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

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

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