ARITMA ÇAMURLARINDA POLİSİKLİK AROMATİK HİDROKARBONLARIN (PAH’LARIN) GİDERİMLERİNİN VERİ MADENCİLİĞİ YÖNTEMLERİ İLE TAHMİNİ

Year 2021, Volume: 26 Issue: 1, 233 - 252, 30.04.2021

Burcu Çağlar Gençosman , Gizem Eker Şanlı

https://doi.org/10.17482/uumfd.813911

Abstract

Çevreye ve insan sağlığına olumsuz etkileri olan polisiklik aromatik hidrokarbonların (PAH'ların) atıksu arıtma çamurlarından gideriminde kullanılan yöntemlerden biri UV-C (ultraviyole-C) ışığı ve fotokatalizörler varlığında gerçekleştirilen fotoparçalanma uygulamalarıdır. PAH gideriminin sağlanıp sağlanmadığı, gerçekleştirilen deneylerden sonra ortaya çıkar ve bu durum zaman ve maliyeti arttırır. Alternatif olarak veri madenciliği sınıflandırma yöntemleri ile deney girdi koşullarına göre PAH'ların giderimi tahmin edilebilir, böylece zaman ve maliyet tasarrufu sağlanabilir. Bu sayede, arıtma çamurlarındaki başlangıç PAH konsantrasyonları esas alınarak UV teknolojilerinin kullanımı kararı daha az maliyet ve çabayla verilebilir. Çalışmanın ilk aşamasında 12 PAH türünü içeren 4 farklı özellikteki arıtma çamurunda UV uygulamaları gerçekleştirilerek PAH giderimleri belirlenmiş, sonrasında ilk aşamadaki sonuçlar veri kümelerinde kullanılarak başlangıç PAH seviyelerine göre PAH'ların giderimleri tahmin edilmiştir. Çok katmanlı algılayıcı (ÇKA) ağı, k-en yakın komşu (k-NN), C4.5 karar ağacı (C4.5), rastgele orman (RO) ve torbalama yöntemleri gibi çeşitli sınıflandırma yöntemleri giderim tahmini için kullanılmıştır. Performans karşılaştırmaları için kesinlik+ , duyarlılık, belirleyicilik, %doğruluk, AUC (Alıcı işlem karakteristikleri eğrisi) ve F-ölçütü esas alınmıştır. Ortalama doğruluk parametresine göre en başarılı üç yöntem sırasıyla RO (%95,730), k-NN (%95,588) ve ÇKA (%91.275) yöntemleridir. Azınlık sınıfı tahmininde ise ortalama AUC göz önüne alındığında RO (0,974), k-NN (0,944) ve Torbalama (0.939) yöntemleri diğer yöntemlerden daha iyi performans göstermiştir.

Keywords

Kentsel/endüstriyel atıksu arıtma çamuru, UV-C ışığı, veri madenciliği sınıflandırma yöntemleri, aşırı-örnekleme yöntemleri, PAH giderim tahmini

Prediction of Polycyclic Aromatic Hydrocarbons (PAHs) Removal in Wastewater Treatment Sludge with Data Mining Methods

Abstract

One of the methods used in the removal of polycyclic aromatic hydrocarbons (PAHs), which are known to have negative effects on the environment and human health, from wastewater treatment sludge, is photodegradation applications performed with UV-C (ultraviolet-C) light and photocatalysts. However, the PAH removal is revealed after the experiments performed and this increases the time and cost. Alternatively, with the data mining classification methods, the removal of PAHs can be estimated before the experiments are carried out; hence, the application of UV technologies is decided with less cost and effort. In this study, UV applications were performed on 4 types of treatment sludge containing 12 PAH types, and PAH removals were determined. Then the removal of PAHs was estimated regarding the initial PAH levels. Multi-layer perceptron (MLP) network, k-nearest neighbor (k-NN), C4.5 decision tree (C4.5), random forest (RF), and bagging were performed for the removal prediction. Precision+ , recall, specificity, accuracy%, AUC (Area Under the ROC Curve), and F-measure were used for performance comparisons. Regarding the average accuracy, the three most successful methods are RO (95.730%), k-NN (95.588%) and MCA (91.275%), respectively. Considering the average AUC, RO (0.974), k-NN (0.944) and Bagging (0.939) methods performed better than other methods.

Keywords

Urban/industrial wastewater treatment sludge, UV-C light, data mining classification methods, over-sampling methods, PAH removal prediction

References

• Aha, D. W., Kibler, D. ve Albert, M. K. (1991) Instance-based learning algorithms, Machine Learning, 6(1), 37–66. https://doi.org/10.1007/BF00153759.
• Akpınar, H. (2014) Data: Veri Madenciliği Veri Analizi, (1.baskı), Papatya Yayıncılık Eğitim, İstanbul.
• Aydemir, S. (2018) Weka ile Yapay Zeka, (1.baskı), Seçkin Yayıncılık, Ankara.
• Ballesteros-Gómez, A., Caballero-Casero, N., García-Fonseca, S., Lunar, L. ve Rubio, S. (2019) Multifunctional vesicular coacervates as engineered supramolecular solvents for wastewater treatment, Chemosphere, 223, 569–576. https://doi.org/10.1016/j.chemosphere.2019.02.089.
• Bhagat, S. K., Tung, T. M. ve Yaseen, Z. M. (2020) Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research, Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2019.119473.
• Blanchard, M., Teil, M. J., Ollivon, D., Legenti, L. ve Chevreuil, M. (2004) Polycyclic aromatic hydrocarbons and polychlorobiphenyls in wastewaters and sewage sludges from the Paris area (France), Environmental Research, 95(2), 184–197. https://doi.org/10.1016/j.envres.2003.07.003.
• Boztoprak, H., Özbay, Y., Güçlü, D., ve Küçükhemek, M. (2016) Prediction of sludge volume index bulking using image analysis and neural network at a full-scale activated sludge plant, Desalination and Water Treatment, 57(37), 17195–17205. https://doi.org/10.1080/19443994.2015.1085909.
• Bradley, A. P. (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognition, 30(7), 1145–1159. https://doi.org/10.1016/S0031-3203(96)00142-2.
• Breiman, L. (1996) Bagging predictors, Machine Learning, 24(2), 123–140. https://doi.org/10.1007/bf00058655.
• Breiman, L. (2001) Random Forests, Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324.
• Bunkhumpornpat, C., Sinapiromsaran, K. ve Lursinsap, C. (2009) Safe-level-SMOTE: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem, Lecture Notes in Computer Science, 475–482. https://doi.org/10.1007/978-3-642-01307-2_43.
• Bunkhumpornpat, C., Sinapiromsaran, K. ve Lursinsap, C. (2012) DBSMOTE: Density-based synthetic minority over-sampling technique, Applied Intelligence, 36(3), 664–684. https://doi.org/10.1007/s10489-011-0287-y.
• Chawla, N. V., Bowyer, K. W., Hall, L. O. ve Kegelmeyer, W. P. (2002) SMOTE: Synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953.
• Corominas, L., Garrido-Baserba, M., Villez, K., Olsson, G., Cortés, U. ve Poch, M. (2018) Transforming data into knowledge for improved wastewater treatment operation: A critical review of techniques, Environmental Modelling and Software, 106, 89–103. https://doi.org/10.1016/j.envsoft.2017.11.023.
• Cover, T. M. ve Hart, P. E. (1967) Nearest Neighbor Pattern Classification, IEEE Transactions on Information Theory, 13(1), 21–27. https://doi.org/10.1109/TIT.1967.1053964.
• Da Rocha, O. R. S., Dantas, R. F., Bezerra Duarte, M. M. M., Lima Duarte, M. M. ve da Silva, V. L. (2013) Solar photo-Fenton treatment of petroleum extraction wastewater, Desalination and Water Treatment, 51(28–30), 5785–5791. https://doi.org/10.1080/19443994.2013.792136.
• Dong, D., Li, P., Li, X., Xu, C., Gong, D., Zhang, Y. ve Li, P. (2010) Photocatalytic degradation of phenanthrene and pyrene on soil surfaces in the presence of nanometer rutile TiO2 under UV-irradiation, Chemical Engineering Journal, 158(3), 378–383. https://doi.org/10.1016/j.cej.2009.12.046.
• Eker, G. ve Hatipoğlu, M. (2019) Effect of UV wavelength, temperature and photocatalyst on the removal of PAHs from industrial soil with photodegradation applications, Environmental Technology, 40(28), 3793–3803. https://doi.org/10.1080/09593330.2018.1491635.
• Eker, G., Şengül, B. ve Cindoruk, S. S. (2019) Performance Evaluation of Diethylamine to the Removal of Polycyclic Aromatic Hydrocarbons (PAHs) from Polluted Soils with Sunlight, Polycyclic Aromatic Compounds, 1–13. https://doi.org/10.1080/10406638.2019.1578809.
• European Communities, E. (2001) Pollutants in urban wastewater and sewage sludge, https://ec.europa.eu/environment/archives/waste/sludge/pdf/sludge_pollutants.pdf, Erişim Tarihi: 12.10.2020.
• Fan, M., Hu, J., Cao, R., Ruan, W. ve Wei, X. (2018) A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence, Chemosphere, 200, 330-343. https://doi.org/10.1016/j.chemosphere.2018.02.111.
• Farzin, S., Chianeh, F. N., Anaraki, M. V. ve Mahmoudian, F. (2020) Introducing a framework for modeling of drug electrochemical removal from wastewater based on data mining algorithms, scatter interpolation method, and multi criteria decision analysis (DID), Journal of Cleaner Production, 266, 122075. https://doi.org/10.1016/j.jclepro.2020.122075
• Fayyad, U., Piatetsky-Shapiro, G. ve Smyth, P. (1996) From data mining to knowledge discovery in databases. AI magazine, 17(3), 37-37.
• Filibeli, A. (1996) Arıtma Çamurlarının İşlenmesi, Dokuz Eylül Üniversitesi Mühendislik Fakültesi Yayınları, 255.
• Gaur, N., Narasimhulu, K. ve PydiSetty, Y. (2018) Recent advances in the bio-remediation of persistent organic pollutants and its effect on environment, Journal of Cleaner Production, 198, 1602–1631. https://doi.org/10.1016/j.jclepro.2018.07.076.
• Ghasemi, N., Gbeddy, G., Egodawatta, P., Zare, F. ve Goonetilleke, A. (2019) Removal of polycyclic aromatic hydrocarbons from wastewater using dual‐mode ultrasound system, Water and Environment Journal, 1-10. https://doi.org/10.1111/wej.12540.
• Hadjimichael, A., Comas, J. ve Corominas, L. (2016) Do machine learning methods used in data mining enhance the potential of decision support systems? A review for the urban water sector, AI Communications, 29(6), 747–756. https://doi.org/10.3233/AIC-160714.
• Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H. ve Bing, G. (2017) Learning from class-imbalanced data: Review of methods and applications, Expert Systems with Applications, 73, 220–239. https://doi.org/10.1016/j.eswa.2016.12.035.
• Han, J., Kamber, M. ve Pei, J. (2012) Data Mining: Concepts and Techniques, (3. Basım), USA:Morgan Kaufmann Publishers, Elsevier.
• Haykin, S. S. (2009) Neural Networks and Learning Machines (3. Basım), Upper Saddle River, NJ, USA: Pearson Publishing.
• Japkowicz, N. ve Stephen, S. (2002) The class imbalance problem: A systematic study, Intelligent Data Analysis, 6(5), 429–449. https://doi.org/10.3233/ida-2002-6504.
• Jing, L., Chen, B. ve Zhang, B. (2014) Modeling of UV-induced photodegradation of naphthalene in marine oily wastewater by artificial neural networks. Water, Air, and Soil Pollution, 225(4), 1–14. https://doi.org/10.1007/s11270-014-1906-0.
• Karaca, G. (2013) Arıtma çamurları ve Nilüfer Çayı sedimentindeki poliaromatik hidrokarbon (PAH) seviyelerinin belirlenmesi ve giderimlerinin araştırılması, Doktora Tezi, Fen Bilimleri Enstitüsü, Bursa Uludağ Universitesi, Bursa.
• Karaca, G. ve Tasdemir, Y. (2013) Effects of Temperature and Photocatalysts on Removal of Polycyclic Aromatic Hydrocarbons (PAHs) from Automotive Industry Sludge, Polycyclic Aromatic Compounds, 33(4), 380–395. https://doi.org/10.1080/10406638.2013.782880.
• Khoshgoftaar, T. M., Golawala, M. ve Van Hulse, J. (2007) An empirical study of learning from imbalanced data using random forest, International Conference on Tools with Artificial Intelligence, ICTAI, 2, 310–317. https://doi.org/10.1109/ICTAI.2007.46.
• Kong, Q., Wu, H., Liu, L., Zhang, F., Preis, S., Zhu, S. ve Wei, C. (2018) Solubilization of polycyclic aromatic hydrocarbons (PAHs) with phenol in coking wastewater treatment system: Interaction and engineering significance, Science of the Total Environment, 628–629, 467–473. https://doi.org/10.1016/j.scitotenv.2018.02.077.
• Lin, Y. J., Teng, L. S., Lee, A. ve Chen, Y. L. (2004) Effect of photosensitizer diethylamine on the photodegradation of polychlorinated biphenyls, Chemosphere, 55(6), 879–884. https://doi.org/10.1016/j.chemosphere.2003.11.059.
• Maldonado, S., López, J. ve Vairetti, C. (2019) An alternative SMOTE oversampling strategy for high-dimensional datasets, Applied Soft Computing Journal, 76, 380–389. https://doi.org/10.1016/j.asoc.2018.12.024.
• Malhotra, R. ve Kamal, S. (2019) An empirical study to investigate oversampling methods for improving software defect prediction using imbalanced data, Neurocomputing, 343, 120–140. https://doi.org/10.1016/j.neucom.2018.04.090.
• Manoli, E. ve Samara, C. (2008). The removal of Polycyclic Aromatic Hydrocarbons in the wastewater treatment process: Experimental calculations and model predictions, Environmental Pollution, 151(3), 477–485. https://doi.org/10.1016/j.envpol.2007.04.009.
• Maynard, M. (2020) Neural Networks: Introduction to Artificial Neurons, Backpropagation and Multilayer Feedforward Neural Networks with Real-World Applications, Bağımsız basım.
• McCulloch, W. S. ve Pitts, W. (1943) A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics, 5(4), 115–133. https://doi.org/10.1007/BF02478259.
• Mojiri, A., Zhou, J. L., Ohashi, A., Ozaki, N. ve Kindaichi, T. (2019) Comprehensive review of polycyclic aromatic hydrocarbons in water sources, their effects and treatments, Science of the Total Environment, 696, p. 133971. https://doi.org/10.1016/j.scitotenv.2019.133971.
• Nekooeimehr, I. ve Lai-Yuen, S. K. (2016) Adaptive semi-unsupervised weighted oversampling (A-SUWO) for imbalanced datasets, Expert Systems with Applications, 46, 405–416. https://doi.org/10.1016/j.eswa.2015.10.031.
• Newhart, K. B., Holloway, R. W., Hering, A. S. ve Cath, T. Y. (2019) Data-driven performance analyses of wastewater treatment plants: A review, Water Research, 157, 498–513. https://doi.org/10.1016/j.watres.2019.03.030.
• Nguwi, Y. Y. ve Cho, S. Y. (2010) An unsupervised self-organizing learning with support vector ranking for imbalanced datasets, Expert Systems with Applications, 37(12), 8303–8312. https://doi.org/10.1016/j.eswa.2010.05.054.
• Ofman, P. ve Struk-Sokołowska, J. (2019) Artificial Neural Network (ANN) Approach to Modelling of Selected Nitrogen Forms Removal from Oily Wastewater in Anaerobic and Aerobic GSBR Process Phases, Water, 11(8), 1594. https://doi.org/10.3390/w11081594.
• Oh, J. Y., Choi, S. D., Kwon, H. O. ve Lee, S. E. (2016) Leaching of polycyclic aromatic hydrocarbons (PAHs) from industrial wastewater sludge by ultrasonic treatment, Ultrasonics Sonochemistry, 33, 61–66. https://doi.org/10.1016/j.ultsonch.2016.04.027.
• Öztürk, N., Şentürk, H., Gündoğdu, A., & Duran, C. (2020). İçme Suyu Arıtma Tesisi Atık Çamuru Üzerine Metilen Mavisi Adsorpsiyonu ve Yapay Sinir Ağları ile Modellenmesi, Uludağ University Journal of The Faculty of Engineering, 25(2), 1083–1104. https://doi.org/10.17482/uumfd.674224.
• Picos-Benítez, A. R., López-Hincapié, J. D., Chávez-Ramírez, A. U. ve Rodríguez-García, A. (2017) Artificial intelligence based model for optimization of COD removal efficiency of an up-flow anaerobic sludge blanket reactor in the saline wastewater treatment, Water Science and Technology, 75(6), 1351–1361. https://doi.org/10.2166/wst.2017.005.
• Quinlan, J. R. (1993) C4.5: Programs for Machine Learning,. San Mateo, California, USA: Morgan Kaufmann Publishers, Elsevier.
• Rababah, A. ve Matsuzawa, S. (2002) Treatment system for solid matrix contaminated with fluoranthene. II - Recirculating photodegradation technique, Chemosphere, 46(1), 49–57. https://doi.org/10.1016/S0045-6535(01)00090-X.
• Ren, R. Y., Yang, L. H., Han, J. L., Cheng, H. Y., Ajibade, F. O., Guadie, A. ve Wang, A. J. (2020) Perylene pigment wastewater treatment by fenton-enhanced biological process, Environmental Research, 186, 109522. https://doi.org/10.1016/j.envres.2020.109522.
• Rosenblatt, F. (1958) The perceptron: A probabilistic model for information storage and organization in the brain, Psychological Review, 65(6), 386–408. https://doi.org/10.1037/h0042519.
• Saber, A. N., Zhang, H., Cervantes-Avilés, P., Islam, A., Gao, Y., An, W. ve Yang, M. (2020) Emerging concerns of VOCs and SVOCs in coking wastewater treatment processes: Distribution profile, emission characteristics, and health risk assessment, Environmental Pollution, 265. https://doi.org/10.1016/j.envpol.2020.114960.
• Salari, D., Daneshvar, N., Aghazadeh, F. ve Khataee, A. R. (2005) Application of artificial neural networks for modeling of the treatment of wastewater contaminated with methyl tert-butyl ether (MTBE) by UV/H 2O2 process, Journal of Hazardous Materials, 125(1–3), 205–210. https://doi.org/10.1016/j.jhazmat.2005.05.030.
• Salihoğlu, N. K., Salihoğlu, G., Taşdemir, Y., Cindoruk, S. S., Yolsal, D., Oğulmuş, R. ve Karaca, G. (2010). Comparison of polycyclic aromatic hydrocarbons levels in sludges from municipal and industrial wastewater treatment plants, Archives of Environmental Contamination and Toxicology, 58(3),523–534. https://doi.org/10.1007/s00244-009-9389-5.
• Salihoğlu, N. K., Karaca, G., Salihoğlu, G. ve Taşdemir, Y. (2012) Removal of polycyclic aromatic hydrocarbons from municipal sludge using UV light, Desalination and Water Treatment, 44(1–3), 324–333. https://doi.org/10.1080/19443994.2012.691689.
• Sinan, R. K. (2010) Evsel Atıksu Arıtma Tesislerinde Ön Arıtım ve Biyolojik Arıtım Çıkış Parametrelerinin YSA ile Tahmini, Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, Selçuk Üniversitesi, Konya.
• Sprovieri, M., Feo, M. L., Prevedello, L., Manta, D. S., Sammartino, S., Tamburrino, S. ve Marsella, E. (2007) Heavy metals, polycyclic aromatic hydrocarbons and polychlorinated biphenyls in surface sediments of the Naples harbour (southern Italy), Chemosphere, 67(5), 998–1009. https://doi.org/10.1016/j.chemosphere.2006.10.055.
• Stevens, J. L., Northcott, G. L., Stern, G. A., Tomy, G. T. ve Jones, K. C. (2003) PAHs, PCBs, PCNs, organochlorine pesticides, synthetic musks, and polychlorinated n-alkanes in U.K. sewage sludge: Survey results and implications, Environmental Science and Technology, 37(3), 462–467. https://doi.org/10.1021/es020161y.
• Syafiuddin, A. ve Boopathy, R. (2020) A review of polycyclic aromatic hydrocarbons and their substitutions in full-scale wastewater treatment plants, Environmental Quality Management, 1-17. https://doi.org/10.1002/tqem.21694.
• Talebkeikhah, F., Rasam, S., Talebkeikhah, M., Torkashvand, M., Salimi, A. ve Moraveji, M. K. (2020) Investigation of effective processes parameters on lead (II) adsorption from wastewater by biochar in mild air oxidation pyrolysis process, International Journal of Environmental Analytical Chemistry, 1–21. https://doi.org/10.1080/03067319.2020.1777291.
• Wang, J., Tian, Z., Huo, Y., Yang, M., Zheng, X. ve Zhang, Y. (2018) Monitoring of 943 organic micropollutants in wastewater from municipal wastewater treatment plants with secondary and advanced treatment processes, Journal of Environmental Sciences (China), 67, 309–317. https://doi.org/10.1016/j.jes.2017.09.014.
• Wang, Y. R., Sun, G. D., ve Jin, Q. (2020) Imbalanced sample fault diagnosis of rotating machinery using conditional variational auto-encoder generative adversarial network, Applied Soft Computing Journal, 92, 106333. https://doi.org/10.1016/j.asoc.2020.106333.
• WEKA (1993) Waikato Environment for Knowledge Analysis, https://waikato.github.io/weka-wiki/downloading_weka/, Erişim Tarihi: 14.12.2020.
• Wu, G. ve Chang, E. Y. (2003) Class-Boundary Alignment for Imbalanced Dataset Learning, Proceedings of the ICML’03 Workshop on Learning from Imbalanced Data Sets. Washington, USA.
• Yaqub, M., Asif, H., Kim, S. ve Lee, W. (2020) Modeling of a full-scale sewage treatment plant to predict the nutrient removal efficiency using a long short-term memory (LSTM) neural network, Journal of Water Process Engineering, 37, 101388. https://doi.org/10.1016/j.jwpe.2020.101388.
• Yaqub, M. ve Lee, S. H. (2020) Micellar enhanced ultrafiltration (MEUF) of mercury-contaminated wastewater: Experimental and artificial neural network modeling, Journal of Water Process Engineering, 33, 101046. https://doi.org/10.1016/j.jwpe.2019.101046.
• Ye, Z., Yang, J., Zhong, N., Tu, X., Jia, J. ve Wang, J. (2020) Tackling environmental challenges in pollution controls using artificial intelligence: A review, Science of the Total Environment, 699, 134279. https://doi.org/10.1016/j.scitotenv.2019.134279.
• Zhang, L., Li, P., Gong, Z. ve Li, X. (2008) Photocatalytic degradation of polycyclic aromatic hydrocarbons on soil surfaces using TiO2 under UV light, Journal of Hazardous Materials, 158(2–3), 478–484. https://doi.org/10.1016/j.jhazmat.2008.01.119.
• Zhang, X., Yu, T., Li, X., Yao, J., Liu, W., Chang, S. ve Chen, Y. (2019) The fate and enhanced removal of polycyclic aromatic hydrocarbons in wastewater and sludge treatment system: A review, Critical Reviews in Environmental Science and Technology, 49(16), 1425–1475. https://doi.org/10.1080/10643389.2019.1579619.
• Zhao, L., Dai, T., Qiao, Z., Sun, P., Hao, J. ve 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.