Optimizing stabilization of waste-activated sludge using Fered-Fenton process and artificial neural network modeling (KSOFM, MLP)

Abstract

Sludge management is a fundamental activity in accordance with wastewater treatment aims. Sludge stabilization is always considered as a significant step of wastewater sludge handling. There has been a progressive development observed in the approach to the novel solutions in this regard. In this research, based on own initially experimental results in lab-scale regarding Fered-Fenton processes in view of organic loading (volatile-suspended solids, VSS) removal efficiency, a combination of both methods towards proper improving of excess biological sludge stabilization was investigated. Firstly, VSS removal efficiency has been experimentally studied in lab-scale under different operational conditions taking into consideration pH [Fe(2+)]/[H2O2], detention time [H2O2], and current density parameters. Therefore, the correlations of the same parameters have been determined by utilizing Kohonen self-organizing feature maps (KSOFM). In addition, multi-layer perceptron (MLP) has been employed afterwards for a comprehensive evaluation of investigating parameters correlation and prediction aims. The findings indicated that the best proportion of iron to hydrogen peroxide and the optimum pH were 0.58 and 3.1, respectively. Furthermore, maximum retention time about 6 h with a hydrogen peroxide concentration of 1,568 mg/l and a current density of 650-750 mA results to the optimum VSS removal (efficiency equals to 81 %). The performance of KSOFM and MLP models is found to be magnificent, with correlation ranging (R) from 0.873 to 0.998 for the process simulation and prediction. Finally, it can be concluded that the Fered-Fenton reactor is a suitable efficient process to reduce considerably sludge organic load and mathematical modeling tools as artificial neural networks are impressive methods of process simulation and prediction accordingly.

Characterizing dissolved organic matter in Taihu Lake with PARAFAC and SOM method

“…That is, when processing high-dimensional data, the topological structure of points with high-dimensional coordinates can be mapped better, to low-dimensional space and maximally preserve the correlation between the original data to meet the requirements for data confidence and accuracy (Kalteh et al 2008); since its training process is an unsupervised self-organizing process, it can be used to discover some hidden data of the input data structure. In the field of environmental science, this method can be used for classification of water resources (Kalteh et al 2008;Cereghino & Park 2009), watershed monitoring analysis (Jampani et al 2018), water treatment evaluation (Llorens et al 2008;Gholikandi et al 2014), pipe network analysis (Ghasemi et al 2019), etc. However, its application on the analysis of fluorescence characterization of DOM in surface water has never been reported.…”

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

“…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.…”

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

“…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]. Under this circumstance, soft-sensor based on AI techniques gradually becomes the hot area for predicting the quality of sludge process.…”