Galip Seckin

Heavy metal content and distribution in surface sediments of the Seyhan River, Turkey

Chemical fractionation of seven heavy metals (Cd, Cr, Cu, Mn, Ni, Pb and Zn) was studied using a modified three-step sequential procedure to assess their impacts in the sediments of the Seyhan River, Turkey. Samples were collected from six representative stations in two campaigns in October 2009 and June 2010, which correspond to the wet and dry seasons, respectively. The total metal concentrations in the sediments demonstrated different distribution patterns at the various stations. Cadmium was the only metal that was below detection at all stations during both sampling periods. Metal fractionation showed that, except for Mn and Pb, the majority of metals were found in the residual fraction regardless of sampling time, indicating that these metals were strongly bound to the sediments. The potential mobility of the metals (non-residual fractions) is reflected in the following ranking: Pb > Mn > Zn > Cu > Ni > Cr in October 2009 and Mn > Pb > Zn > Cu > Ni > Cr in June 2010. The second highest proportion of metals was bound to organic matter/sulfides, originating primarily from anthropogenic activities. Non-residual metal fractions for all stations were highest in June 2010, which may be linked to higher organic matter concentrations in the sediment samples with 1.40% and 15.1% in October 2009 and June 2010, respectively. Potential sediment toxicity was evaluated using the Risk Assessment Code (RAC). Based on RAC classification, Cd and Cr pose no risk, Cu and Ni pose low risk, Pb and Zn were classified as medium risk metals, while the environmental risk from Mn was high. In addition, based on the sediment quality guidelines (SQG), the Seyhan River can be classified as a river with no, to moderate, toxicological risks, based on total metal concentrations.

Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels

Most natural streams or rivers exhibit a compound or two-stage geometry consisting of a main channel and one or two floodplains. The discharge capacity of compound channels has an importance in flood defence schemes and in the economic development of floodplain areas for agriculture and parks. Therefore, the comprehensive stage-discharge model studies performed and different one or two-dimensional methods have been developed. In this study, the single-channel method (SCM), the divided-channel method (DCM), the coherence method (COHM), the exchange discharge method (EDM) and the Shiono-Knight method (SKM) have been compared with a multilayer perception neural network (MLP) with Levenberg-Marquardt algorithm. The results of the comparisons reveal that the artificial neural network (ANN) model gives slightly better statistical results than those of the COHM, EDM and these three give more accurate results than those of the SCM, DCM, and SKM.

Hydropower Optimization for the Lower Seyhan System in Turkey using Dynamic Programming

Abstract Dynamic programming with successive approximation has been used in the past for optimizing multi-reservoir water resources systems. In this study, the State Incremental Dynamic Programming (SIDP) model is developed for energy optimization of multi-reservoir systems. A random file access method is used for reaching initial and intermediate data to cope with the curse of dimensionality of dynamic programming. A conventional dynamic programming method is used for each single reservoir to find the initial trajectory of the reservoirs. Then, the computer program developed in the study is applied to the multipurpose-multi-reservoir system in Lower Seyhan Basin, which has six reservoirs, some of which are serial and some parallel. First, extended historical flows were used to maximize firm energy in the critical period, and then total energy in the total flows. The program was run with 50-year long segments (20 flow scenarios) of the synthetic flow data generated by using the HEC-4 generalized computer program to take into account the stochastic nature of stream flows. An increment of approximately 20 percent in total energy was obtained by using the model for the Lower Seyhan System, as compared to that calculated previously by conventional methods.

Bridge afflux analysis through arched bridge constrictions using artificial intelligence methods

Although many studies have been carried out for estimating the afflux through modern straight deck bridge constrictions, little attention has been given to medieval arched bridge constrictions. Hydraulic Research Wallingford in the UK (Brown, P.M., 1988. Afflux at arch bridges. Report SR 182. Wallingford, UK: HR Wallingford) recently published a major coverage of both experimental and field afflux data obtained from arched bridge constrictions. The report pointed out that the present day formulas developed for estimating the bridge afflux are inadequate to apply to ancient arched structures. Therefore, this study aimed at developing new afflux methods for arched bridge constrictions using multi-layer perceptrons (MLP) neural networks, radial basis function-based neural networks (RBNN), generalised regression neural networks (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) model. Multiple linear and multiple nonlinear regression analyses were also used for comparison purposes. Mean square errors, mean absolute errors, mean absolute relative errors, average of individual ratios between predicted and actual values, and determination coefficients were used as comparison criteria for the evaluation of model performances. The test results showed that MLP, RBNN, GRNN, and ANFIS models gave reasonable accuracy when applied to both the field and experimental data collected by Hydraulic Research Wallingford.

Artificial neural network approaches for prediction of backwater through arched bridge constrictions

This paper presents the findings of laboratory model testing of arched bridge constrictions in a rectangular open channel flume whose bed slope was fixed at zero. Four different types of arched bridge models, namely single opening semi-circular arch (SOSC), multiple opening semi-circular arch (MOSC), single opening elliptic arch (SOE), and multiple opening elliptic arch (MOE), were used in the testing program. The normal crossing (@f=0), and five different skew angles (@f=10^o, 20^o, 30^o, 40^o, and 50^o) were tested for each type of arched bridge model. The main aim of this study is to develop a suitable model for estimating backwater through arched bridge constrictions with normal and skewed crossings. Therefore, different artificial neural network approaches, namely multi-layer perceptron (MLP), radial basis neural network (RBNN), generalized regression neural network (GRNN), and multi-linear and multi-nonlinear regression models, MLR and MNLR, respectively were used. Results of these experimental studies were compared with those obtained by the MLP, RBNN, GRNN, MLR, and MNLR approaches. The MLP produced more accurate predictions than those of the others.

3D model for prediction of flow profiles around bridges

Computational fluid dynamics models have become well established as tools for simulating free surface flow over a wide range of structures. This study is an assessment and comparison of the performance of a commercially available three-dimensional numerical software, which solves the Reynolds-averaged Navier–Stokes equations, to predict the free surface profiles from up- to downstream of four different bridge types with and without piers in a compound channel. The model results were compared with the available experimental data. Comparisons indicate that the model provides a reasonably good description of free surface profiles under both gradually and rapidly varied flow conditions in the bridge vicinity, respectively.

3D Numerical Modelling of Flow Around Skewed Bridge Crossing

Abstract This study investigates the performance of commercially available three-Dimensional (3D) numerical software, FLOW-3D, on the prediction of the water surface profiles using a series of experimental data obtained in a two stage channel with skewed bridge crossing. The experiments were carried out for four different types of bridge models with two different skew angles of ø = 30° and ø = 45°. FLOW-3D, which solves the Reynolds-averaged Navier - Stokes equations, was applied to experimental data for the prediction of water surface profiles along the compound channel from upstream to downstream. The comparison of free surface profiles of 3D model showed good agreement with the experimental data. Notably, the measured and computed afflux values are found to be almost identical.

Modeling nitrate concentrations in a moving bed sequencing batch biofilm reactor using an artificial neural network technique

AbstractIn this study, the performance data of a moving-bed sequencing batch biofilm reactor (MBSBBR) treating synthetic wastewater were simulated using multi-layer perceptron neural-network technique. Multi-linear regression (MLR) technique is also used for a comparison. The performance of MBSBBR was evaluated using these models for a set of experimental results obtained from a model reactor operated with different cycle times and temperatures. The experimental data were retrieved from a previous reported work. Operational time, temperature, ammonium nitrogen, and pH were used as inputs for modeling, whereas nitrate concentration was the output variable. The results of the models were compared using statistical criteria, such as mean square error, mean absolute error, mean absolute relative error, and determination coefficient (R2). The results showed that the multi-layer perceptron neural-network produced more accurate results than those of MLR, although the latter gave reasonable results.

Kinetic evaluation and process performance of an upflow anaerobic filter reactor degrading terephthalic acid

The anaerobic degradation of terephthalic acid (TA) as the sole organic carbon source was studied in an upflow anaerobic filter (UAF) reactor. The reactor was seeded with biomass obtained from a full-scale upflow anaerobic sludge bed (UASB) reactor and was used to treat wastewater from a petrochemical facility producing dimethyl terephthalate. The UAF reactor was operated for 252 d with a constant hydraulic retention time of 24 h, and the organic loading rate (OLR) was gradually increased from 1 to 10 g-chemical oxygen demand (COD)/L d. After a lag period of approximately 40 d, the COD removal efficiency increased exponentially and high removal rate values (≈90%) were obtained, except for at highest OLR (10 g-COD/L d). The high removal rates and the robustness of the reactor performance could be attributed to the formation of biofilm as well as granular sludge. The methane production rates (0.22 to 2.15 L/d) correlated well with the removed OLRs (0.3 to 6.8 g-COD/L d) during the various phases of treatment, indicating that the main mechanism of TA degradation occurs via methanogenic reactions. The average methane content of the produced biogas was 70.3%. The modified Stover-Kincannon model was found to be applicable for the anaerobic degradation of TA in UAFs (Umax = 64.5, KB = 69.1 g-COD/L d and Ymax = 0.27 L-CH4/g-CODremoved). These results suggest that UAF reactors are among the most effective reactor configurations for the anaerobic degradation of TA.

Prediction of afflux on undistorted scale bridge model

This paper presents the results and findings from several sets of experimental data on afflux around bridge waterways in overbank flow condition. The paper also investigates the accuracy, capability, and suitability of one-dimensional hydraulic river modelling software (HEC-RAS and ISIS) to model flow through bridge structure. To eliminate a scaling effect between the laboratory-scale experiments and model techniques, all experimental results were scaled up using an undistorted-scale model in which both the vertical and horizontal scale ratio was 100. A total of six methods of predicting afflux, the energy method, the momentum method, the water surface profile (WSPRO) method, Yarnells method (in HEC-RAS), the U.S. Bureau of Public Roads (USBPR) method, and the arch bridge method (in ISIS) were compared with scaled up experimental results. The results for ISIS are significantly different from the measured data and the output from HEC-RAS. The energy method and the momentum method in HEC-RAS are the most a...