Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach

Bagheri, Majid; Mirbagheri, Seyed Ahmad; Bagheri, Zeinab; Kamarkhani, Ali Morad

doi:10.1016/j.psep.2015.02.008

Cited by 126 publications

(55 citation statements)

References 83 publications

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“…The fitness function was applied for evaluating the merit of each chromosome. Then, selection operation was to choose some individuals from initial generation (parents) to breed a new generation (offspring) by utilizing the roulette wheel selection method [41]. Next, crossover operator was aimed to swap the information of parents that led to generating new individuals for the next generation.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

Ruan

Shi

et al. 2017

Applied Sciences

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Abstract:The commercially available nanoscale zerovalent zinc (nZVZ) was used as an adsorbent for the removal of malachite green (MG) from aqueous solutions. This material was characterized by X-ray diffraction and X-ray photoelectron spectroscopy. The advanced experimental design tools were adopted to study the effect of process parameters (viz. initial pH, temperature, contact time and initial concentration) and to reduce number of trials and cost. Response surface methodology and rapidly developing artificial intelligence technologies, i.e., artificial neural network coupled with particle swarm optimization (ANN-PSO) and artificial neural network coupled with genetic algorithm (ANN-GA) were employed for predicting the optimum process variables and obtaining the maximum removal efficiency of MG. The results showed that the removal efficiency predicted by ANN-GA (94.12%) was compatible with the experimental value (90.72%). Furthermore, the Langmuir isotherm was found to be the best model to describe the adsorption of MG onto nZVZ, while the maximum adsorption capacity was calculated to be 1000.00 mg/g. The kinetics for adsorption of MG onto nZVZ was found to follow the pseudo-second-order kinetic model. Thermodynamic parameters (∆G 0 , ∆H 0 and ∆S 0 ) were calculated from the Van't Hoff plot of lnKc vs. 1/T in order to discuss the removal mechanism of MG.

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Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

Ruan

Shi

et al. 2017

Applied Sciences

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“…The control system of sludge process can be divided into the thickening control and the dewatering control to reduce the control difficulties. In the thickening control, the different algorithms such as artificial intelligence (AI) techniques [2][3][4][5][6][7][8][9][10][11] have become one Figure 1. The control system of sludge process.…”

Section: Introductionmentioning

confidence: 99%

Research on Controlling Dewatering Ratio Based on Fuzzy PID in Sludge Process

Luo¹,

Liu²,

Liao³

2018

dtcse

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The sludge produced by treating the urban sewage needs thickening and dewatering and it is important to reduce the quantity of discharging sludge by controlling dewatering ratio in the process of sludge thickening and dewatering. In this paper, the fuzzy adaptive PID control algorithm is designed to replace the common PID control to be used to control dewatering ratio in sludge process. The results of the simulation show that the method is effective in improving the control of the dewatering ratio in sludge process 1

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“…In the last several years, the different algorithms such as artificial intelligence (AI) techniques [2][3][4][5][6][7][8][9][10][11] have become one of the most important topics in predicting the quality of sludge process. AI techniques produce better results than classic regression methods for developing the software sensors [2].…”

Section: Introductionmentioning

confidence: 99%

“…They concluded that ANN as the one of soft-sensing methods was capable to model accurately the sludge process. Majid Bagheri et al developed a hybrid artificial neural networks-genetic algorithm approach to improve the accuracy of the model and the prediction of the quality of sludge process by optimizing the key parameters of ANN [5].…”

Section: Introductionmentioning

confidence: 99%

Modeling Based on LS-SVM Method and Predicting Dewatering Ratio in Sludge Process

Luo¹,

Qiao²,

Liao³

2018

Proceedings of the 2017 3rd International Forum on Energy, Environment Science and Materials (IFEESM 2017)

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Abstract. The sludge produced by treating the urban sewage needs thickening and dewatering and it is important for improving environment to reduce the quantity of discharging sludge by increasing dewatering ratio in sludge process. This paper proposes a novel method that can model least squares support vector machines (LS-SVM) by Soft-sensing to meet the characteristics of multivariable, nonlinearity, large time delay, and time-varying in sludge thickening and dewatering process，and predict the dewatering ratio. The results indicate that the proposed method can not only improve prediction accuracy, but also efficiently get high dewatering ratio by controlling thickening chemicals comparing to the cases using different methods.

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Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach

Cited by 126 publications

References 83 publications

Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

Research on Controlling Dewatering Ratio Based on Fuzzy PID in Sludge Process

Modeling Based on LS-SVM Method and Predicting Dewatering Ratio in Sludge Process

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