Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

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BACKGROUND: Process modeling is a useful tool for description and prediction of the performance of anaerobic digestion systems under varying operation conditions. The objective of this study was to implement a model to simulate the dynamic behavior of a large-scale anaerobic sewage sludge digestion system. Artificial neural network (ANN) models using algorithms best suited to environmental problems (the Levenberg-Marquardt algorithm and the 'gradient descent with adaptive learning rate' back propagation algorithms) were used to model the anaerobic sludge digester of the Ankara Central Wastewater Treatment Plant (ACWTP) using dynamic data.

In the present study, Fenton and sono-Fenton processes were applied to the oxidative decolorisation of synthetic textile wastewater including CI Reactive Orange 127 and polyvinyl alcohol. Process optimisation [pH, ferrous ion (Fe 2+ ) and hydrogen peroxide (H 2 O 2 )], kinetic studies and their comparison were carried out for both of the processes. The sono-Fenton process was performed by indirect sonication in an ultrasonic water bath, which was operated at a fixed 35-kHz frequency and 80 W power. The optimum conditions were determined as [Fe 2+ ] = 20 mg l )1 , [H 2 O 2 ] = 15 mg l )1 and pH = 3 for the Fenton process and [Fe 2+ ] = 25 mg l )1 , [H 2 O 2 ] = 5 mg l )1 and pH = 3 for the sono-Fenton process. The colour removals were 89.9% and 91.8% by the Fenton and sono-Fenton processes, respectively. The highest decolorisation was achieved by the sono-Fenton process because of the production of some oxidising agents as a result of sonication. Consequently, ultrasonic irradiation in the sono-Fenton process slightly increased the colour removal to 91.8%, while decreasing the hydrogen peroxide dosage to one-third of that of the Fenton process.