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    Diagnosis of the anaerobic reject water effects on WWTP operational characteristics as a precursor of bulking and foaming
    (IWA PUBLISHING, 2015) Erdirencelebi, Dilek; Kucukhemek, Murat
    This study investigates the effects observed on operational parameters in a large and full-scale wastewater treatment plant subjected to anaerobic reject water (ARW) diversion off the main line for a 3-month period and further monitoring for a 2-year period. The plant's secondary unit consists of a two-stage plug-flow-modified Bardenpho process receiving wastewater from both municipal and industrial origins. As a result, ARW was found to have a direct effect on bulking in secondary clarifiers and foaming in anaerobic digesters (AD) despite its relatively small flow rate. During the cut-off period a highly stable sludge volume index at 80 mL g(-1) level was obtained in the secondary clarifiers, effluent suspended solids concentration was reduced and continuous feeding to AD was recovered. Sludge density increased in the thickeners during hot season. Secondary clarifiers showed good and stable settleability despite low dissolved oxygen, food/microorganism ratio and high sludge retention time and ammonium levels in the biological unit. The bulking and foaming effect was presented on the plant's internal flow balance. ARW needs serious consideration for elimination by appropriate technologies because of its high potential as a multi-dimensional pollutant source, not only as a carrier of nutrients but also as a possible carrier of filamentous bacteria, which might promote chronic seeding and retention in the system.
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    Prediction of sludge volume index bulking using image analysis and neural network at a full-scale activated sludge plant
    (DESALINATION PUBL, 2016) Boztoprak, Halime; Ozbay, Yuksel; Guclu, Dunyamin; Kucukhemek, Murat
    Sludge volume index parameter should be monitored daily for the performance of wastewater treatment plants. It was aimed to estimate this parameter using image processing and artificial intelligence techniques for full-scale wastewater treatment plant. The activated sludge samples were collected from the aeration tank of the activated sludge process in Konya Domestic Wastewater Treatment Plant. Sludge characteristics and settling properties were observed microscopically via the measurements of flocs and filaments. The 49 images per slide were taken by an image-analysis system developed for automated image acquisition. A total of 120 samples were examined over a period of year. The floc and filament structures were analyzed using Cellular Neural Networks (CNN). Iteration value of the CNN was modified according to the image. Then, a number of morphological operations were applied for an accurate identification of the floc and filaments separately. Textural, shape, and statistical approaches were utilized for creating a set of data for each sample. After preparing the training and test data by shuffling the data randomly, a fivefold cross-validation method was applied. And, these training and test data were applied to an artificial neural network. The weights of the neural network were trained using the Levenberg-Marquardt, Genetic, and Artificial Bee Colony algorithms.