The amount of sediment should be taken into consideration in the planning of water structures for efficient use of limited water resources. It is important to estimate the amount of sediment for the successful operation of these structures in their future performances. Such estimations can be achieved by Artificial Neural Network (ANNs) with low error percentages as seen in many other disciplines. These networks also enable the modeling of nonlinear relationships between the parameters affecting the event. The purpose of this research is to establish models for sediment amounts in the Tigris River at the Diyarbakir measurement station in Turkey. Rainfall, temperature and discharge are taken as independent variables in the models, whereas sediment is taken as the dependent variable. Fourteen different models are generated using ANNs and Regression Analysis (RA). The results are compared with each other and with the observed data. The relative error and determination coefficient are used as comparison criteria. It is concluded that due to their nonlinear modeling capability, ANNs give better results than RA.
The food may cause serious loss of life and property. The evaluation of food events is necessary for the planning and design of engineering projects. In a dam, food problems should be considered for both upstream and downstream conditions. The characteristics of food problems can be derived from observation of dam sites. Tis paper aims to evaluate the impact of food hazards from spillway based on downstream conditions after dam construction. Also, the paper discusses downstream food problems depending on stream characteristics in an example dam site. In results, three reasons are determined for downstream foods problems: control problems of spillway gates, the naturally developing vegetation through channel flow line and incompetence of stream cross-section for carrying of food flows. Therefore, downstream conditions should be investigated before dam construction. Tis investigation can be considered in the planning phase. In addition, the cross-section and longitudinal profile of stream should be controlled via maintenance of stream after dam construction. In stream cross-section problems (such as vegetation, incompetence and irregularities), some modifications (e.g. straighten, deepen, and widen) can be applied with engineering works.
The current understanding of membrane fouling in submerged membrane bioreactors is still insufficient. Therefore, the role of microbial polymeric substances in membrane fouling mechanisms in a submerged membrane bioreactor was systematically investigated in this study. Microbial polymeric foulants accumulated on the membrane surface and in its pores. The foulants were then extracted in three steps, without deforming the membrane structure. Moreover, filtration resistances were determined by the resistance in series model. A strong functional relationship was observed between total polymeric substances accumulated in the membrane and their fouling resistances. The greatest accumulation of polymeric substances occurred in cake layers formed on the membrane surface. Correspondingly, the greatest fouling resistance resulted from the cake layer. The results indicated that more protein accumulated on the membrane surface than carbohydrates because the proteins were stickier. Regarding the analysis of microbial polymeric substances, it was observed that the filtration with a 0.45 mm pore size filter did not capture all of the polymeric substances that took part in membrane fouling.
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