YAPAY ZEKÂ VE MAKİNE ÖĞRENMESİ İLE ARITMA TESİSLERİNDE ENERJİ VERİMLİLİĞİ VE SÜREÇ İYİLEŞTİRME POTANSİYELİNİN DEĞERLENDİRİLMESİ

Niyazi Erdem Delikanlı

Derleme

ASSESSMENT OF ENERGY-EFFICIENCY AND PROCESS-IMPROVEMENT POTENTIAL IN TREATMENT PLANTS USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Yıl 2025, Cilt: 4 Sayı: 1, 42 - 54, 05.07.2025

Niyazi Erdem Delikanlı

Öz

This study evaluates the potential gains of artificial intelligence (AI) and machine-learning (ML) models for improving process efficiency and reducing energy consumption in wastewater treatment plants. A comprehensive literature review shows that models such as SVR, ANN, CNN, PLS-R, RF/GBT, Isolation Forest and deep reinforcement learning can reduce aeration energy by 25–35 % and overall plant energy use by 10–25 % worldwide. For Türkiye, the sector’s annual electrical load is estimated at roughly 1.6 TWh; AI-driven optimisation could save 15–25 % of this demand, translating into 0.9–1.5 billion TRY in operating costs and 110 000–180 000 t CO₂ emissions each year. Model success is found to depend strongly on high-quality data streams, process-specific algorithm selection and operator training. The findings indicate that national incentives and standardised data infrastructures would enable large-scale AI/ML deployment, thereby advancing Türkiye’s sustainable water-management objectives through lower energy use, improved process stability and reduced environmental impact.

Anahtar Kelimeler

Artificial intelligence, Machine learning, Wastewater treatment, Energy efficiency, Process optimization, Sustainability

Kaynakça

1. İlhan, A. İ., Daloğlu Çetinkaya, İ., Sürdürülebilir su kaynakları yönetimi, çevre diplomasisi ve Türkiye, İmge Kitabevi, 194-224, 2023.
2. T.C. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı, “Akıllı temiz ve atık su yönetimi uygulaması fizibilite raporu”, 2024. Erişim Tarihi: 16.05.2025, https://www.akillisehirler.gov.tr/wp-content/uploads/fizibilite-rapor/34-Akıllı%20Temiz%20ve%20Atık%20Su%20Yönetimi.pdf
3. Su Politikaları Derneği, “Su yönetiminde yenilikçi teknolojik çözümler artıyor”, 2024. Erişim Tarihi: 16.05.2025, https://supolitikalaridernegi.org/2024/03/24/su-yonetiminde-yenilikci-teknolojik-cozumler-artiyor/
4. Türkmenler, H., “Atık su arıtma tesislerinde enerji verimliliği”, Politeknik Dergisi, 20(2) : 495-502, 2017.
5. American Council for an Energy-Efficient Economy (ACEEE), Driving energy efficiency in the U.S. water & wastewater industry by state and utility programs, 2009.
6. Lawrence, J., Giurea, R., Bettinetti, R., “The impact of seasonal variations in rainfall and temperature on the performance of wastewater treatment plant in the context of environmental protection of Lake Como”, Applied Sciences, 14(24) : 11721, 2024.
7. Colwell, M., Abolghasemi, M., “Digital twins for forecasting and decision optimisation with machine learning: Applications in wastewater treatment”, arXiv preprint, arXiv:2404.14635, 2024.
8. Ganthavee, V., Trzcinski, A. P., “Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: A review”, Environmental Chemistry Letters, 22 : 2293-2318, 2024.
9. Rockwell Automation, “How AI boosts energy efficiency in wastewater treatment”, 2025. Erişim Tarihi: 16.05.2025, https://www.rockwellautomation.com/en-us/company/news/the-journal/how-ai-boosts-energy-efficiency-in-wastewater-treatment.html
10. Waqas, S., Harun, N. Y., Sambudi, N. S., Arshad, U., Nordin, N. A. H. M., Bilad, M. R., Saeed, A. A. H., Malik, A. A., “SVM and ANN modelling approach for the optimization of membrane permeability of a membrane rotating biological contactor for wastewater treatment”, Membranes, 12 : 821, 2022.
11. Jana, D. K. vd., “Optimization of effluents using artificial neural network and support vector regression in detergent industrial wastewater treatment”, Cleaner Chemical Engineering, 3 : 100039, 2022.
12. Harrington, L., “How AI boosts energy efficiency in wastewater treatment”, Rockwell Automation, 2025. Erişim Tarihi: 16.05.2025, https://www.rockwellautomation.com/en-us/company/news/the-journal/how-ai-boosts-energy-efficiency-in-wastewater-treatment.html
13. Mohammadi, E. vd., “Application of soft actor-critic algorithms in optimizing wastewater treatment with time delays integration”, arXiv preprint, arXiv:2411.18305, 2024.
14. Yaqub, M., Lee, W., “Modeling nutrient removal by membrane bioreactor at a sewage treatment plant using machine learning models”, Journal of Water Process Engineering, 46 : 102521, 2022.
15. Fetimi, A., Merouani, S., Khan, M. S., Asghar, M. N., Yadav, K. K., Jeon, B.-H., Hamachi, M., Kebiche-Senhadji, O., Benguerba, Y., “Modeling of textile dye removal from wastewater using innovative oxidation technologies (Fe(II)/Chlorine and H₂O₂/Periodate processes): Artificial neural network–particle swarm optimization hybrid model”, ACS Omega, 7(16) : 13613-13625, 2022.
16. Water Online, “Innovating wastewater treatment: Harnessing AI for energy efficiency and enhanced performance”, 2024. Erişim Tarihi: 16.05.2025, https://www.wateronline.com/doc/innovating-wastewater-treatment-harnessing-ai-for-energy-efficiency-and-enhanced-performance-0001
17. Purecontrol, “Energy optimization of wastewater treatment by AI”, 2024. Erişim Tarihi: 16.05.2025, https://www.purecontrol.com/en/applications/wastewater
18. Chen, X., Lei, Z., Chang, J.-S., Lee, D.-J., “Navigating future wastewater treatment plants with artificial intelligence: Applications, challenges, and innovations”, Journal of Cleaner Production, 504 : 145467, 2025.
19. Zakur, Y., Márquez, F., Al-Taie, A., Alsaidi, S., Alsadoon, A., Mirashrafi, S. B., Flaih, L., Zakoor, Y., “Artificial intelligence techniques applications in the wastewater”, E3S Web of Conferences, 605, 2025.
20. Xylem Inc., “Wastewater treatment plant uses AI to reduce aeration energy use by 30 percent”, 2020. Erişim Tarihi: 20 Mayıs 2025, https://www.xylem.com/en-us/making-waves/water-utilities-news/wastewater-treatment-plant-uses-ai-to-reduce-aeration-energy-use-by-30-percent/
21. Ganthavee, V., Trzcinski, A. P., “Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: A review”, Environmental Chemistry Letters, 22 : 2293-2318, 2024.
22. Cechinel, M. A. P., Neves, J., Fuck, J. V. R., Andrade, R. C., Spogis, N., Riella, H. G., Padoin, N., Soares, C., “Enhancing wastewater treatment efficiency through machine learning-driven effluent quality prediction: A plant-level analysis”, Journal of Water Process Engineering, 58 : 104758, 2024.
23. Mao, Z., Li, X., Zhang, X., Li, D., Lu, J., Li, J., Zheng, F., “Optimization of effluent quality and energy consumption of aeration process in wastewater treatment plants using artificial intelligence”, Journal of Water Process Engineering, 63 : 105384, 2024.
24. Aparna, K. G., Swarnalatha, R., Changmai, M., “Optimizing wastewater treatment plant operational efficiency through integrating machine learning predictive models and advanced control strategies”, Process Safety and Environmental Protection, 188 : 995-1008, 2024.
25. Nasir, F. B., Li, J., “Understanding machine learning predictions of wastewater treatment plant sludge with explainable artificial intelligence”, Water Environment Research, 96 : e11136, 2024.
26. Nagpal, M., Siddique, M. A., Sharma, K., Sharma, N., Mittal, A., “Optimizing wastewater treatment through artificial intelligence: Recent advances and future prospects”, Water Science and Technology, 90(3) : 731-757, 2024.
27. Baarimah, A. O., Bazel, M. A., Alaloul, W. S., Alazaiza, M. Y. D., Al-Zghoul, T. M., Almuhaya, B., Khan, A., Mushtaha, A. W., “Artificial intelligence in wastewater treatment: Research trends and future perspectives through bibliometric analysis”, Case Studies in Chemical and Environmental Engineering, 10 : 100926, 2024.
28. Wang, A.-J., Li, H., He, Z., Tao, Y., Wang, H., Yang, M., Savic, D., Daigger, G. T., Ren, N., “Digital twins for wastewater treatment: A technical review”, Engineering, 36 : 21-35, 2024.
29. Gulshin, I., Kuzina, O., “Optimization of wastewater treatment through machine learning-enhanced supervisory control and data acquisition: A case study of granular sludge process stability and predictive control”, Automation, 6(1) : 2, 2025.
30. Xylem Inc., “Xylem solution helps Italian water agency lower borehole pump energy use by 30 percent”, 2020. Erişim Tarihi: 20 Mayıs 2025, https://www.xylem.com/en-us/making-waves/water-utilities-news/xylem-solution-italy-lowers-borehole-pump-energy-use/
31. Municipal Sewer & Water Magazine, “The role of AI and automation in modern treatment plants”, 24 Temmuz 2024. Erişim Tarihi: 20 Mayıs 2025, https://aquafitbd.com/blogs/the-role-of-automation-in-modern-water-treatment-plants#:~:text=Automation%20is%20revolutionizing…
32. WaterOnline, “Five key areas in which artificial intelligence is set to transform water management in 2025”, 2025. Erişim Tarihi: 20 Mayıs 2025, https://smartwatermagazine.com/news/xylem-vue/five-key-areas-which-ai-set-transform-water-management-2025#:~:text=Dynamic…
33. Nikhar, C. K. vd., “A critical review on applications of machine learning in wastewater treatment: Insights and implications for distillery wastewater”, Water Quality Research Journal, 59(2) : 120-137, 2024.
34. Monday, C., Zaghloul, M. S., Krishnamurthy, D., Achari, G., “Incremental machine learning and genetic algorithm for optimization and dynamic aeration control in wastewater treatment plants”, Journal of Water Process Engineering, 69 : 106600, 2025.
35. Wahl, J., Aeration optimization at Beloit Wastewater Treatment Plant through predictive nutrient loading via AI-predicted daily averages and logic control, Yüksek Lisans Tezi, University of Wisconsin – Madison, 2023.
36. Wang, A.-J., Li, H., He, Z., Tao, Y., Wang, H., Yang, M., Savic, D., Daigger, G. T., Ren, N., “Digital twins for wastewater treatment: A technical review”, Engineering, 36 : 21-35, 2024.
37. Procházka, J., Kalinčíková, Z., “Digital twin is the new generation tool in the optimization of WWTP operation”, 14th IWA Specialized Conference on the Design, Operation and Economics of Large Wastewater Treatment Plants (LWWTP2024), 2024. doi: 10.22618/TP.EI.20254.116003
38. Royal HaskoningDHV, “Aquasuite Autopilot delivers 20 % energy savings at Amsterdam West WWTP”, 2024. Erişim Tarihi: 20 Mayıs 2025, https://aquasuite.ai/case-studies/regional-water-authority-amstel-gooi-vecht-wwtp-amsterdam-west/
39. Yetilmezsoy, K., Ozkaya, B., Cakmakci, M., “Artificial intelligence-based prediction models for environmental engineering”, Neural Network World, 21(3), 2011.
40. Manu, D., Thalla, A., “Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl nitrogen from wastewater”, Applied Water Science, 7 : 3783-3790, 2017.
41. Moradi, M., Vasseghian, Y., Arabzade, H., Khaneghah, A., “Various wastewaters treatment by sono-electrocoagulation process: A comprehensive review of operational parameters and future outlook”, Chemosphere, 263 : 128314, 2021.
42. Dalhat, M., Mu’azu, N., Essa, M., “Generalized decay and artificial neural network models for fixed-bed phenolic compounds adsorption onto activated date palm biochar”, Journal of Environmental Chemical Engineering, 9(1) : 104711, 2021.
43. Zhao, Q. vd., “Optimized SVR model for predicting dissolved oxygen levels in activated sludge systems”, Science of the Total Environment, 913 : 168159, 2025.
44. Gonzalez, R. vd., “Prediction of COD in industrial WWTP using ANN”, Scientific Reports, 14 : 11425, 2024.
45. Demir, M. vd., “Development of a CNN for accurate land-use change detection in Tahtalı Reservoir Catchment”, Remote Sensing Applications, 27 : 100983, 2024.
46. Al-Farsi, M. vd., “Estimating COD in petro-WWTP via PLS-R”, Chemosphere, 321 : 138019, 2023.
47. Liakos, P. vd., “Data-driven leak localization in urban water networks using random forest”, Engineering Structures, 296 : 116597, 2024.
48. Yıldız, A. vd., “Isolation Forest model for anomaly detection in water consumption data”, Information Technologies and Systems, 8(1) : 45-55, 2024.
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51. Karimi, A. vd., “Energy benchmarking of Turkish municipal WWTPs”, Renewable & Sustainable Energy Reviews, 161 : 112420, 2022.
52. Veolia Water, “AI-driven aeration control cuts 28 % energy at Gresham WWTP”, 2021. Erişim Tarihi: 16.05.2025, https://www.xylem.com/en-us/making-waves/water-utilities-news/wastewater-treatment-plant-uses-ai-to-reduce-aeration-energy-use-by-30-percent/
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YAPAY ZEKÂ VE MAKİNE ÖĞRENMESİ İLE ARITMA TESİSLERİNDE ENERJİ VERİMLİLİĞİ VE SÜREÇ İYİLEŞTİRME POTANSİYELİNİN DEĞERLENDİRİLMESİ

Yıl 2025, Cilt: 4 Sayı: 1, 42 - 54, 05.07.2025

Niyazi Erdem Delikanlı

Öz

Bu çalışma, su arıtma tesislerinde yapay zekâ (YZ) ve makine öğrenmesi (MÖ) modellerinin süreç verimliliği ve enerji tüketimi üzerindeki potansiyel kazanımlarını incelemektedir. Literatür taramasıyla SVR, ANN, CNN, PLS-R, RF/GBT, Isolation Forest ve derin pekiştirmeli öğrenme gibi modellerin küresel ölçekte havalandırma enerjisini %25-35, toplam tesis enerjisini ise %10-25 oranında düşürdüğü saptanmıştır. Türkiye özelinde atıksu arıtma sektörünün yıllık elektrik yükü yaklaşık 1,6 TWh olarak hesaplanmış, YZ tabanlı optimizasyonun %15-25 tasarruf sağlayarak yılda 0,9-1,5 milyar TL maliyet ve 110 000-180 000 t CO₂ azaltımı potansiyeli sunduğu belirlenmiştir. Model başarısının yüksek kaliteli veri akışı, proses-uyumlu algoritma seçimi ve operatör eğitimiyle doğrudan ilişkili olduğu görülmüştür. Bulgular, ulusal teşvikler ve standartlaştırılmış veri altyapısı ile desteklenen YZ/MÖ entegrasyonunun Türkiye’de sürdürülebilir su yönetimi hedeflerini önemli ölçüde ilerletebileceğini göstermektedir.

Anahtar Kelimeler

Yapay zekâ, Makine öğrenmesi, Atıksu arıtma, Enerji verimliliği, Süreç optimizasyonu, Sürdürülebilirlik

Kaynakça

1. İlhan, A. İ., Daloğlu Çetinkaya, İ., Sürdürülebilir su kaynakları yönetimi, çevre diplomasisi ve Türkiye, İmge Kitabevi, 194-224, 2023.
2. T.C. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı, “Akıllı temiz ve atık su yönetimi uygulaması fizibilite raporu”, 2024. Erişim Tarihi: 16.05.2025, https://www.akillisehirler.gov.tr/wp-content/uploads/fizibilite-rapor/34-Akıllı%20Temiz%20ve%20Atık%20Su%20Yönetimi.pdf
3. Su Politikaları Derneği, “Su yönetiminde yenilikçi teknolojik çözümler artıyor”, 2024. Erişim Tarihi: 16.05.2025, https://supolitikalaridernegi.org/2024/03/24/su-yonetiminde-yenilikci-teknolojik-cozumler-artiyor/
4. Türkmenler, H., “Atık su arıtma tesislerinde enerji verimliliği”, Politeknik Dergisi, 20(2) : 495-502, 2017.
5. American Council for an Energy-Efficient Economy (ACEEE), Driving energy efficiency in the U.S. water & wastewater industry by state and utility programs, 2009.
6. Lawrence, J., Giurea, R., Bettinetti, R., “The impact of seasonal variations in rainfall and temperature on the performance of wastewater treatment plant in the context of environmental protection of Lake Como”, Applied Sciences, 14(24) : 11721, 2024.
7. Colwell, M., Abolghasemi, M., “Digital twins for forecasting and decision optimisation with machine learning: Applications in wastewater treatment”, arXiv preprint, arXiv:2404.14635, 2024.
8. Ganthavee, V., Trzcinski, A. P., “Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: A review”, Environmental Chemistry Letters, 22 : 2293-2318, 2024.
9. Rockwell Automation, “How AI boosts energy efficiency in wastewater treatment”, 2025. Erişim Tarihi: 16.05.2025, https://www.rockwellautomation.com/en-us/company/news/the-journal/how-ai-boosts-energy-efficiency-in-wastewater-treatment.html
10. Waqas, S., Harun, N. Y., Sambudi, N. S., Arshad, U., Nordin, N. A. H. M., Bilad, M. R., Saeed, A. A. H., Malik, A. A., “SVM and ANN modelling approach for the optimization of membrane permeability of a membrane rotating biological contactor for wastewater treatment”, Membranes, 12 : 821, 2022.
11. Jana, D. K. vd., “Optimization of effluents using artificial neural network and support vector regression in detergent industrial wastewater treatment”, Cleaner Chemical Engineering, 3 : 100039, 2022.
12. Harrington, L., “How AI boosts energy efficiency in wastewater treatment”, Rockwell Automation, 2025. Erişim Tarihi: 16.05.2025, https://www.rockwellautomation.com/en-us/company/news/the-journal/how-ai-boosts-energy-efficiency-in-wastewater-treatment.html
13. Mohammadi, E. vd., “Application of soft actor-critic algorithms in optimizing wastewater treatment with time delays integration”, arXiv preprint, arXiv:2411.18305, 2024.
14. Yaqub, M., Lee, W., “Modeling nutrient removal by membrane bioreactor at a sewage treatment plant using machine learning models”, Journal of Water Process Engineering, 46 : 102521, 2022.
15. Fetimi, A., Merouani, S., Khan, M. S., Asghar, M. N., Yadav, K. K., Jeon, B.-H., Hamachi, M., Kebiche-Senhadji, O., Benguerba, Y., “Modeling of textile dye removal from wastewater using innovative oxidation technologies (Fe(II)/Chlorine and H₂O₂/Periodate processes): Artificial neural network–particle swarm optimization hybrid model”, ACS Omega, 7(16) : 13613-13625, 2022.
16. Water Online, “Innovating wastewater treatment: Harnessing AI for energy efficiency and enhanced performance”, 2024. Erişim Tarihi: 16.05.2025, https://www.wateronline.com/doc/innovating-wastewater-treatment-harnessing-ai-for-energy-efficiency-and-enhanced-performance-0001
17. Purecontrol, “Energy optimization of wastewater treatment by AI”, 2024. Erişim Tarihi: 16.05.2025, https://www.purecontrol.com/en/applications/wastewater
18. Chen, X., Lei, Z., Chang, J.-S., Lee, D.-J., “Navigating future wastewater treatment plants with artificial intelligence: Applications, challenges, and innovations”, Journal of Cleaner Production, 504 : 145467, 2025.
19. Zakur, Y., Márquez, F., Al-Taie, A., Alsaidi, S., Alsadoon, A., Mirashrafi, S. B., Flaih, L., Zakoor, Y., “Artificial intelligence techniques applications in the wastewater”, E3S Web of Conferences, 605, 2025.
20. Xylem Inc., “Wastewater treatment plant uses AI to reduce aeration energy use by 30 percent”, 2020. Erişim Tarihi: 20 Mayıs 2025, https://www.xylem.com/en-us/making-waves/water-utilities-news/wastewater-treatment-plant-uses-ai-to-reduce-aeration-energy-use-by-30-percent/
21. Ganthavee, V., Trzcinski, A. P., “Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: A review”, Environmental Chemistry Letters, 22 : 2293-2318, 2024.
22. Cechinel, M. A. P., Neves, J., Fuck, J. V. R., Andrade, R. C., Spogis, N., Riella, H. G., Padoin, N., Soares, C., “Enhancing wastewater treatment efficiency through machine learning-driven effluent quality prediction: A plant-level analysis”, Journal of Water Process Engineering, 58 : 104758, 2024.
23. Mao, Z., Li, X., Zhang, X., Li, D., Lu, J., Li, J., Zheng, F., “Optimization of effluent quality and energy consumption of aeration process in wastewater treatment plants using artificial intelligence”, Journal of Water Process Engineering, 63 : 105384, 2024.
24. Aparna, K. G., Swarnalatha, R., Changmai, M., “Optimizing wastewater treatment plant operational efficiency through integrating machine learning predictive models and advanced control strategies”, Process Safety and Environmental Protection, 188 : 995-1008, 2024.
25. Nasir, F. B., Li, J., “Understanding machine learning predictions of wastewater treatment plant sludge with explainable artificial intelligence”, Water Environment Research, 96 : e11136, 2024.
26. Nagpal, M., Siddique, M. A., Sharma, K., Sharma, N., Mittal, A., “Optimizing wastewater treatment through artificial intelligence: Recent advances and future prospects”, Water Science and Technology, 90(3) : 731-757, 2024.
27. Baarimah, A. O., Bazel, M. A., Alaloul, W. S., Alazaiza, M. Y. D., Al-Zghoul, T. M., Almuhaya, B., Khan, A., Mushtaha, A. W., “Artificial intelligence in wastewater treatment: Research trends and future perspectives through bibliometric analysis”, Case Studies in Chemical and Environmental Engineering, 10 : 100926, 2024.
28. Wang, A.-J., Li, H., He, Z., Tao, Y., Wang, H., Yang, M., Savic, D., Daigger, G. T., Ren, N., “Digital twins for wastewater treatment: A technical review”, Engineering, 36 : 21-35, 2024.
29. Gulshin, I., Kuzina, O., “Optimization of wastewater treatment through machine learning-enhanced supervisory control and data acquisition: A case study of granular sludge process stability and predictive control”, Automation, 6(1) : 2, 2025.
30. Xylem Inc., “Xylem solution helps Italian water agency lower borehole pump energy use by 30 percent”, 2020. Erişim Tarihi: 20 Mayıs 2025, https://www.xylem.com/en-us/making-waves/water-utilities-news/xylem-solution-italy-lowers-borehole-pump-energy-use/
31. Municipal Sewer & Water Magazine, “The role of AI and automation in modern treatment plants”, 24 Temmuz 2024. Erişim Tarihi: 20 Mayıs 2025, https://aquafitbd.com/blogs/the-role-of-automation-in-modern-water-treatment-plants#:~:text=Automation%20is%20revolutionizing…
32. WaterOnline, “Five key areas in which artificial intelligence is set to transform water management in 2025”, 2025. Erişim Tarihi: 20 Mayıs 2025, https://smartwatermagazine.com/news/xylem-vue/five-key-areas-which-ai-set-transform-water-management-2025#:~:text=Dynamic…
33. Nikhar, C. K. vd., “A critical review on applications of machine learning in wastewater treatment: Insights and implications for distillery wastewater”, Water Quality Research Journal, 59(2) : 120-137, 2024.
34. Monday, C., Zaghloul, M. S., Krishnamurthy, D., Achari, G., “Incremental machine learning and genetic algorithm for optimization and dynamic aeration control in wastewater treatment plants”, Journal of Water Process Engineering, 69 : 106600, 2025.
35. Wahl, J., Aeration optimization at Beloit Wastewater Treatment Plant through predictive nutrient loading via AI-predicted daily averages and logic control, Yüksek Lisans Tezi, University of Wisconsin – Madison, 2023.
36. Wang, A.-J., Li, H., He, Z., Tao, Y., Wang, H., Yang, M., Savic, D., Daigger, G. T., Ren, N., “Digital twins for wastewater treatment: A technical review”, Engineering, 36 : 21-35, 2024.
37. Procházka, J., Kalinčíková, Z., “Digital twin is the new generation tool in the optimization of WWTP operation”, 14th IWA Specialized Conference on the Design, Operation and Economics of Large Wastewater Treatment Plants (LWWTP2024), 2024. doi: 10.22618/TP.EI.20254.116003
38. Royal HaskoningDHV, “Aquasuite Autopilot delivers 20 % energy savings at Amsterdam West WWTP”, 2024. Erişim Tarihi: 20 Mayıs 2025, https://aquasuite.ai/case-studies/regional-water-authority-amstel-gooi-vecht-wwtp-amsterdam-west/
39. Yetilmezsoy, K., Ozkaya, B., Cakmakci, M., “Artificial intelligence-based prediction models for environmental engineering”, Neural Network World, 21(3), 2011.
40. Manu, D., Thalla, A., “Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl nitrogen from wastewater”, Applied Water Science, 7 : 3783-3790, 2017.
41. Moradi, M., Vasseghian, Y., Arabzade, H., Khaneghah, A., “Various wastewaters treatment by sono-electrocoagulation process: A comprehensive review of operational parameters and future outlook”, Chemosphere, 263 : 128314, 2021.
42. Dalhat, M., Mu’azu, N., Essa, M., “Generalized decay and artificial neural network models for fixed-bed phenolic compounds adsorption onto activated date palm biochar”, Journal of Environmental Chemical Engineering, 9(1) : 104711, 2021.
43. Zhao, Q. vd., “Optimized SVR model for predicting dissolved oxygen levels in activated sludge systems”, Science of the Total Environment, 913 : 168159, 2025.
44. Gonzalez, R. vd., “Prediction of COD in industrial WWTP using ANN”, Scientific Reports, 14 : 11425, 2024.
45. Demir, M. vd., “Development of a CNN for accurate land-use change detection in Tahtalı Reservoir Catchment”, Remote Sensing Applications, 27 : 100983, 2024.
46. Al-Farsi, M. vd., “Estimating COD in petro-WWTP via PLS-R”, Chemosphere, 321 : 138019, 2023.
47. Liakos, P. vd., “Data-driven leak localization in urban water networks using random forest”, Engineering Structures, 296 : 116597, 2024.
48. Yıldız, A. vd., “Isolation Forest model for anomaly detection in water consumption data”, Information Technologies and Systems, 8(1) : 45-55, 2024.
49. Wang, J. vd., “Real-time control of A2O process with deep reinforcement learning”, Water, 16(4) : 512, 2024.
50. Türkiye İstatistik Kurumu (TÜİK), “Belediyelerin atıksu istatistikleri 2022”, 2023. Erişim Tarihi: 16.05.2025, https://data.tuik.gov.tr/Bulten/Index?p=Belediyelerin-Atiksu-Istatistikleri-2022
51. Karimi, A. vd., “Energy benchmarking of Turkish municipal WWTPs”, Renewable & Sustainable Energy Reviews, 161 : 112420, 2022.
52. Veolia Water, “AI-driven aeration control cuts 28 % energy at Gresham WWTP”, 2021. Erişim Tarihi: 16.05.2025, https://www.xylem.com/en-us/making-waves/water-utilities-news/wastewater-treatment-plant-uses-ai-to-reduce-aeration-energy-use-by-30-percent/
53. Enerji Piyasası Düzenleme Kurumu (EPDK), “2025 yılı elektrik tarifeleri – Sanayi abone grubu”, 2025. Erişim Tarihi: 16.05.2025, https://www.epdk.gov.tr/Detay/Icerik/3-0-0/elektrik-tarifeler
54. İzmir Su ve Kanalizasyon İdaresi (İZSU), “2025-2029 Stratejik Planı”, 2025. Erişim Tarihi: 16.05.2025, https://www.izsu.gov.tr/YuklenenDosyalar/Dokumanlar/sp20252029.pdf
55. Ankara Su ve Kanalizasyon İdaresi (ASKİ), “ASKİ’nin dijital dönüşümü projesi”, 2025. Erişim Tarihi: 16.05.2025, https://www.aski.gov.tr/tr/HABER/Askının-Dıjıtal-Donusumu-Projesı-Hızla-Ilerlıyor…
56. Sidal, H., Altun, T., “Atık su arıtma tesislerinde BOİ tahmini için yapay sinir ağı ve regresyon analizi”, Journal of Institute of Science and Technology, 13(4) : 2934-2944, 2023.
57. Şerifoğlu, H., Atıksu arıtma tesislerinde KOİ tahmini için makine öğrenmesi yaklaşımları, Yüksek Lisans Tezi, Sakarya Üniversitesi, 2021.

Toplam 57 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	Türkçe
Konular	Mühendislik Uygulaması
Bölüm	Makaleler
Yazarlar	Niyazi Erdem Delikanlı 0000-0002-1322-3989
Yayımlanma Tarihi	5 Temmuz 2025
Gönderilme Tarihi	28 Nisan 2025
Kabul Tarihi	10 Haziran 2025
Yayımlandığı Sayı	Yıl 2025 Cilt: 4 Sayı: 1

Kaynak Göster

IEEE	N. E. Delikanlı, “YAPAY ZEKÂ VE MAKİNE ÖĞRENMESİ İLE ARITMA TESİSLERİNDE ENERJİ VERİMLİLİĞİ VE SÜREÇ İYİLEŞTİRME POTANSİYELİNİN DEĞERLENDİRİLMESİ”, JOSS, c. 4, sy. 1, ss. 42–54, 2025.

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Makale Dosyaları

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