Indian Journal of Science and Technology

Open Access Open Access  Restricted Access Subscription Access

Fuzzy Logic Based Monitoring System for Detecting the Concentration of Hydrogen Cyanide


Affiliations
1 Dept. of Physics, B.M. Engg. College, Indore-452012, India
2 School of Studies in Physics, Vikram University, Ujjain, India
 

This paper describes a fuzzy logic based monitoring system able to detect, measure the concentration of hydrogen cyanide released into the environment from the exhaust of vehicles, burning nitrogen-containing plastics or from any other source. We applied our suggested model to sensor network. The mobile sensor network is composed of a collection of distributed nodes each of which has limited sensing, intelligence and communication capabilities. An ad hoc wireless network is established among all nodes and each node considers other node as extended sensor.

Keywords

Hydrogen Cyanide Monitoring System, Fuzzy Logic Control System
User

  • Baum MM, John A. Moss, Stephen H. Pastel and Gregory A. Poskrebyshev (2007) Hydrogen cyanide exhaust emissions from in-use motor vehicles. Environ. Sci. Technol. 41 (3), 857–862.

  • Cui X, Hardin T, Ragade RK and Elmaghraby AS (2004) A swarm-based fuzzy logic control mobile sensor network for hazardous contaminants localization. In: IEEE Int. Conf. on Mobile Ad Hoc & Sensor Systems. Oct. 25-27, Univ. of Louisville, KY 40292. pp: 194–203.

  • Dashore Pankaj and Jain Suresh (2009) Fuzzy Rule-based system andmetagraph for risk management in electronic banking activities.IJET Singapore vol1No.2,101-107

  • El Ghawabi SH, Gaafar MA, El-Saharti AA, Ahmed SH, Malash KK and Fares R (1975) Chronic cyanide exposure: a clinical, radioisotope, and laboratory study. Br. J. Ind. Med. 32, 215-219.

  • Fiskel J, Cooper C, and Eschenroeder A (1981) Exposure and risk assessment for cyanide. EPA/440/4-85/008. NTIS PB85-220572

  • Fungal Genetics & Biol. 28 (2), 126-134.

  • amalu BP (1995) The adverse effects of long-term cassava (Manihot esculenta Crantz) consumption. Int. J. Food Sci. Nutr. 46(1), 65-93.

  • Lewis FL (2005) Wireless Sensor Network. The University of Texas, Arlington.

  • Mohammad AA and Abbas J (2006) "Optimized Forwarding for Wireless Sensor Networks by Fuzzy Inference System” The University of Sydney, NSW, Australia.

  • Nilesh Dashore and Gopal Upadhyay (2009) Fuzzy logic based monitoring system for detecting radon concentration. 2 (5), 29-30.

  • Ping Wang, Robert W. Sandrock and Hans D. VanEtten (1999) Disruption of the cyanide hydratase gene in Gloeocercospora sorghi increases its sensitivity to the phytoanticipin cyanide but does not affect its pathogenicity on the cyanogenic plant sorghum. Pradyot Patnaik (2002) Handbook of Inorganic Chemicals. McGraw-Hill, ISBN 0070494398.

  • SP Technical Research Institute of Sweden (2010) Formation of Hydrogen Cyanide in Fires. http://www.sp.se/en/index/research/hydrogen_formation/Sidor/default.aspx. Extracted on 20th April 2010.


Abstract Views: 361

PDF Views: 84




  • Fuzzy Logic Based Monitoring System for Detecting the Concentration of Hydrogen Cyanide

Abstract Views: 361  |  PDF Views: 84

Authors

Nilesh Dashore
Dept. of Physics, B.M. Engg. College, Indore-452012, India
Gopal Upadhyaya
School of Studies in Physics, Vikram University, Ujjain, India

Abstract


This paper describes a fuzzy logic based monitoring system able to detect, measure the concentration of hydrogen cyanide released into the environment from the exhaust of vehicles, burning nitrogen-containing plastics or from any other source. We applied our suggested model to sensor network. The mobile sensor network is composed of a collection of distributed nodes each of which has limited sensing, intelligence and communication capabilities. An ad hoc wireless network is established among all nodes and each node considers other node as extended sensor.

Keywords


Hydrogen Cyanide Monitoring System, Fuzzy Logic Control System

References





DOI: https://doi.org/10.17485/ijst%2F2010%2Fv3i3%2F29700