2020
DOI: 10.3390/en13071610
|View full text |Cite
|
Sign up to set email alerts
|

Optimization Model for Biogas Power Plant Feedstock Mixture Considering Feedstock and Transportation Costs Using a Differential Evolution Algorithm

Abstract: In this paper, an optimization model for biogas power plant feedstock mixture with respect to feedstock and transportation costs using a differential evolution algorithm (DEA) is presented. A mathematical model and an optimization problem are presented. The proposed model introduces an optimal mixture of different feedstock combinations in a biogas power plant and informs about the maximal transportation distance for each feedstock before being unprofitable. In the case study, the proposed model is applied to … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
5

Relationship

0
5

Authors

Journals

citations
Cited by 6 publications
(3 citation statements)
references
References 40 publications
0
3
0
Order By: Relevance
“…This step is performed by DE, a population-based bio-inspired metaheuristic that showed a good capability to solve various problems from different areas. For example, in simple or different combinations, it was applied for: the prediction of polycyclic aromatic hydrocarbons formation in grilled meats [29] prediction of reactive extraction of pseudomonic acids [23], modeling the biogas production from anaerobic wastewater treatment plant [30], and optimization of biogas power plant feedstock [31].…”
Section: Artificial Neural Network and Differential Evolutionmentioning
confidence: 99%
“…This step is performed by DE, a population-based bio-inspired metaheuristic that showed a good capability to solve various problems from different areas. For example, in simple or different combinations, it was applied for: the prediction of polycyclic aromatic hydrocarbons formation in grilled meats [29] prediction of reactive extraction of pseudomonic acids [23], modeling the biogas production from anaerobic wastewater treatment plant [30], and optimization of biogas power plant feedstock [31].…”
Section: Artificial Neural Network and Differential Evolutionmentioning
confidence: 99%
“…The latest economic and energetic evaluation of using maize silage (with a purchase price of 54 EUR/t) in anaerobic digestion showed that a transportation distance of up to 18 km is convenient to ensure feasible biogas plant operation in Italy [70]. In the Croatian case, the latest price of maize silage of 34 EUR/t determined that a transportation distance between 24 and 38 km is still feasible for those biogas plants operating under the feed-in tariff [71]. This study revealed that locally available residue material, such as grass, with no actual cost of materials (except harvesting and transport, which are in total estimated at ca.…”
Section: Lignocellulosic Biomass From Landscape Managementmentioning
confidence: 99%
“…Different assessments can be considered to emphasize the feasibility of CR biogas projects, such as choosing the proper biogas system [50], the substances used for biogas production [51,52], the size of the biogas plant [43], and the obtained digestate [53]. A large-scale biogas plant operates on the feedstock of crop waste [54]. Typically, the biogas is distributed by pipelines to households from the central biogas tank.…”
Section: The Cr Biogas Projectmentioning
confidence: 99%