SavedAdd your journal

Journals / Balkan Journal of Electrical and Computer Engineering / 2026 / Cilt: 14

Bibliometric and Machine Learning-Based Forecasting of Research Trends in Agricultural Hydrogen Energy and Efficiency

Tarımsal Hidrojen Enerjisi ve Verimliliği Alanındaki Araştırma Eğilimlerinin Bibliyometrik ve Makine Öğrenimi Temelli Tahmini

Sami Ekici(FIRAT ÜNİVERSİTESİ)0000-0002-6760-2183
Masud Kabır(FIRAT UNIVERSITY)0000-0001-9766-226X
Pages
299–307
Publication Date
2026-10-22
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results indicate sustained growth in scientific output, with a marked acceleration after 2015.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study examines energy efficiency in hydrogen production from agricultural resources through an integrated bibliometric and machine learning framework.
📊
Key Findings & Data:A corpus of 958 Web of Science–indexed publications published between 2000 and 2024 was analyzed using Bibliometrix to characterize publication dynamics, citation patterns, journal concentrations, international collaboration, and thematic evolution.
💡
Conclusion & Impact:The results indicate sustained growth in scientific output, with a marked acceleration after 2015.
🔬Design:Modeling & Simulation
📍Scope:2000 Verileri
⚙️Variables:Bibliyometrik · enerji verimliliği · hidrojen enerjisi

Abstract

This study examines energy efficiency in hydrogen production from agricultural resources through an integrated bibliometric and machine learning framework. A corpus of 958 Web of Science–indexed publications published between 2000 and 2024 was analyzed using Bibliometrix to characterize publication dynamics, citation patterns, journal concentrations, international collaboration, and thematic evolution. In parallel, two long short-term memory (LSTM) models were developed in MATLAB: one to forecast annual publication counts for 2021–2034 and another to generate prospective research keywords from bibliometric text data. The results indicate sustained growth in scientific output, with a marked acceleration after 2015. China and the United States account for the largest citation volumes, whereas International Journal of Hydrogen Energy and Bioresource Technology emerge as the most influential outlets. Recurrent themes include biomass conversion, hydrogen production efficiency, photocatalysis, and performance optimization. The publication-forecast model projects a steep increase in papers through 2028, followed by stabilization above 500 publications per year, while the keyword model consistently highlights evaluation, gasification, and catalytic processing as emerging directions. Overall, the study provides a data-driven map of the field and a replicable approach for anticipating future research priorities at the energy–agriculture interface.

Özet

Bu çalışma, entegre bir bibliyometrik ve makine öğrenimi çerçevesi aracılığıyla tarımsal kaynaklardan hidrojen üretiminde enerji verimliliğini incelemektedir. 2000 ile 2023 yılları arasında yayınlanmış ve Web of Science’ta indekslenmiş 958 yayından oluşan bir veri kümesi, Bibliometrix kullanılarak analiz edildi; bu analizle yayın dinamikleri, atıf kalıpları, dergi yoğunlaşmaları, uluslararası işbirliği ve tematik gelişim belirlenmeye çalışıldı. Buna paralel olarak, MATLAB'da iki uzun kısa süreli bellek (LSTM) modeli geliştirilmiştir: biri 2021–2034 yılları için yıllık yayın sayılarını tahmin etmek, diğeri ise bibliyometrik metin verilerinden gelecekteki araştırma anahtar kelimelerini oluşturmak içindir. Sonuçlar, bilimsel çıktıda sürdürülebilir bir büyüme olduğunu ve 2015'ten sonra belirgin bir hızlanma olduğunu göstermektedir. En yüksek atıf hacmi Çin ve Amerika Birleşik Devletleri'ne aitken, International Journal of Hydrogen Energy ve Bioresource Technology en etkili yayınlar olarak öne çıkmaktadır. Tekrarlanan temalar arasında biyokütle dönüşümü, hidrojen üretim verimliliği, fotokataliz ve performans optimizasyonu yer almaktadır. Yayın tahmin modeli, 2028 yılına kadar makalelerde keskin bir artış öngörürken, ardından yıllık 500'ün üzerinde bir seviyede istikrarın sağlanacağını öngörmektedir; anahtar kelime modeli ise değerlendirme, gazlaştırma ve katalitik işlemeyi sürekli olarak yeni yönelimler olarak öne çıkarmaktadır. Genel olarak, çalışma bu alana ilişkin veriye dayalı bir harita sunmakta ve enerji-tarım arayüzünde gelecekteki araştırma önceliklerini öngörmek için tekrarlanabilir bir yaklaşım sağlamaktadır.

🔖

Cite This Article

Ekici, S., Kabır, M. (2026). Bibliometric and Machine Learning-Based Forecasting of Research Trends in Agricultural Hydrogen Energy and Efficiency. Balkan Journal of Electrical and Computer Engineering, Cilt: 14, 299–307. https://doi.org/10.17694/bajece.1971799

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (33)

  1. Joseph, J. (2023). Green energy transmission chain of climate management and smart transition. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4417370
  2. Hoehn, D., et al. (2019). Energy embedded in food loss management and in the production of uneaten food: seeking a sustainable pathway. Energies, 12(4), 767. https://doi.org/10.3390/en12040767
  3. Tang, C., Liu, H., Yao, H., & Fu, L. (2020). Hydrogen trapping efficiency of Li decorated porous boron fullerene B38: The first-principles study. International Journal of Hydrogen Energy, 45(41), 21646-21654. https://doi.org/10.1016/j.ijhydene.2020.05.232
  4. Mor, S., Madan, S., & Prasad, K. D. (2021). Artificial intelligence and carbon footprints: Roadmap for Indian agriculture. Strategic Change, 30(3), 269-280. https://doi.org/10.1002/jsc.2409
  5. Ahmad, T., et al. (2022). Energetics systems and artificial intelligence: Applications of industry 4.0. Energy Reports, 8, 334-361. https://doi.org/10.1016/j.egyr.2021.11.256
  6. Lung, R. B., Masanet, E., & McKane, A. (2006). The role of emerging technologies in improving energy efficiency: Examples from the food processing industry. Industrial Energy Technology Conference. Lawrence Berkeley National Laboratory. Retrieved from https://escholarship.org/uc/item/43c841xs
  7. Yue, Q., Wu, H., Wang, Y., & Guo, P. (2021). Achieving sustainable development goals in agricultural energy-water-food nexus system: An integrated inexact multi-objective optimization approach. Resources, Conservation and Recycling, 174, 105833. https://doi.org/10.1016/j.resconrec.2021.105833
  8. United Nations. (n.d.). The 17 goals: Sustainable development. United Nations. https://sdgs.un.org/goals
  9. Filippov, S. P., & Yaroslavtsev, A. B. (2021). Hydrogen energy: Development prospects and materials. Russian Chemical Reviews, 90(6), 627-643. https://doi.org/10.1070/rcr5014
  10. Wang, S., et al. (2021). Hydrogen production from the steam reforming of bioethanol over novel supported Ca/Ni-hierarchical Beta zeolite catalysts. International Journal of Hydrogen Energy, 46(73), 36245-36256. https://doi.org/10.1016/j.ijhydene.2021.08.170
  11. Guo, X. M., Trably, E., Latrille, E., Carrere, H., & Steyer, J. P. (2010). Hydrogen production from agricultural waste by dark fermentation: A review. International Journal of Hydrogen Energy, 35(19), 10660-10673. https://doi.org/10.1016/j.ijhydene.2010.03.008
  12. Sharma, R. K., Nazari, M. A., Haydary, T., Singh, T. P., & Mandal, S. (2023). A review on advanced processes of biohydrogen generation from lignocellulosic biomass with special emphasis on thermochemical conversion. Energies, 16(17), 6349. https://doi.org/10.3390/en16176349
  13. Agrawal, R., Singh, N. R., Ribeiro, F. H., Delgass, W. N., Perkis, D. F., & Tyner, W. E. (2009). Synergy in the hybrid thermochemical-biological processes for liquid fuel production. Computers & Chemical Engineering, 33(12), 2012-2017. https://doi.org/10.1016/j.compchemeng.2009.06.026
  14. Teiseh, E. A., & Capareda, S. (2013). Maximizing the concentrations of hydrogen, carbon monoxide and methane produced from the pyrolysis of a MixAlco process derived sludge. Journal of Analytical and Applied Pyrolysis, 102, 76-82. https://doi.org/10.1016/j.jaap.2013.03.013
  15. Kannah, R. Y., et al. (2019). Biohydrogen production from rice straw: Effect of combinative pretreatment, modelling assessment and energy balance consideration. International Journal of Hydrogen Energy, 44(4), 2203-2215. https://doi.org/10.1016/j.ijhydene.2018.07.201
  16. Michalsky, R., Parman, B. J., Amanor-Boadu, V., & Pfromm, P. H. (2012). Solar thermochemical production of ammonia from water, air and sunlight: Thermodynamic and economic analyses. Energy, 42(1), 251-260. https://doi.org/10.1016/j.energy.2012.03.062
  17. Obileke, K., Makaka, G., & Nwokolo, N. (2023). Recent advancements in anaerobic digestion and gasification technology. Applied Sciences, 13(9), 5597. https://doi.org/10.3390/app13095597
  18. Shahbeig, H., Shafizadeh, A., Rosen, M. A., & Sels, B. F. (2022). Exergy sustainability analysis of biomass gasification: A critical review. Biofuel Research Journal, 9(1), 1592-1607. https://doi.org/10.18331/BRJ2022.9.1.5
  19. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
  20. Wang, H., Lei, Z., Zhang, X., Zhou, B., & Peng, J. (2019). A review of deep learning for renewable energy forecasting. Energy Conversion and Management, 198, 111799. https://doi.org/10.1016/j.enconman.2019.111799
  21. Shi, Y., Yang, K., Yang, Z., & Zhou, Y. (2022). Primer on artificial intelligence. In Y. Zhou (Ed.), Mobile edge artificial intelligence (pp. 7-36). Elsevier. https://doi.org/10.1016/b978-0-12-823817-2.00011-5
  22. Gupta, H. P., Chopade, S., & Dutta, T. (2022). Computational intelligence in agriculture. In Emerging computing paradigms (pp. 125-142). John Wiley & Sons. https://doi.org/10.1002/9781119813439.ch7
  23. Jeong, J. (2022). Introduction of the first AI impact assessment and future tasks: South Korea discussion. Laws, 11(5), 73. https://doi.org/10.3390/laws11050073
  24. Liu, X., Zhang, S., & Bae, J. (2017). The nexus of renewable energy-agriculture-environment in BRICS. Applied Energy, 204, 489-496. https://doi.org/10.1016/j.apenergy.2017.07.077
  25. Kabir, M., & Ekici, S. (2024). Energy-agriculture nexus: Exploring the future of artificial intelligence applications. Energy Nexus, 13, 100263. https://doi.org/10.1016/j.nexus.2023.100263
  26. Hao, Y., Li, L., Liu, D., Yu, H., & Zhou, Q. (2018). The synergy of SPR effect and Z-scheme of Ag on enhanced photocatalytic performance of 3DOM Ag/CeO2-ZrO2 composite. Molecular Catalysis, 447, 37-46. https://doi.org/10.1016/j.mcat.2018.01.004
  27. Pahlavan, R., Omid, M., & Akram, A. (2012). Energy input-output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy, 37(1), 171-176. https://doi.org/10.1016/j.energy.2011.11.055
  28. Brennan, L., & Owende, P. (2010). Biofuels from microalgae: A review of technologies for production, processing, and extractions of biofuels and co-products. Renewable & Sustainable Energy Reviews, 14(2), 557-577. https://doi.org/10.1016/j.rser.2009.10.009
  29. Dinh, C.-T., et al. (2018). CO2 electroreduction to ethylene via hydroxide-mediated copper catalysis at an abrupt interface. Science, 360(6390), 783-787. https://doi.org/10.1126/science.aas9100
  30. Rabaey, K., Boon, N., Siciliano, S. D., Verhaege, M., & Verstraete, W. (2004). Biofuel cells select for microbial consortia that self-mediate electron transfer. Applied and Environmental Microbiology, 70(9), 5373-5382. https://doi.org/10.1128/AEM.70.9.5373-5382.2004
  31. Chen, G.-F., et al. (2020). Electrochemical reduction of nitrate to ammonia via direct eight-electron transfer using a copper-molecular solid catalyst. Nature Energy, 5(8), 605-613. https://doi.org/10.1038/s41560-020-0654-1
  32. ahamuni, N. N., & Adewuyi, Y. G. (2010). Advanced oxidation processes (AOPs) involving ultrasound for waste water treatment: A review with emphasis on cost estimation. Ultrasonics Sonochemistry, 17(6), 990-1003. https://doi.org/10.1016/j.ultsonch.2009.09.005
  33. Wang, H., Pu, Y., Ragauskas, A., & Yang, B. (2019). From lignin to valuable products: Strategies, challenges, and prospects. Bioresource Technology, 271, 449-461. https://doi.org/10.1016/j.biortech.2018.09.072