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Shaho Karami, Gholamreza Nabibidhendi, Hamidreza Jafari, Hassan Hoveidi, Amir Hedayati,
Volume 7, Issue 2 (10-2014)
Abstract

Background & Objectives: Human environment is surrounded bychemicals that could directly or indirectly endanger human health. Some statistics of WHO is indicative of the fact that four million people are employed in the chemical industry throughout the world and one million people die or become disabled annually due to contact with chemicals. Moreover, 1-4 Millions chemical toxicity occur annually. The purpose of this study was to understand the risks involved in chemicals in the workplace, to assess the task risk, and to propose appropriate control measures in order to eliminate or reduce risk in the petrochemical industry. Materials & Methods: In this study, the chemicals were identified in Arak Petrochemical and features that are indicative of hazardous materials were identified and using TOPSIS, The hazard rate were determined. Then the job duties of employees and employee exposure rate with chemicals were calculated and finally, a risk rate for exposure to chemicals in job duties was determined. Results: It was found that chemicals do not have too high risk to employees however, but the high risky chemicals were five chemicals including naphtha, ammonia, acetic acid, chlorine, and methanol for operational staff and two chemicals, i.e. ammonia and chlorine for operation and maintenance staffs . Conclusion: It is better to have an alternative for the materials that their risk rang is high and very high, and their production is suggested to be avoided.


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.
 


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