Modeling and Optimization of Poly Electrolyte Dosage in Water Treatment Process by GMDH Type- NN and MOGA

Akbari-Zadeh, Mohammad-Reza; Daghbandan, Allahyar; Yaghoobi, Mohammad Ali

doi:10.4018/ijcce.2013070107

Cited by 3 publications

(8 citation statements)

References 16 publications

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“…The actual results for M experimental data with n inputs and one output is therefore shown as Equation ( 1) [21,26,27,29,30]:…”

Section: Overview Of Gmdhmentioning

confidence: 99%

“…The second degree of the above-mentioned equations, which will be used in this research, can be written as below [21,27,29,30]:…”

Section: Overview Of Gmdhmentioning

confidence: 99%

“…The a i unknown coefficients are obtained by regression methods in such a manner which the differences between the real output 'y' and the calculated ' ŷ' values are minimized for each pair of input variable x i and x j . A set of polynomials is made by considering the above-mentioned equation and all their unknown coefficients are obtained through considering the least square approach [21,27,29,30]. The coefficients of the equations of each neuron for minimizing its total error, to efficiently match the inputs with all the pairs of input-output sets, are obtained for each G i function (each generated neuron).…”

Section: Overview Of Gmdhmentioning

confidence: 99%

“…Group method of data handling (GMDH) is one type of artificial intelligence approach that presents elementary equations for calculating the problem's target parameter and performing well in nonlinear complex systems [20][21][22][23][24]. However, many numerical and experimental methods have been utilized to predict and measure the velocity at open channel intakes [25,26].…”

Section: Introductionmentioning

confidence: 99%

“…Due to these problems, soft computing techniques can be a desirable alternative for conventional tools to estimate mean velocity in open channel intakes for various conditions. In order to train GMDH technique to predict velocity, the thorough flow field dataset (resulted from ANSYS-CFX simulation) is needed [20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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Combination of Group Method of Data Handling (GMDH) and Computational Fluid Dynamics (CFD) for Prediction of Velocity in Channel Intake

et al. 2020

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This paper utilizes computational fluid dynamics as well as a group method of data handling (GMDH) method to predict the mean velocity of intake. Firstly, the three dimensional flow pattern in a 90-degree intake is simulated with ANSYS-CFX at a transverse ratio equal to one (W*b/W*m = 1) that W*m is the width of the main channel and W*b is the width of the branch channel. The comparison of mean velocity in the simulated intake and experimental channel represents the high accuracy of ANSYS-CFX modeling (mean absolute percentage error (MAPE) = 5% and root mean square error (RMSE) = 0.017). A group method of data handling (GMDH) is one type of artificial intelligence approach that presents elementary equations for calculating the problem’s target parameter and performing well in complex nonlinear systems. In this research, to train and test the GMDH method, input data is needed in all parts of the channel. Since there is not enough laboratory data in all parts of the channel, to increase the benchmarks, the laboratory model is simulated by the Computational Fluid Dynamics (CFD) numerical model. After ensuring the proper accuracy of the numerical results, the built-in CFD numerical model has been used as a tool to create primary benchmarks in the channel points, especially in areas where there is no laboratory data. This generated data has been used in training and testing the GMDH method. The diversion angle with the longitudinal direction of the main channel (θ), the longitudinal coordinates in the intake (y*), and the ratio of the branch channel width to the main channel (Wr) have been applied as the input training data in the GMDH method to estimate mean velocity. The results of the statistical indexes used to quantitatively examine this model, (R2 = 0.86, MAPE = 10.44, RMSE = 0.03, SI = 0.12), indicated the accuracy of this model in predicting the mean velocity of the flow within open channel intakes.

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“…The actual results for M experimental data with n inputs and one output is therefore shown as Equation ( 1) [21,26,27,29,30]:…”

Section: Overview Of Gmdhmentioning

confidence: 99%

“…The second degree of the above-mentioned equations, which will be used in this research, can be written as below [21,27,29,30]:…”

Section: Overview Of Gmdhmentioning

confidence: 99%

Section: Overview Of Gmdhmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combination of Group Method of Data Handling (GMDH) and Computational Fluid Dynamics (CFD) for Prediction of Velocity in Channel Intake

et al. 2020

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Investigating the management performance of disinfection analysis of water distribution networks using data mining approaches

Zounemat‐Kermani

Ramezani-Charmahineh

Adamowski

et al. 2018

Environ Monit Assess

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Chlorination, the basic treatment utilized for drinking water sources, is widely used for water disinfection and pathogen elimination in water distribution networks. Thereafter, the proper prediction of chlorine consumption is of great importance in water distribution network performance. In this respect, data mining techniques-which have the ability to discover the relationship between dependent variable(s) and independent variables-can be considered as alternative approaches in comparison to conventional methods (e.g., numerical methods). This study examines the applicability of three key methods, based on the data mining approach, for predicting chlorine levels in four water distribution networks. ANNs (artificial neural networks, including the multi-layer perceptron neural network, MLPNN, and radial basis function neural network, RBFNN), SVM (support vector machine), and CART (classification and regression tree) methods were used to estimate the concentration of residual chlorine in distribution networks for three villages in Kerman Province, Iran. Produced water (flow), chlorine consumption, and residual chlorine were collected daily for 3 years. An assessment of the studied models using several statistical criteria (NSC, RMSE, R, and SEP) indicated that, in general, MLPNN has the greatest capability for predicting chlorine levels followed by CART, SVM, and RBF-ANN. Weaker performance of the data-driven methods in the water distribution networks, in some cases, could be attributed to improper chlorination management rather than the methods' capability.

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Mühendislik Alanında Akademik Analiz ve Tartışmalar

2023

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Mühendislik bilim, teknoloji ve matematiği birbirine bağlamayı amaçlar ve bir disiplin olarak binlerce yıldır uygulanmaktadır. Mühendisler, hücre zarlarından inşaat ve protezlere, motor ve taşıma verimliliğinin iyileştirilmesine ve yenilenebilir enerji kaynaklarının geliştirilmesine kadar her konuda çalışırlar. Mühendislik, tekerleğin icadına (ve ötesine) kadar uzanıyor olsa da, terimin kendisi, bir "mühendis"in mancınık ve diğer "kuşatma makineleri" gibi askeri motorlar inşa eden kişi anlamına geldiği 14. yüzyıla kadar uzanmaktadır. Mühendislik, askeri uygulamaların ötesinde gelişerek köprüler ve binalar gibi sivil yapılara uygulanmaya başlanmıştır. Bu da onu orijinal askeri mühendislik alanından ayırmak için inşaat mühendisliği teriminin yaratılmasına yol açmıştır. Bu kitapta mühendislik bilimleri incelenmiştir.

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Modeling and Optimization of Poly Electrolyte Dosage in Water Treatment Process by GMDH Type- NN and MOGA

Cited by 3 publications

References 16 publications

Combination of Group Method of Data Handling (GMDH) and Computational Fluid Dynamics (CFD) for Prediction of Velocity in Channel Intake

Combination of Group Method of Data Handling (GMDH) and Computational Fluid Dynamics (CFD) for Prediction of Velocity in Channel Intake

Investigating the management performance of disinfection analysis of water distribution networks using data mining approaches

Mühendislik Alanında Akademik Analiz ve Tartışmalar

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