Artificial intelligence-based estimation of flushing half-cone geometry

An accurate estimation of half-cone geometry (i.e., volume and length) created by pressure flushing operation in dam reservoirs is required for sediment management in the reservoir storage. In this study, two artificial intelligence techniques namely, Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference System (ANFIS) were utilized to estimate the volume and length of flushing half-cone based on influential variables, i.e., mean flow velocity through bottom outlet (u), water depth in reservoir (H_w), mean grain diameter of deposited sediments (d₅₀), thickness of deposited sediment (H_s) and bottom outlet diameter (D). Experimental data in both dimensional and non-dimensional forms were used to train and test ANN and ANFIS models. The results of the intelligence-based models were also compared with those of existing studies. The outcomes indicated that both ANN and ANFIS models predict the volume and length of flushing half-cone more accurately than existing studies. Also, it was found that the ANN model provides a better estimation of the geometry of flushing half-cone compared to the ANFIS model. Finally, sensitivity analysis was conducted to determine the most and the least influential variables affecting the flushing half-cone geometry. It was found that the sediment characteristics (H_s and d₅₀) and fluids properties (H_w and u) have respectively the most and the least effect on flushing half-cone volume and length.     

Estimation of soil dispersivity using soft computing approaches

The accurate estimation of soil dispersivity (α) is required for characterizing the transport of contaminants in soil. The in situ measurement of α is costly and time-consuming. Hence, in this study, three soft computing methods, namely adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and gene expression programming (GEP), are used to estimate α from more readily measurable physical soil variables, including travel distance from source of pollutant (L), mean grain size (D ₅₀), soil bulk density (ρ _b), and contaminant velocity (V _c). Based on three statistical metrics [i.e., mean absolute error, root-mean-square error (RMSE), and coefficient of determination (R ²)], it is found that all approaches (ANN, ANFIS, and GEP) can accurately estimate α. Results also show that the ANN model (with RMSE = 0.00050 m and R ² = 0.977) performs better than the ANFIS model (with RMSE = 0.00062 m and R ² = 0.956), and the estimates from GEP are almost as accurate as those from ANFIS. The performance of ANN, ANFIS, and GEP models is also compared with the traditional multiple linear regression (MLR) method. The comparison indicates that all of the soft computing methods outperform the MLR model. Finally, the sensitivity analysis shows that the travel distance from source of pollution (L) and bulk density (ρ _b) have, respectively, the most and the least effect on the soil dispersivity.

     

Electrochemical preparation and properties of polypyrrole and its blend with poly(vinyl sulfonate) doped with perchlorate anions

In this article, we report polypyrrole (PPy)/poly(vinyl sulfonate) (PVS) and PPy/perchlorate (ClO) composite films generated by the electrochemical oxidation of pyrrole on a glassy carbon electrode (GCE) in an aqueous solution. The response of the produced films to an applied potential at 0.7 V was obtained by a cyclic voltammetry study in acetonitrile media. The films were significantly similar in their electrochemical behavior when ClO ions doped during the redox process. We concluded that with an increasing number of cycles, the anodic current increased because the number of the electroactive participants transported in the copolymer matrix was increased. Theoretical studies based on the Nernst and Butler–Volmer equations indicated that the ClO ion was transported during the oxidation/reduction process of the PPY/PVS and PPY/ClO films. The produced films were characterized further by means of IR spectroscopy, electrochemical impedance spectroscopy, and scanning electron microscopy to verify that the anion of ClO was doped into the copolymer matrix as well. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010