The Adaptive Neuro-Fuzzy Inference System (ANFIS) Application for the Ammonium Removal from Aqueous Solution Predicting by Biochar

Khalil, Ahmed; Starovoytov, Sergey V.; Серпокрылов, Николай Сергеевич

doi:10.4028/www.scientific.net/msf.931.985

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…The structure of this algorithm consists of inputs and outputs represented by neurons, and between them, there are connections used to transfer the weights given to each cell according to its effect. The backpropagating algorithm is characterized by its ability to change the neuron weights to reduce the differences between the goals and the output values of the algorithm using the error reduction technique [26]. The final set of node biases and connection weights is known when the error rate is reduced to permissible limits [27].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the elastic modulus of pavement layers using different types of neural networks models

Elshamy

Tiraturyan

Uglova

2022

Vestnik Donskogo gosudarstvennogo tehničeskogo universiteta

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Introduction. This paper studies the capability of different types of artificial neural networks (ANN) to predict the modulus of elasticity of pavement layers for flexible asphalt pavement under operating conditions. The falling weight deflectometer (FWD) was selected to simulate the dynamic traffic loads and measure the flexural bowls on the road surface to obtain the database of ANN models.Materials and Methods. Artificial networks types (the feedforward backpropagation, layer-recurrent, cascade back- propagation, and Elman backpropagation) are developed to define the optimal ANN model using Matlab software. To appreciate the efficiency of every model, we used the constructed ANN models for predicting the elastic modulus values for 25 new pavement sections that were not used in the process of training, validation, or testing to ensure its suitability. The efficiency measures such as mean absolute error (MAE), the coefficient of multiple determinations R2, Root Mean Square Error (RMSE), Mean Absolute Percent Error (MAPE) values were obtained for all models results.Results. Based on the performance parameters, it was concluded that among these algorithms, the feed-forward model has a better performance compared to the other three ANN types. The results of the best four models were compared to each other and to the actual data obtained to determine the best method.Discussion and Conclusions. The differences between the results of the four best models for the four types of algorithms used were very small, as they showed the closeness between them and the actual values. The research results confirm the possibility of ANN-based models to evaluate the elastic modulus of pavement layers speedily and reliably for using it in the structural assessment of (NDT) flexible pavement data at the appropriate time.

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Section: Methodsmentioning

confidence: 99%

Evaluation of the elastic modulus of pavement layers using different types of neural networks models

Elshamy

Tiraturyan

Uglova

2022

Vestnik Donskogo gosudarstvennogo tehničeskogo universiteta

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“…The discharge of NH4 and COD in the environment are worrying for both toxicological and economically reasons. Wastewater from fish farms and agricultural drainage are some of the sources for NH4 + and COD effluents [1,2] . Therefore, it is necessary to treat the wastewater containing COD and NH4 + for reusing it in fish farms.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Ammonium and COD Adsorption in Aqueous Solutions Using an Artificial Neural Network

Khalil¹,

Smolyanichenko²,

Vilson³

et al. 2019

Proceedings of the International Symposium "Engineering and Earth Sciences: Applied and Fundamental Research" Dedicated to the

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This paper illustrates the application of the artificial neural network for adsorption of ammonium NH4 + and COD from fish farm by rice straw as low cost carbonaceous. The effects of input parameters (contact time, pH, initial concentration of NH4 + and COD, adsorbent dosages, and temperature) are studied to optimize the conditions for maximum removal of NH4 + and COD. The artificial neural network with a single hidden layer with ten nodes trained with Levenberg-Marquardt algorithm predicted the removal efficiency of NH4 + and COD from aqueous solution accurately.

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Sustainable Management of Wastewater Treatment Plants Using Artificial Intelligence Techniques

Nasr

Mohamed

Attia

et al. 2021

Soft Computing Techniques in Solid Waste and Wastewater Management

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The Adaptive Neuro-Fuzzy Inference System (ANFIS) Application for the Ammonium Removal from Aqueous Solution Predicting by Biochar

Cited by 6 publications

References 14 publications

Evaluation of the elastic modulus of pavement layers using different types of neural networks models

Evaluation of the elastic modulus of pavement layers using different types of neural networks models

Modeling of Ammonium and COD Adsorption in Aqueous Solutions Using an Artificial Neural Network

Sustainable Management of Wastewater Treatment Plants Using Artificial Intelligence Techniques

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