A new hybrid approach in selection of optimum establishment location of the biogas energy production plant

Ceylan, Ali Bahadır; Aydın, Levent; Nil, Mustafa; Mamur, Hayati; Polatoğlu, İlker; Sözen, Hasan

doi:10.1007/s13399-021-01532-8

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…Since Turkey has a goal to reduce emissions, other cities can be included to generalize the problem. Also, the stochastic optimization approach [27] and fuzzy logic [28] can be considered for the uncertain criteria to decide the biogas plant locations. Considering designing a novel biogas network with low installation, maintenance, process costs and carbon prices [29] is a need for future research.…”

Section: Discussionmentioning

confidence: 99%

An Optimization Approach for A Biogas Supply Chain using Goal Programming and Mixed Integer Linear Programming

Derse

Göçmen

2023

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Environmental concerns prompt the world for a transition to renewable energy sources from fossil energy. Reducing the dependency on non-renewable sources of energy is prerequisite for the sustainable world and less environmental pollution. Biogas energy, which is one of the most important renewable energy sources, is produced by burning organic wastes and can be used in many different fields. In this study, a two-stage approach is undertaken to optimize a biogas supply chain design by incorporating of 30 districts in Izmir. In the first stage, the selection of the most suitable biogas plants is considered by the goal programming approach, which is of great importance to decide the optimal location with high energy potential. The most suitable sites for the biogas plants are obtained as Konak and Narlıdere districts. In the second stage, the location problem of the biogas vehicle charging stations (BVS) for biogas vehicles is handled considering the results of the first stage using mixed integer linear programming (MILP) approach. Computational results demonstrate that it would be more appropriate to establish BVS in 12 districts of İzmir. The model and solution approach are pioneering for supply chain problems and an efficient tool for renewable energy plans.

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Section: Discussionmentioning

confidence: 99%

An Optimization Approach for A Biogas Supply Chain using Goal Programming and Mixed Integer Linear Programming

Derse

Göçmen

2023

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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“…Ceylan et al ( 2023 ) developed a hybrid optimization model for Manisa, Turkey, utilizing a neuro-regression approach to determine the optimal biogas power plant location (Ceylan et al 2023 ).…”

Section: Introductionmentioning

confidence: 99%

“…They stated that biomass energy production could prevent emissions of approximately 1707 to 3583 million kg/year (Nehra and Jain 2023). Ceylan et al (2023) developed a hybrid optimization model for Manisa, Turkey, utilizing a neuro-regression approach to determine the optimal biogas power plant location (Ceylan et al 2023). Heydari et al (2023) studied the optimal design of a renewable wind/solar/biomass hybrid system for grid-independent applications in Iran by comparing the performance of genetic algorithms and particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

Future prediction of biogas potential and CH4 emission with boosting algorithms: the case of cattle, small ruminant, and poultry manure from Turkey

Pence,

Kumaş,

Cesmeli

et al. 2024

Environ Sci Pollut Res

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Animal waste can be converted into a renewable energy source using biogas technology. This process has an impact on greenhouse gas emissions and is a sustainable source of energy for countries. It can reduce the effects of climate change and protect the planet for future generations. Tier1 and tier2 approaches are commonly used in the literature to calculate emissions factors. With boosting algorithms, this study estimated each animal category’s biogas potential and CH4 emissions (tier1 and tier2 approach) for 2004–2021 in all of Turkey’s provinces. Two different scenarios were created in the study. For scenario-1, the years 2020–2021 were predicted using data from 2004 to 2019, while for scenario-2, the years 2022–2024 were predicted using data from 2004 to 2021. According to the scenario-1 analysis, the eXtreme Gradient Boosting Regressor (XGBR) algorithm was the most successful algorithm with an R2 of 0.9883 for animal-based biogas prediction and 0.9835 and 0.9773 for animal-based CH4 emission predictions (tier1 and tier2 approaches) for the years 2020–2021. When the mean absolute percentage error was evaluated, it was found to be relatively low at 0.46%, 1.07%, and 2.78%, respectively. According to the scenario-2 analysis, the XGBR algorithm predicted the log10 values of the animal-based biogas potential of five major cities in Turkey for the year 2024, with 11.279 for Istanbul, 12.055 for Ankara, 12.309 for Izmir, 11.869 for Bursa, and 12.866 for Antalya. In the estimation of log10 values of CH4 emission, the tier1 approach yielded estimates of 3.080, 3.652, 3.929, 3.411, and 3.321, respectively, while the tier2 approach yielded estimates of 1.810, 2.806, 2.757, 2.552 and 2.122, respectively. Graphical Abstract

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Modeling of energy and emissions from animal manure using machine learning methods: the case of the Western Mediterranean Region, Turkey

Pençe

Kumaş

Siseci

et al. 2022

Environ Sci Pollut Res

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A new hybrid approach in selection of optimum establishment location of the biogas energy production plant

Cited by 10 publications

References 33 publications

An Optimization Approach for A Biogas Supply Chain using Goal Programming and Mixed Integer Linear Programming

An Optimization Approach for A Biogas Supply Chain using Goal Programming and Mixed Integer Linear Programming

Future prediction of biogas potential and CH4 emission with boosting algorithms: the case of cattle, small ruminant, and poultry manure from Turkey

Modeling of energy and emissions from animal manure using machine learning methods: the case of the Western Mediterranean Region, Turkey

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