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IDENTIFYING OPTIMAL BIOGAS PLANT INSTALLATION IN EASTERN ANATOLIA REGION USING CLUSTERING TECHNIQUES

Year 2025, Volume: 10 Issue: 1, 19 - 32, 27.03.2025
https://doi.org/10.57120/yalvac.1636844

Abstract

The increasing depletion of fossil fuels and their contribution to environmental issues have prompted energy managers and planners to shift their focus toward renewable energy sources to meet energy demands. Biogas produced through anaerobic digestion or fermentation of organic materials, stands out as a key energy source for converting agricultural, animal, industrial, and municipal waste into usable energy. This renewable energy is applied across various domains, including heating, transportation, and electricity generation. Biogas plants are crucial in efficiently processing agricultural, industrial, and urban waste. Therefore, optimizing the location and capacity of biogas plants during their installation is essential to maximize their efficiency and potential. In this study, to determine the biogas plant installation with the most suitable location and capacity according to the 2021 data of the Eastern Anatolia Region, firstly, the number of cattle, sheep, and poultry raised in the provinces and districts of the region, the amount of biogas produced accordingly, and the latitude and longitude values of the relevant settlement were achieved. The collected data were evaluated with the K-means clustering method, and the most suitable location for the biogas plant installation was found together with the production capacity. The results obtained from this study are anticipated to guide researchers operating in the relevant field and pave the way for similar studies

References

  • [1]. Ekinci, K., Kulcu, R., Kaya, D., Yaldız, O., Ertekin, C., Ozturk, H. H. (2010). The prospective of potential biogas plants that can utilize animal manure in Turkey, Energy Exploration & Exploitation, 28 (3), 187-206.
  • [2]. Güngör-Demirci, D. (2015). Spatial analysis of renewable energy potential and use in Turkey, Journal of Renewable and Sustainable Energy, 7(1):13126.
  • [3]. Yuruk, F., Erdogmus, P. (2018). Finding an optimum location for biogas plant: a case study for Duzce, Turkey, Neural Computing and Applications, 29(1), 157-165.
  • [4]. Aksu, H. H., Kumas, K., Inan, O., Akyuz, A., Gungor, A. (2019). Determination and spatial analysis of Muğla, Turkey biogas potential by tier methodologies, AIP Conference Proceedings 2178(1),030044.
  • [5]. Dundar, S., Bircan H., Eleroglu, H. (2021). Optimal Ranking of Compost Facilities that Can Be Established in Çorum with COPRAS and MAIRCA Methods, 2nd International Congress of the Turkish Journal of Agriculture – Food Science and Technology, 2523-2531.
  • [6]. Tulun, S., Arsu, T., Gurbuz, E. (2023). Selection of the most suitable biogas facility location with the geographical information system and multi-criteria decision-making methods: a case study of Konya Closed Basin, Turkey, Biomass Conversion and Biorefinery, 13, 3439–3461.
  • [7]. Nacar-Kocer, N. and Unlu, A., Biomass Potential of East Anatolia Region and Energy Production, Research of Eastern Anatolia Region, 5(2), 175-181, 2007.
  • [8]. Asla, F., Ozgen, I., Esen, H. (2016). The Opportunities for Utilization from Biogas and Microalgae in Energy Planning and the Potential of Eastern Anatolia Region, International Conference on Natural Science and Engineering (ICNASE’16), 1289-1299.
  • [9]. Caglayan, G. H. (2020). Investigation of Biogas Potential of Cattle and Sheep Waste in Eastern Anatolia Region, Turkish Journal of Agricultural and Natural Sciences, 7(3), 672 – 681.
  • [10]. Ceylan, A. B., Aydın, L., Nil M., Mamur, H, Polatoglu, I and Sozen, H. (2023). A new hybrid approach in selection of optimum establishment location of the biogas energy production plant, Biomass Conversion and Biorefinery, 13:5771–5786.
  • [11]. Liu, T., Ferrari, G., Pezzuolo, A., Alengebawy, A., Jin, K., Yang, G., Li, Q. and Ai, P. (2023). Evaluation and analysis of biogas potential from agricultural waste in Hubei Province, China, Agricultural Systems, 205:103577.
  • [12]. Nehra, M. and Jain, S. (2023) Estimation of renewable biogas energy potential from livestock manure: a case study of India, Bioresource Technology Reports, 22:101432.
  • [13]. Senocak, A. A, Guner Goren, H. (2023). Three-phase artificial intelligence-geographic information systems-based biomass network design approach: a case study in Denizli, Applied Energy, 343:121214.
  • [14]. Zhang, C., Nie, J. and Yan, X. (2023) Estimation of biomass utilization potential in China and the impact on carbon peaking, Environmental Science and Pollution Research, 30:94255–94275.
  • [15]. Du, KL., Angelov P. (2014). Data density-based clustering, 14th UK Workshop on Computational Intelligence (UKCI), 1-7.
  • [16]. Wagstaff, K., Cardie, C., Rogers, S. and Schrödl, S. (2001). Constrained K-means Clustering with Background Knowledge, ICML '01: Proceedings of the Eighteenth International Conference on Machine Learning, 577 – 584.
  • [17]. Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman R., Wu A. Y., (2004). A local search approximation algorithm for k-means clustering, Computational Geometry, 28(2-3), 89-112.
  • [18]. Dhavaleswarapu, R.K., Hoysall, C.N. and Srinivasaiah, D. (2023). Statistical clustering of biomass to predict biogas yields, Bioresource Technology Reports, 23: 101557.
  • [19] Obal, T.M., Souza, J.T., Florentino, H.O., Francisco, A.C. and Soler, E.M. (2024). A matheuristic applied to clustering rural properties and allocating plants for biogas generation, Energy, 305:132249
  • [20]. Karakuzulu, Z., Arici, F. and Dumansizoglu, M. (2017). Biogas Energy Potential of Eastern Anatolian Regıon, The Journal of Academic Social Science, 39, 541-554.
  • [21]. Caglayan, G.H. (2020). Investigation of Biogas Potential of Cattle and Sheep Waste in Eastern Anatolia Region, Turkish Journal Of Agricultural And Natural Sciences, 7(3): 672–681.
  • [22]. Pence, I., Kumaş, K., Siseci Çeşmeli, M., Akyüz, A. (2023). Modeling of energy and emissions from animal manure using machine learning methods: the case of the Western Mediterranean Region, Turkey. Environ Sci Pollut Res 30, 22631–22652 https://doi.org/10.1007/s11356-022-23780-5
  • [23]. Pence, I., Kumaş, K., Cesmeli, M.S. Akyüz, A. (2024). Future prediction of biogas potential and CH4 emission with boosting algorithms: the case of cattle, small ruminant, and poultry manure from Turkey. Environ Sci Pollut Res 31, 24461–24479. https://doi.org/10.1007/s11356-024-32666-7
  • [24]. MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1, 281 – 297.
  • [25]. Berkhin, P. (2002). Survey of Clustering Data Mining Techniques, Technical Report, Accrue Software, Inc.,
  • [26]. Han, J., Kamber, M., Tung, A. (2001). Spatial Clustering Methods in Data Mining: A Survey, Geographic Data Mining and Knowledge Discovery, Research Monographs in GIS.
  • [27]. Amasyali, M. F., Ersoy, O. (2008). The Performance Factors of Clustering Ensembles, IEEE 16th Signal Processing, Communication and Applications Conference.
  • [28]. Gersho, A., Gray, R. M. (1992). Vector Quantization and Signal Compression, The Springer International Series in Engineering and Computer Science 1st edition.
  • [29]. Linde, Y., Buzo, A., Gray R. (1980). An Algorithm for Vector Quantizer Design, IEEE Transactions on Communications, 28(1), 84-95.
  • [30]. Sariman, G. (2011). A Study of Clustering Techniques in Data Mining: Comparison of The K-Means and K-Medoids Clustering Algorithms, Suleyman Demirel University Science Institute Journal, 15(3), 192–202.
  • [31]. Turkish Statistical Institute (TUİK-2021 data), https://data.tuik.gov.tr.
  • [32]. Tibshirani, R., Walther, G., Hastie, T. (2001). Estimating the Number of Clusters in a Data Set via the Gap Statistic, Journal of the Royal Statistical Society. Series B (Statistical Methodology), 63(2), 411–423.
  • [33]. Avcioǧlu, A.O. and Turker, U. (2012). Status and potential of biogas energy from animal wastes in Turkey, Renewable and Sustainable Energy Reviews, 16:1557–1561.
  • [34]. Melikoglu, M. and Menekse, Z.K. (2020). Forecasting Turkey’s cattle and sheep manure based biomethane potentials till 2026, Biomass and Bioenergy, 132:105440.

IDENTIFYING OPTIMAL BIOGAS PLANT INSTALLATION IN EASTERN ANATOLIA REGION USING CLUSTERING TECHNIQUES

Year 2025, Volume: 10 Issue: 1, 19 - 32, 27.03.2025
https://doi.org/10.57120/yalvac.1636844

Abstract

The increasing depletion of fossil fuels and their contribution to environmental issues have prompted energy managers and planners to shift their focus toward renewable energy sources to meet energy demands. Biogas produced through anaerobic digestion or fermentation of organic materials, stands out as a key energy source for converting agricultural, animal, industrial, and municipal waste into usable energy. This renewable energy is applied across various domains, including heating, transportation, and electricity generation. Biogas plants are crucial in efficiently processing agricultural, industrial, and urban waste. Therefore, optimizing the location and capacity of biogas plants during their installation is essential to maximize their efficiency and potential. In this study, to determine the biogas plant installation with the most suitable location and capacity according to the 2021 data of the Eastern Anatolia Region, firstly, the number of cattle, sheep, and poultry raised in the provinces and districts of the region, the amount of biogas produced accordingly, and the latitude and longitude values of the relevant settlement were achieved. The collected data were evaluated with the K-means clustering method, and the most suitable location for the biogas plant installation was found together with the production capacity. The results obtained from this study are anticipated to guide researchers operating in the relevant field and pave the way for similar studies.

References

  • [1]. Ekinci, K., Kulcu, R., Kaya, D., Yaldız, O., Ertekin, C., Ozturk, H. H. (2010). The prospective of potential biogas plants that can utilize animal manure in Turkey, Energy Exploration & Exploitation, 28 (3), 187-206.
  • [2]. Güngör-Demirci, D. (2015). Spatial analysis of renewable energy potential and use in Turkey, Journal of Renewable and Sustainable Energy, 7(1):13126.
  • [3]. Yuruk, F., Erdogmus, P. (2018). Finding an optimum location for biogas plant: a case study for Duzce, Turkey, Neural Computing and Applications, 29(1), 157-165.
  • [4]. Aksu, H. H., Kumas, K., Inan, O., Akyuz, A., Gungor, A. (2019). Determination and spatial analysis of Muğla, Turkey biogas potential by tier methodologies, AIP Conference Proceedings 2178(1),030044.
  • [5]. Dundar, S., Bircan H., Eleroglu, H. (2021). Optimal Ranking of Compost Facilities that Can Be Established in Çorum with COPRAS and MAIRCA Methods, 2nd International Congress of the Turkish Journal of Agriculture – Food Science and Technology, 2523-2531.
  • [6]. Tulun, S., Arsu, T., Gurbuz, E. (2023). Selection of the most suitable biogas facility location with the geographical information system and multi-criteria decision-making methods: a case study of Konya Closed Basin, Turkey, Biomass Conversion and Biorefinery, 13, 3439–3461.
  • [7]. Nacar-Kocer, N. and Unlu, A., Biomass Potential of East Anatolia Region and Energy Production, Research of Eastern Anatolia Region, 5(2), 175-181, 2007.
  • [8]. Asla, F., Ozgen, I., Esen, H. (2016). The Opportunities for Utilization from Biogas and Microalgae in Energy Planning and the Potential of Eastern Anatolia Region, International Conference on Natural Science and Engineering (ICNASE’16), 1289-1299.
  • [9]. Caglayan, G. H. (2020). Investigation of Biogas Potential of Cattle and Sheep Waste in Eastern Anatolia Region, Turkish Journal of Agricultural and Natural Sciences, 7(3), 672 – 681.
  • [10]. Ceylan, A. B., Aydın, L., Nil M., Mamur, H, Polatoglu, I and Sozen, H. (2023). A new hybrid approach in selection of optimum establishment location of the biogas energy production plant, Biomass Conversion and Biorefinery, 13:5771–5786.
  • [11]. Liu, T., Ferrari, G., Pezzuolo, A., Alengebawy, A., Jin, K., Yang, G., Li, Q. and Ai, P. (2023). Evaluation and analysis of biogas potential from agricultural waste in Hubei Province, China, Agricultural Systems, 205:103577.
  • [12]. Nehra, M. and Jain, S. (2023) Estimation of renewable biogas energy potential from livestock manure: a case study of India, Bioresource Technology Reports, 22:101432.
  • [13]. Senocak, A. A, Guner Goren, H. (2023). Three-phase artificial intelligence-geographic information systems-based biomass network design approach: a case study in Denizli, Applied Energy, 343:121214.
  • [14]. Zhang, C., Nie, J. and Yan, X. (2023) Estimation of biomass utilization potential in China and the impact on carbon peaking, Environmental Science and Pollution Research, 30:94255–94275.
  • [15]. Du, KL., Angelov P. (2014). Data density-based clustering, 14th UK Workshop on Computational Intelligence (UKCI), 1-7.
  • [16]. Wagstaff, K., Cardie, C., Rogers, S. and Schrödl, S. (2001). Constrained K-means Clustering with Background Knowledge, ICML '01: Proceedings of the Eighteenth International Conference on Machine Learning, 577 – 584.
  • [17]. Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman R., Wu A. Y., (2004). A local search approximation algorithm for k-means clustering, Computational Geometry, 28(2-3), 89-112.
  • [18]. Dhavaleswarapu, R.K., Hoysall, C.N. and Srinivasaiah, D. (2023). Statistical clustering of biomass to predict biogas yields, Bioresource Technology Reports, 23: 101557.
  • [19] Obal, T.M., Souza, J.T., Florentino, H.O., Francisco, A.C. and Soler, E.M. (2024). A matheuristic applied to clustering rural properties and allocating plants for biogas generation, Energy, 305:132249
  • [20]. Karakuzulu, Z., Arici, F. and Dumansizoglu, M. (2017). Biogas Energy Potential of Eastern Anatolian Regıon, The Journal of Academic Social Science, 39, 541-554.
  • [21]. Caglayan, G.H. (2020). Investigation of Biogas Potential of Cattle and Sheep Waste in Eastern Anatolia Region, Turkish Journal Of Agricultural And Natural Sciences, 7(3): 672–681.
  • [22]. Pence, I., Kumaş, K., Siseci Çeşmeli, M., Akyüz, A. (2023). Modeling of energy and emissions from animal manure using machine learning methods: the case of the Western Mediterranean Region, Turkey. Environ Sci Pollut Res 30, 22631–22652 https://doi.org/10.1007/s11356-022-23780-5
  • [23]. Pence, I., Kumaş, K., Cesmeli, M.S. Akyüz, A. (2024). Future prediction of biogas potential and CH4 emission with boosting algorithms: the case of cattle, small ruminant, and poultry manure from Turkey. Environ Sci Pollut Res 31, 24461–24479. https://doi.org/10.1007/s11356-024-32666-7
  • [24]. MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1, 281 – 297.
  • [25]. Berkhin, P. (2002). Survey of Clustering Data Mining Techniques, Technical Report, Accrue Software, Inc.,
  • [26]. Han, J., Kamber, M., Tung, A. (2001). Spatial Clustering Methods in Data Mining: A Survey, Geographic Data Mining and Knowledge Discovery, Research Monographs in GIS.
  • [27]. Amasyali, M. F., Ersoy, O. (2008). The Performance Factors of Clustering Ensembles, IEEE 16th Signal Processing, Communication and Applications Conference.
  • [28]. Gersho, A., Gray, R. M. (1992). Vector Quantization and Signal Compression, The Springer International Series in Engineering and Computer Science 1st edition.
  • [29]. Linde, Y., Buzo, A., Gray R. (1980). An Algorithm for Vector Quantizer Design, IEEE Transactions on Communications, 28(1), 84-95.
  • [30]. Sariman, G. (2011). A Study of Clustering Techniques in Data Mining: Comparison of The K-Means and K-Medoids Clustering Algorithms, Suleyman Demirel University Science Institute Journal, 15(3), 192–202.
  • [31]. Turkish Statistical Institute (TUİK-2021 data), https://data.tuik.gov.tr.
  • [32]. Tibshirani, R., Walther, G., Hastie, T. (2001). Estimating the Number of Clusters in a Data Set via the Gap Statistic, Journal of the Royal Statistical Society. Series B (Statistical Methodology), 63(2), 411–423.
  • [33]. Avcioǧlu, A.O. and Turker, U. (2012). Status and potential of biogas energy from animal wastes in Turkey, Renewable and Sustainable Energy Reviews, 16:1557–1561.
  • [34]. Melikoglu, M. and Menekse, Z.K. (2020). Forecasting Turkey’s cattle and sheep manure based biomethane potentials till 2026, Biomass and Bioenergy, 132:105440.
There are 34 citations in total.

Details

Primary Language English
Subjects Energy
Journal Section Articels
Authors

Onur İnan 0000-0002-9683-344X

Early Pub Date March 24, 2025
Publication Date March 27, 2025
Submission Date February 10, 2025
Acceptance Date March 21, 2025
Published in Issue Year 2025 Volume: 10 Issue: 1

Cite

APA İnan, O. (2025). IDENTIFYING OPTIMAL BIOGAS PLANT INSTALLATION IN EASTERN ANATOLIA REGION USING CLUSTERING TECHNIQUES. Yalvaç Akademi Dergisi, 10(1), 19-32. https://doi.org/10.57120/yalvac.1636844

http://www.yalvacakademi.org/