Search published articles


Showing 2 results for Mehrdadi
Self-organizing map neural network application in detecting the extent of intentional pollution in the urban water distribution network
Naser Mehrdadi, Davood Vafaei Mehr, Gholamreza Nabi Bidhendi, Hassan Hoveidi,
Volume 15, Issue 1 (4-2022)
Abstract
Background and Objective: Water distribution networks are prone to terrorist attacks by injecting toxic substances, due to their vastness and availability. The main objective of this paper was detecting the extent of intentional pollution in the urban water distribution network by self-organizing map (SOM).
Materials and Methods: The existing hydraulic condition of the water distribution network covered by reservoir No. 4 in Tehran was modeled as a pilot. Possible injection scenarios of contamination in different parts of the water distribution network were performed using qualitative analysis of the water distribution network, using the EPANET analyzer engine and coding in R software environment. Artificial neural network of SOM was used to find the contamination range for the injection of arsenic at different times and places in the distribution network.
Results: The concentration of contamination at a certain point decreased over time and a high correlation was observed between time and concentration. The extent of contamination depended on the consumption of subscribers and consequently, the time of contaminat injection. The results of the artificial neural network model showed that the method developed in this research was 91% accurate and was able to determine the extent of contamination in the water distribution network at high speed.
Conclusion: SOM can be used as a complement to the water quality monitoring and pollution detection system in the urban water distribution network to determine the extent of pollution when detecting potential pollution in the shortest possible time, and as an alternative to quantitative-qualitative modeling of the water network.
 

Investigating the effect of micro aerobic process on the efficiency of anaerobic digester of sulfate-rich municipal sewage sludge
Abbas Ali Moserzadeh, Gholamreza Nabi Bidhendi, Nasser Mehrdadi, Mohammad Javad Amiri,
Volume 17, Issue 1 (6-2024)
Abstract
Background and Objective: A high concentration of Hydrogen Sulfide in biogas is a major problem associated with anaerobic digestion of waste rich in sulfate. It disrupts the functional process and reduces the lifespan of biogas facilities. The micro-aerobic (MA) process is an alternative method for direct sulfurization.
Materials and Methods: The effect of sulfate loading (200, 500 and 700 mg/L) on H2S in biogas were investigated. Subsequently, the effect of MA process (0.88, 1.04, 1.34 NL/day) on H2S reduction in biogas production was evaluated. Additionally, oxidation-reduction potential (ORP) and pH were measured. Finally, under optimal conditions, the biogas volume and the content of CH4 and CO2 in biogas were determined.
Results: The results indicated that there were no significant differences in biogas volume production between the reactor fed with 200 mg/L sulfate and the control. However, the biogas production in reactors with 500 and 700 mg/L sulfate decreased to 4103 and 3929 mL, respectively. The H2S levels in control and reactors with 200, 500, 700 mg/L sulfate were 0.35, 0.46, 2.4, and 1.8%, respectively. In reactors with MA at rates of 0, 0.88, 1.04, 1.34 NL/day, the H2S levels were 1.95%, 0.9%, 0.4% and 0.1% (V/V) in biogas, respectively. The pH in reactor varied between 2.7 and 4.7, and the ORP was measured between -281 and -291 mV. Statistical analysis shows that no significant difference was observed between the average daily production of biogas with MA process of 0.88 and 1.04 NL/day. However, MA with 1.34 NL/day resulted in a decrease in biogas production.
Conclusion: The results indicated MA at a rate of 1.04 NL/day is a favorable option for the treatment of sulfate-rich urban wastewater sludge due to its efficiency in H2S removal.
 

Page 1 from 1     

© 2024 , Tehran University of Medical Sciences, CC BY-NC 4.0

Designed & Developed by: Yektaweb