The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Vassilios A Tsihrintzis - One of the best experts on this subject based on the ideXlab platform.
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total nitrogen and ammonia removal prediction in horizontal subsurface flow constructed wetlands use of artificial neural networks and development of a Design Equation
Bioresource Technology, 2009Co-Authors: Christos S Akratos, John N E Papaspyros, Vassilios A TsihrintzisAbstract:The aim of this paper is to examine if artificial neural networks (ANNs) can predict nitrogen removal in horizontal subsurface flow (HSF) constructed wetlands (CWs). ANN development was based on experimental data from five pilot-scale CW units. The proper selection of the components entering the ANN was achieved using principal component analysis (PCA), which identified the main factors affecting TN removal, i.e., porous media porosity, wastewater temperature and hydraulic residence time. Two neural networks were examined: the first included only the three factors selected from the PCA, and the second included in addition meteorological parameters (i.e., barometric pressure, rainfall, wind speed, solar radiation and humidity). The first model could predict TN removal rather satisfactorily (R(2)=0.53), and the second resulted in even better predictions (R(2)=0.69). From the application of the ANNs, a Design Equation was derived for TN removal prediction, resulting in predictions comparable to those of the ANNs (R(2)=0.47). For the validation of the results of the ANNs and of the Design Equation, available data from the literature were used and showed a rather satisfactory performance.
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an artificial neural network model and Design Equations for bod and cod removal prediction in horizontal subsurface flow constructed wetlands
Chemical Engineering Journal, 2008Co-Authors: Christos S Akratos, John N E Papaspyros, Vassilios A TsihrintzisAbstract:A model is presented, which can be used in the Design of horizontal subsurface flow (HSF) constructed wetlands. This model was developed based on experimental data from five pilot-scale CW units, used in conjunction with artificial neural networks. The CWs were operated for a two-year period under four different hydraulic residence times (HRT). For the proper selection of the parameters entering the neural network, a principal component analysis (PCA) was performed first. From the PCA and model results, it occurs that the main parameters affecting BOD removal are porous media porosity, wastewater temperature and hydraulic residence time, and a set of other parameters which include the meteorological ones. Two artificial neural networks (ANNs) were examined: the first included only the three main parameters selected from the PCA, and the second included, in addition, the meteorological parameters. The first ANN predicted BOD removal rather satisfactorily and the second one examined resulted in even better predictions. From the predictions of the ANNs, a hyperbolic Design Equation, which combines zero and first order kinetics, was produced to predict BOD removal. The results of the ANNs and of the model Design Equation were compared to available data from the literature, and showed a rather satisfactory correlation. COD removal was found to be strongly correlated to BOD removal. An Equation for COD removal prediction was also produced.
Christos S Akratos - One of the best experts on this subject based on the ideXlab platform.
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total nitrogen and ammonia removal prediction in horizontal subsurface flow constructed wetlands use of artificial neural networks and development of a Design Equation
Bioresource Technology, 2009Co-Authors: Christos S Akratos, John N E Papaspyros, Vassilios A TsihrintzisAbstract:The aim of this paper is to examine if artificial neural networks (ANNs) can predict nitrogen removal in horizontal subsurface flow (HSF) constructed wetlands (CWs). ANN development was based on experimental data from five pilot-scale CW units. The proper selection of the components entering the ANN was achieved using principal component analysis (PCA), which identified the main factors affecting TN removal, i.e., porous media porosity, wastewater temperature and hydraulic residence time. Two neural networks were examined: the first included only the three factors selected from the PCA, and the second included in addition meteorological parameters (i.e., barometric pressure, rainfall, wind speed, solar radiation and humidity). The first model could predict TN removal rather satisfactorily (R(2)=0.53), and the second resulted in even better predictions (R(2)=0.69). From the application of the ANNs, a Design Equation was derived for TN removal prediction, resulting in predictions comparable to those of the ANNs (R(2)=0.47). For the validation of the results of the ANNs and of the Design Equation, available data from the literature were used and showed a rather satisfactory performance.
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an artificial neural network model and Design Equations for bod and cod removal prediction in horizontal subsurface flow constructed wetlands
Chemical Engineering Journal, 2008Co-Authors: Christos S Akratos, John N E Papaspyros, Vassilios A TsihrintzisAbstract:A model is presented, which can be used in the Design of horizontal subsurface flow (HSF) constructed wetlands. This model was developed based on experimental data from five pilot-scale CW units, used in conjunction with artificial neural networks. The CWs were operated for a two-year period under four different hydraulic residence times (HRT). For the proper selection of the parameters entering the neural network, a principal component analysis (PCA) was performed first. From the PCA and model results, it occurs that the main parameters affecting BOD removal are porous media porosity, wastewater temperature and hydraulic residence time, and a set of other parameters which include the meteorological ones. Two artificial neural networks (ANNs) were examined: the first included only the three main parameters selected from the PCA, and the second included, in addition, the meteorological parameters. The first ANN predicted BOD removal rather satisfactorily and the second one examined resulted in even better predictions. From the predictions of the ANNs, a hyperbolic Design Equation, which combines zero and first order kinetics, was produced to predict BOD removal. The results of the ANNs and of the model Design Equation were compared to available data from the literature, and showed a rather satisfactory correlation. COD removal was found to be strongly correlated to BOD removal. An Equation for COD removal prediction was also produced.
E M Dexter - One of the best experts on this subject based on the ideXlab platform.
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Design Equation for overlap tubular k joints under axial loading
Journal of Structural Engineering-asce, 2000Co-Authors: F Gazzola, Marcus M K Lee, E M DexterAbstract:Using a previously presented finite-element study of axially loaded overlap tubular K-joints as a platform, this paper presents further work that aimed at extending and enhancing the strength estimation Equation already presented. The current work includes extending the brace angle validity range to between 30 and 60; investigating the effects of the yield stress/ultimate tensile strength ratio and the brace/chord yield stress ratio; exploring the influence of loading reversal; and quantifying the reduction in strength caused by the absence of the hidden weld. The end result of these studies is a new, extended capacity Equation. The accuracy of the proposed Equation to predict the axial capacity of test joints was established by assessing it using an experimental database. The applicability of the proposed Equation for Design practice was further ascertained by investigating strength sensitivities to material curve variations, different chord end support conditions, and joints with unequal brace inclinations. The proposed Design Equation is found to be both accurate and conservative and is therefore recommended to be used in practice.
William M. Bulleit - One of the best experts on this subject based on the ideXlab platform.
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new yield model for wood dowel connections
Journal of Structural Engineering-asce, 2010Co-Authors: Joseph F Miller, William M. Bulleit, Richard J SchmidtAbstract:The current National Design Specification (NDS) for Wood Construction Design methods for dowel-type connectors are based on four-yield modes for connections in double shear. These yield modes were formulated for use with steel dowels as fasteners. Hence Design with nonferrous fasteners, such as wood pegs, common in timber frame joinery, is not addressed. Wood pegs, while large in diameter, are considerably more flexible than steel dowels of the same size. A fifth failure mode is proposed for use with wood pegs. This new failure mode, called Mode V yielding, which is an effective cross-grain dowel failure, has been observed in physical testing as well as numerical modeling. The objective of this paper is to establish a Design procedure for the Mode V yielding of pegs. The procedure is calibrated to the level of performance (reliability) expected from the other yield modes. A regression Equation to relate the effective cross-grain yield capacity of pegs to specific gravity is also developed for use with the Mode V Design Equation. A reliability analysis indicates that the proposed Mode V Design Equation can be used in conjunction with existing NDS yield Equations for Design of wood doweled connections.
John N E Papaspyros - One of the best experts on this subject based on the ideXlab platform.
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total nitrogen and ammonia removal prediction in horizontal subsurface flow constructed wetlands use of artificial neural networks and development of a Design Equation
Bioresource Technology, 2009Co-Authors: Christos S Akratos, John N E Papaspyros, Vassilios A TsihrintzisAbstract:The aim of this paper is to examine if artificial neural networks (ANNs) can predict nitrogen removal in horizontal subsurface flow (HSF) constructed wetlands (CWs). ANN development was based on experimental data from five pilot-scale CW units. The proper selection of the components entering the ANN was achieved using principal component analysis (PCA), which identified the main factors affecting TN removal, i.e., porous media porosity, wastewater temperature and hydraulic residence time. Two neural networks were examined: the first included only the three factors selected from the PCA, and the second included in addition meteorological parameters (i.e., barometric pressure, rainfall, wind speed, solar radiation and humidity). The first model could predict TN removal rather satisfactorily (R(2)=0.53), and the second resulted in even better predictions (R(2)=0.69). From the application of the ANNs, a Design Equation was derived for TN removal prediction, resulting in predictions comparable to those of the ANNs (R(2)=0.47). For the validation of the results of the ANNs and of the Design Equation, available data from the literature were used and showed a rather satisfactory performance.
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an artificial neural network model and Design Equations for bod and cod removal prediction in horizontal subsurface flow constructed wetlands
Chemical Engineering Journal, 2008Co-Authors: Christos S Akratos, John N E Papaspyros, Vassilios A TsihrintzisAbstract:A model is presented, which can be used in the Design of horizontal subsurface flow (HSF) constructed wetlands. This model was developed based on experimental data from five pilot-scale CW units, used in conjunction with artificial neural networks. The CWs were operated for a two-year period under four different hydraulic residence times (HRT). For the proper selection of the parameters entering the neural network, a principal component analysis (PCA) was performed first. From the PCA and model results, it occurs that the main parameters affecting BOD removal are porous media porosity, wastewater temperature and hydraulic residence time, and a set of other parameters which include the meteorological ones. Two artificial neural networks (ANNs) were examined: the first included only the three main parameters selected from the PCA, and the second included, in addition, the meteorological parameters. The first ANN predicted BOD removal rather satisfactorily and the second one examined resulted in even better predictions. From the predictions of the ANNs, a hyperbolic Design Equation, which combines zero and first order kinetics, was produced to predict BOD removal. The results of the ANNs and of the model Design Equation were compared to available data from the literature, and showed a rather satisfactory correlation. COD removal was found to be strongly correlated to BOD removal. An Equation for COD removal prediction was also produced.