Bu kaydın yasal hükümlere uygun olmadığını düşünüyorsanız lütfen sayfa sonundaki Hata Bildir bağlantısını takip ederek bildirimde bulununuz. Kayıtlar ilgili üniversite yöneticileri tarafından eklenmektedir. Nadiren de olsa kayıtlarla ilgili hatalar oluşabilmektedir. MİTOS internet üzerindeki herhangi bir ödev sitesi değildir!

Prediction Of Chemical Oxygen Demand From The Chemical Composition Of Wastewater By Artifıcial Neural Networks

Diğer Başlık: Prediction Of Chemical Oxygen Demand From The Chemical Composition Of Wastewater By Artifıcial Neural Networks

Oluşturulma Tarihi: 24-09-2019

Niteleme Bilgileri

Tür: Tez

Alt Tür: Yüksek Lisans Tezi

Yayınlanma Durumu: Yayınlanmamış

Dosya Biçimi: PDF

Dil: İngilizce

Konu(lar): Kimya mühendisliği,

Yazar(lar): Alobaidi, Basım Ahmed Saleh (Yazar),

Emeği Geçen(ler): KAYI, Hakan (Tez Danışmanı), Güler, Enver (Tez Danışmanı),


Yayın Tarihi: 18-07-2019


Dosya:
file show file
Görüntüle
download file
Kaydet

Anahtar Kelimeler

Artificial neural networks, industrial wastewater, chemical oxygen  demand prediction, chemical composition.


Özet

In our era, many technical applications and modern programs are being used and the role of artificial intelligence (AI) increases. Artificial Neural Networks (ANNs) as one of artificial intelligence tools have emerged to learn and discover a model of dynamic nonlinear behavior depending on a particular set of data through training of the network. They have high accuracy for prediction in multiple disciplines like biology, wastewater treatment and engineering. In this study, six input parameters were taken to predict the value of the Chemical Oxygen Demand (COD) in the wastewater before and after the treatment at the North Gas Company/Kirkuk, by using the standard backpropagation algorithm. The network was trained with the 150 data collected from the quality indices of the untreated and treated waste water, such as total chloride ions Cl, nitrate ions NO3‾ , phosphate ions PO4  3, sulfate ions SO4  2, ammonia NH3, Biochemical Oxygen Demand (BOD5) to predict one element, that is the COD.  After properly training of the neural network, it was tested by using the test data, and the best results were selected by the consideration of the mean square error and the regression coefficient. The findings of this study suggest that artificial neural networks are accurate and effective tools for predicting the COD values of treated wastewater.



İçindekiler



Açıklamalar



Haklar



Notlar



Kaynakça


Atıf Yapanlar

Gözat Sayfasına Dön

 

Sosyal Medya ve Araçlar

İstatistikler

  • Kayıt
    • Bu ay: 3
    • Toplam: 2444
  • Online
    • Ziyaretçi: 58
    • Üye: 0
    • Toplam: 58

Detaylı İstatistikler