Fayazeh Rabanimehr; Mehrdad Farhadian; Ali Reza Solaimany Nazar; Elham Sadat Behineh
Abstract
In photocatalytic microreactors the catalyst layer is obtained by integration of nanostructure films of semiconductors. One of these nanostructures that have a good photocatalytic activity is ZnO nanowires. The photocatalytic degradation of methylene blue in a continuous flow microreactor with ZnO nanowires ...
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In photocatalytic microreactors the catalyst layer is obtained by integration of nanostructure films of semiconductors. One of these nanostructures that have a good photocatalytic activity is ZnO nanowires. The photocatalytic degradation of methylene blue in a continuous flow microreactor with ZnO nanowires deposited film is simulated. A finite element model is developed using COMSOL Multiphysics version 5.3 software to simulate the microreactor performance. The kinetic law of the photocatalytic reaction is assumed to be Langmuir–Hinshelwood. The kinetic constants kLHa and K are determined 1.43×10-7 mol/m2s and 7.5 m3/mol, respectively. The percent of average absolute deviation of the model in predicting the methylene blue outlet concentration obtained about 0.12% mol/m3. The model showed a very good agreement with the published experimental data. The effect of microreactor depth, methylene blue inlet concentration and flow rate on the methylene blue degradation is also investigated. The simulation results showed that the microreactor with shorter depth and lower values of inlet concentration and flow rate has higher efficiency. Thiele modulus and Damköhler number are both estimated lower than 1. It indicates that the photocatalytic reactions occur without internal and bulk mass transfer limitations.
Fariborz Yosefvand; Saeid Shabanlou
Abstract
In this study, for the first time, groundwater level (GWL) variations of the Sarab-e Qanbar well located in the city of Kermanshah, are simulated over a 13-year period by a hybrid model named WANFIS (wavelet-adaptive neuro fuzzy inference system). In order to develop the hybrid model, the wavelet transform ...
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In this study, for the first time, groundwater level (GWL) variations of the Sarab-e Qanbar well located in the city of Kermanshah, are simulated over a 13-year period by a hybrid model named WANFIS (wavelet-adaptive neuro fuzzy inference system). In order to develop the hybrid model, the wavelet transform and the adaptive neuro fuzzy inference system (ANFIS) model are utilized. Furthermore, the 9 and 4 year data are used for training and testing the artificial intelligence models, respectively. Moreover, the effective lags are detected by the autocorrelation function (ACF) and then eight different models are developed for each of the ANFIS and WANFIS models using them. After that, all mother wavelets are evaluated and Dmey mother wavelet is chosen as the most optimal. For this mother wavelet, the values of scatter index (SI), variance account for (VAF) and Root mean square error (RMSE) are obtained 0.192, 94.951 and 3.117, respectively. Next, the superior model is detected through the analysis of the results obtained by all ANFIS and WANFIS models. The superior model estimates the objective function values with reasonable accuracy. For example, the correlation coefficient (R), Scatter Index (SI) and variance account for (VAF) for this model are obtained 0.974, 0.192 and 94.951, respectively. The modeling results indicate that the wavelet transform noticeably enhances the ANFIS model accuracy. Finally, the lags of the time series data for the Sarab-e Qanbar well including (t-1), (t-2), (t-3) and (t-4) are introduced as the most effective lags.
Hamidreza Babaali; Zohreh Ramak; Reza Sepahvand
Abstract
Predicting the river discharge is one of the important subjects in water resourcesengineering. This subject is of utmost importance in terms of planning,management, and policy of water resources with the aim of economic andenvironmental development, especially in a country like Iran with limited waterresources. ...
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Predicting the river discharge is one of the important subjects in water resourcesengineering. This subject is of utmost importance in terms of planning,management, and policy of water resources with the aim of economic andenvironmental development, especially in a country like Iran with limited waterresources. Awareness of the relation between rainfall and runoff of basins is aninseparable past of water design studies. Lack of sufficient data on rainfall-runoffdue to the absence of appropriate hydrometric stations reveals the importanceof using indirect methods and heuristic algorithms for estimating the basins'runoff more than before. In the present research, the genetic programmingmodel has been employed to simulate the rainfall-runoff process ofKhorramabad River basin, and in order to introduce the patterns and identify thebest pattern dominating the nature of flow, all statistical data were divided intotwo groups of training and experiment (52 percent training and 48 percentexperiment) and the program was implemented for 1000 replications using fittingfunctions and going through replication and developmental processes so as tofind the optimal replication. Moreover, in order to evaluate the relations obtainedfrom the simulator model, Root Mean Square Error (RMSE) and Mean SquaredError (MSE) indexes and Coefficient of Determination (R2) have been used. Theinvestigations demonstrate that the employed equation 3 has the greatestrelevance with the observational data. Therefore, it is recommended that the saidequation be used for the rainfall-runoff studies of the abovementioned basin.Based on the results, the genetic programming model is an accurate directmethod for predicting the discharge of Khorramabad River basin.
