olena LYALYUK
, 
Olga RATUSHNYAK
Olga RATUSHNYAK
 und 
Andriy LYALYUK
Andriy LYALYUK
   | 27. Jan. 2021
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Article Category: research-article
Online veröffentlicht: 27. Jan. 2021
Seitenbereich: 115 - 124
Eingereicht: 03. Mai 2020
Akzeptiert: 09. Okt. 2020
DOI: https://doi.org/10.21307/acee-2020-036
Schlüsselwörter
© 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)
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