PERFORMANCE ANALYSIS OF INDUSTRIAL SYSTEMS USING PETRI NETS

Abstract

In the present scenario of automation in manufacturing, the complexity of equipment has increased so rapidly that the reliability of the equipment has come in focus and has emerged as a thrust area especially for production and process industries. Reliability engineering methods help organizations to increase efficiency by developing the availability models of their processing units. These models are used to identify critical areas for reliability improvement. The objective of the present work is to use the powerful modeling tool, namely Petri Net for performance analysis of mechanical systems with an aim to improve system availability. Ramagundam Super Thermal Power Plant, Karimnagar, Andhra pradesh has been studied and Steam generating System of Power plant has been selected for Performance analysis which has mainly three systems namely High Pressure Heaters, Economiser and Boiler Drum. The Petri Net model of Steam Generating system is developed through which Reachability Graph is developed from the different markings of Petri net model. By using the data available from the power Plant, Petri net model is simulated for various failure and repair times of sub-systems of Stem Generating system in Petri net tool box 2.1 installed in MATLAB version 6.1 to calculate the system availability. Also mathematical models are developed from the reachability graph and are further solved to get system availability under various failure and repair rates of sub-systems of Steam Generating System. It observed from those values that there is some error varying from 0.01 to 0.03 or 1 to 3% in System availability between simulated results and solution of mathematical models. From the availability calculations, it is observed that out of the three components of Steam Generating System, Economiser is found to be more vital component, which can enhance system availability by 7.8% where as High Pressure heaters and Boiler Drum can alter the system Availability by 1.7% and 4.7%.