Hamidreza Masihi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Hamidreza Masihi has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Gagik Badalians Gholikandi
Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
4 shared publications Gagik Badalians Gholikandi
2 shared publicationsAli Abrishami
Shahid Beheshti University, A.C.
1 shared publicationHamed Rasouli Sadabad
1 shared publicationMaryam Mirabi
Shahid Beheshti University, A.C.
1 shared publication- Mohammad Azimipour
Shahid Beheshti University, A.C.
1 shared publication
Latest Publications
- 1 February 2020
- Published by Elsevier in Chemosphere
- Vol. 241, 125096
Using acidic-modified bentonite for anaerobically digested sludge conditioning and dewatering
- 1 February 2020
- Published by Elsevier in Chemosphere
- Vol. 241, 125096
Using acidic-modified bentonite for anaerobically digested sludge conditioning and dewatering
- 1 November 2018
- Published by Elsevier in Water Research
- Vol. 144, 373-382
Employing Electrochemical-Fenton process for conditioning and dewatering of anaerobically digested sludge: A novel approach
- 1 November 2018
- Published by Elsevier in Water Research
- Vol. 144, 373-382
Employing Electrochemical-Fenton process for conditioning and dewatering of anaerobically digested sludge: A novel approach
- Open Access
- 1 October 2017
- Published by Elsevier in Desalination and Water Treatment
- Vol. 93, 250-256
Heavy metal ions removal from waste-activated sludge by Fered-Fenton electrochemical advanced oxidation process (EAOP) with the aim of agricultural land application
- 1 October 2017
- Published by Elsevier in Desalination and Water Treatment
- Vol. 93, 250-256
Open AccessHeavy metal ions removal from waste-activated sludge by Fered-Fenton electrochemical advanced oxidation process (EAOP) with the aim of agricultural land application
- 1 January 2017
- Published by Desalination Publications
- Vol. 93, 267-273
Employing Fered-Fenton advanced oxidation process for waste-activated sludge stabilization and reuse
- 1 January 2017
- Published by Desalination Publications
- Vol. 93, 267-273
Employing Fered-Fenton advanced oxidation process for waste-activated sludge stabilization and reuse
- 25 February 2014
- Published by Springer Nature in Environmental Science and Pollution Research
- Vol. 21 (11) , 7177-7186
Optimizing stabilization of waste-activated sludge using Fered-Fenton process and artificial neural network modeling (KSOFM, MLP)
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 [Fe2+]/[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.
- 25 February 2014
- Published by Springer Nature in Environmental Science and Pollution Research
- Vol. 21 (11) , 7177-7186
Optimizing stabilization of waste-activated sludge using Fered-Fenton process and artificial neural network modeling (KSOFM, MLP)
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 [Fe2+]/[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.