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Showing 4 results for Artificial Neural Network
Neural Network Modeling and Prediction of Methane Fraction in Biogas from Landfill Bioreactors
M.j Zoqi, A Ghavidel,
Volume 2, Issue 2 (9-2009)
Abstract

Backgrounds and Objectives:A number of different technologies have recently been studied todetermine the best use of biogas, however, to choose optimize technologies of using biogas for energy recovery it is necessary to monitor and predict the methane percentage of biogas. In this study, a method is proposed for predicting the methane fraction in landfill gas originating from Labscalelandfill bioreactors, based on neural network.
Materials and Methods: In this study, two different systems were applied, to predict the methane fraction in landfill gas as a final product of anaerobic digestion, we used the leachate specifications as input parameters. In system I (C1), the leachate generated from a fresh-waste reactor was drained to recirculation tank, and recycled. In System II (C2), the leachate generated from a fresh waste landfill reactor was fed through a well-decomposed refuse landfill reactor, and at the same time, the leachate generated from a well-decomposed refuse landfill reactor recycled to a fresh waste landfill reactor.
Results: There is very good agreement in the trends between forecasted and measured data. R valuesare 0.999 and 0.997, and the obtained Root mean square error values are 1.098 and 2.387 for training and test data, respectively
Conclusion: The proposed method can significantly predict the methane fraction in landfill gasoriginating and, consequently, neural network can be use to optimize the dimensions of a plant using biogas for energy (i.e. heat and/or electricity) recovery and monitoring system.


Sensitivity Analysis of the Effective Input Parameters upon the Ozone Concentration using Artificial Neural Networks
Mohammadali Ghorbani, Leila Naghipour, Vahid Karimi, Reza Farhoudi,
Volume 6, Issue 1 (5-2013)
Abstract
Background and Objectives: Weather pollution, caused by Ozone (O3) in metropolitans, is one of the major components of pollutants, which damage the environment and hurt all living organisms. Therefore, this study attempts to provide a model for the estimation of O3 concentration in Tabriz at two pollution monitoring stations: Abresan and Rastekuche.
Materials and Methods: In this research, Artificial neural networks (ANNs) were used to consider the impact of the meteorological and weather pollution parameters upon O3 concentration, and weight matrix of ANNs with Garson equation were used for sensitivity analysis of the input parameters to ANNs.
 Results: The results indicate that the O3 concentration is simultaneously affected by the meteorological and the weather pollution parameters. Among the meteorological parameters used by ANNs, maximum temperature and among the air pollution parameters, carbon monoxide had the maximum effect.
Conclusion: The results are representative of the acceptable performance of ANNs to predict O3 concentration. In addition, the parameters used in the modeling process could assess variations of the ozone concentration at the investigated stations.
Forecasting Some of the Qualitative Parameters of Rivers Using Wavelet Artificial Neural Network Hybrid (W-ANN) Model (Case of study: Jajroud River of Tehran and Gharaso River of Kermanshah)
Hossein Banejad, Mahsa Kamali, Kimia Amirmoradi , Ehsan Olyaie,
Volume 6, Issue 3 (12-2013)
Abstract

Background and Objectives: Rivers are the most important resources supplying drinking, agricultural, and industrial water demand. Their quality fluctuates frequently due to crossing from different regions and beds as well as their direct relationship with their peripheral environments. Thus, it is essential to be considered the surveying and predicating changes in the water qualitative parameters in a river. In this study, in order to estimate some of the qualitative parameters (Total dissolved solids, electrical conductivity and sodium absorption rate) for Tehran Jajroud and Kermanshah Gharasu rivers, we used wavelet-artificial neural network (W-ANN) hybrid model during a statistical period of 24 years. Methods: We compared W-ANN model with ANN model in order to evaluate its capability in detecting signals and separating error signals for estimating water quality parameters of the abovementioned rivers. The evaluation of both models was performed by the statistical criteria including correlation coefficient, the Nash-Sutcliffe model efficiency coefficient (NS), the root mean square error (RMSE) and the mean absolute error (MAE). Results: The results showed that the optimized W-ANN with correlation coefficient of 0.9 has high capability to estimate SAR parameter in the stations studied. Moreover, we found that W-ANN had less error and higher accuracy in the case of EC and TDS parameters rather than ANN model. Conclusion: W-ANN proved high efficiency in forecasting of the water quality parameters of rivers, therefore, it can be used for decision making and assurance of monitoring results and optimizing the monitoring costs.


Application of artificial neural networks in the modeling of photocatalytic reduction of Cr(VI) by titanium dioxide nanoparticles: optimization of artificial neural network structure
M Sabonian, Ma Behnajady,
Volume 11, Issue 2 (9-2018)
Abstract
Background and Objective: Chromium is present in two oxidation forms of Cr(III) and Cr(VI). Cr(III) is less toxic than Cr(VI). The aim of this article was to optimize an artificial neural network structure in modeling the photocatalytic reduction of Cr(VI) by TiO2-P25 nanoparticles.
Materials and Methods: In this work, an artificial neural network (ANN) for the modeling photocatalytic reduction Cr(VI) by TiO2-P25 nanoparticles were used and its structure was optimized. The operating parameters were initial concentration of chromium, amount of photocatalyst, ultraviolet light irradiation time and pH. All the experiments were conducted in a batch photoreactor. The Cr(VI) concentration was measured with a UV/Vis spectrophotometer. ANN calculations were performed using Matlab 7 software and the ANN toolbox.
Results: The results show that the optimization of the ANN structure and the use of an appropriate algorithm and transfer function could significantly improve performance. The proposed neural network in modeling the photoactivity of TiO2-P25 nanoparticles in reducing Cr(VI) was acceptable, based on a good correlation coefficient (0.9886) and a small mean square error (0.00018). All the input variables affected the reduction of Cr(VI), however the effect of pH with an impact factor of 34.15 % was more significant than the others. The results indicated that pH = 2 was the best pH for photocatalytic reduction of Cr(VI). Increasing photocatalyst dosage and irradiation time in the investigated range increased Cr(VI) photocatalytic reduction.
Conclusion: Optimized structure of the ANN includes a three-layer feed-forward back propagation network with 4:10:1 topology and the most appropriate algorithm is a scaled conjugate gradient backpropagation algorithm.
 

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