A GRAPHICAL USER INTERFACE DESIGN FOR FORECASTING NUTRIENT CONCENTRATIONS IN WWTP

Eda Göz

doi:10.17780/ksujes.1584253

TR EN

ATIKSU ARITIM TESİSİNDE ORGANİK MADDE ÖNGÖRÜSÜ İÇİN GRAFİKSEL ARAYÜZ GELİŞTİRİLMESİ

Öz

Atıksu yönetimi, dünya genelinde önemli bir zorluk teşkil etmektedir. Veriye dayalı modellerin entegrasyonu, arıtma tesislerinin tasarım ve işletim verimliliğini artırmıştır. Bu çalışmada, yapay sinir ağı algoritması (ANN) evsel atıksu arıtım tesisinde çıkış akımı toplam azot (TN) ve toplam fosfor (TP) parametrelerinin tahmini için zaman serisi öngörüsü yapacak şekilde modifiye edilmiştir. Bu amaçla, 6 farklı ve bağımsız TN ve TP modelleri geliştirilmiştir. Model performansı, Ortalama mutlak yüzde hatası (MAPE, %) ve Hataların Karesinin Ortalamasının Karekökü (RMSE) ile değerlendirilmiştir. Bu kriterlere göre tüm alternatif modeller benzer performans sergilemiştir. Çıkış akımı toplam azot (TN) tahmin modellerinin test fazında MAPE ve RMSE değerleri sırasıyla %12 ve 1.4 civarında elde edilmiştir. Çıkış akımı toplam fosfor (TP) için MAPE değeri yaklaşık %30, RMSE ise 0.25 olarak hesaplanmıştır. Modelleme çalışmaları tamamlandıktan sonra, bir model kullanıcı dostu bir grafik kullanıcı arayüzüne (GUI) entegre edilmiştir ve gerçek verilerle test edilmiştir. Bu, kullanıcıya tek tıklama ile sonuç alma imkânı sunmaktadır.

Anahtar Kelimeler

Supporting Institution

TÜBİTAK (The Scientific and Technological Research Council of Turkey) for financial support through the 2219 International Postdoctoral Research Fellowship Program for Turkish Citizens

Thanks

I would like to thank TÜBİTAK (The Scientific and Technological Research Council of Turkey) for financial support through the 2219 International Postdoctoral Research Fellowship Program for Turkish Citizens. I also acknowledge water utility in USA for providing the data and express my gratitude to my supervisor, Prof. Dr. Tanju Karanfil, for his valuable insights and encouragement.

References

Abba, S., Elkiran, G., & Nourani, V. (2021). Improving novel extreme learning machine using PCA algorithms for multi-parametric modeling of the municipal wastewater treatment plant. Desalination and Water Treatment, 215, 414-426. https://doi.org/10.5004/dwt.2021.26903
Akhtar, N., Ishak, M. I. S., Bhawani, S. A., & Umar, K. (2021). Various natural and anthropogenic factors responsible for water quality degradation: A review. Water, 13, 2660. https://doi.org/10.3390/w13192660
Archontoulis, S. V., & Miguez, F. E. (2015). Nonlinear regression models and applications in agricultural research. Agronomy Journal, 107(3), 786–798. https://doi.org/10.2134/agronj2012.0506
El-Rawy, M., Abd-Ellah, M. K., Fathi, H., & Ahmed, A. K. A. (2021). Forecasting effluent and performance of wastewater treatment plant using different machine learning techniques. Journal of Water Process Engineering, 44, 102380. https://doi.org/10.1016/j.jwpe.2021.102380
Hansen, L. D., Stokholm-Bjerregaard, M., & Durdevic, P. (2022). Modeling phosphorous dynamics in a wastewater treatment process using Bayesian optimized LSTM. Computers and Chemical Engineering, 160, 107738. https://doi.org/10.1016/j.compchemeng.2022.107738
Haykin, S. (1999). Neural networks: A comprehensive foundation (2nd ed.). Prentice Hall.
Ly, Q. V., Truong, H., Ji, B., Nguyen, X. C., Cho, K. H., Ngo, H. H., & Zhang, Z. (2022). Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants. Science of the Total Environment, 832, 154930. https://doi.org/10.1016/j.scitotenv.2022.154930
Manav-Demir, N., Gelgor, H. B., Oz, E., Ilhan, F., Ulucan-Altuntaş, K., Tiwary, A., & Debik, E. (2022). Effluent parameters prediction of a biological nutrient removal (BNR) process using different machine learning methods: A case study. Journal of Environmental Management, 351, 119899. https://doi.org/10.1016/j.jenvman.2023.119899

Manu, D. S., & Thalla, A. K. (2017). Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl nitrogen from wastewater. Applied Water Science, 7, 3783-3791. https://doi.org/10.1007/s13201-017-0526-4
Mohammadi, F., Yavari, Z., Mohammadi, F., & Rahimi, S. (2022). Prediction the performance of full scale wastewater treatment plant with A-B process using artificial neural network and genetic algorithm. International Journal of Environmental Health Engineering, 11(1), 1-7. https://doi.org/10.4103/ijehe.ijehe_52_20
Oliveira, P., Fernandes, B., Analide, C., & Novais, P. (2021). Forecasting energy consumption of wastewater treatment plants with a transfer learning approach for sustainable cities. Electronics, 10, 1149. https://doi.org/10.3390/electronics10101149
Safeer, S., Pandey, R. P., Rehman, B., Hasan, S. W., & Ullah, A. (2022). A review of artificial intelligence in water purification and wastewater treatment: Recent advancements. Journal of Water Process Engineering, 49, 102974. https://doi.org/10.1016/j.jwpe.2022.102974
Sravan, J. S., Matsakas, L., & Sarkar, O. (2024). Advances in biological wastewater treatment processes: Focus on low-carbon energy and resource recovery in biorefinery context. Bioengineering, 11(3), 1-15. https://doi.org/10.3390/bioengineering11030281
Wang, R., Yu, Y., Chen, Y., Pan, Z., Li, X., Tan, Z., & Zhang, J. (2022). Model construction and application for effluent prediction in wastewater treatment plant: Data processing method optimization and process parameters integration. Journal of Environmental Management, 302, 114020. https://doi.org/10.1016/j.jenvman.2021.114020
Xu, Y., Zeng, X., Bernard, S., & He, Z. (2021). Data-driven prediction of neutralizer pH and valve position towards precise control of chemical dosage in a wastewater treatment plant. Journal of Cleaner Production, 348, 131360. https://doi.org/10.1016/j.jclepro.2022.131360
Yu, T., & Bai, Y. (2018). A comparative study of extreme learning machine, least squares support vector machine, and back propagation neural network for outlet total phosphorous prediction. In Prognostics and System Health Management Conference (pp. 717-722).
Zhao, L., Dai, T., Qiao, Z., Sun, P., Hao, J., & Yang, Y. (2020). Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse. Process Safety and Environmental Protection, 133, 169-182. https://doi.org/10.1016/j.psep.2019.11.014

Details

Primary Language

English

Subjects

Wastewater Treatment Processes , Water Treatment Processes , Process Control and Simulation

Journal Section

Research Article

Authors

Eda Göz ^*
0000-0002-3111-9042
Türkiye

Publication Date

March 3, 2025

Submission Date

November 13, 2024

Acceptance Date

December 20, 2024

Published in Issue

Year 1970 Volume: 28 Number: 1

DOI

https://doi.org/10.17780/ksujes.1584253

IZ

https://izlik.org/JA23PD67SF

Cite

RIS / Bibtex

APA

Göz, E. (2025). A GRAPHICAL USER INTERFACE DESIGN FOR FORECASTING NUTRIENT CONCENTRATIONS IN WWTP. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 479-486. https://doi.org/10.17780/ksujes.1584253

A GRAPHICAL USER INTERFACE DESIGN FOR FORECASTING NUTRIENT CONCENTRATIONS IN WWTP

ATIKSU ARITIM TESİSİNDE ORGANİK MADDE ÖNGÖRÜSÜ İÇİN GRAFİKSEL ARAYÜZ GELİŞTİRİLMESİ

Öz

Anahtar Kelimeler