In the present work, a recently developed advanced optimization algorithm named as teaching–learning-based optimization (TLBO) is used for the parameters optimization of fabric finishing system of a textile industry. Fabric Finishing System has four main subsystems, arranged in hybrid configuration. For performance modeling and analysis of availability, a performance evaluating model of fabric finishing system has been developed with the help of mathematical formulation based on Markov-Birth-Death process using Probabilistic Approach. Then, the overall performance of the concerned system has first analyzed and then, optimized by using teaching–learning-based optimization (TLBO). The results of optimization using the proposed algorithm are validated by comparing with those obtained by using the genetic algorithm (GA) on the same system. Improvement in the results is obtained by the proposed algorithm. The results of effect of variation of the algorithm parameters on fitness values of the objective function are reported.

In recent years, the availability of power plants has become increasingly important issue in most developed and developing countries. This paper aims to propose a methodology based on Markov approach to evaluate the availability simulation model for power generation system (Turbine) in a thermal power plant under realistic working environment. The effects of occurrence of failure/course of actions and availability of repair facilities on system performance have been investigated. Higher availability of the components/equipments is inherently associated with their higher reliability and maintainability. The power generation system consists of five subsystems with four possible states: full working, reduced capacity, reduced efficiency and failed state. So, its availability should be carefully evaluated in order to foresee the performance of the power plant. The availability simulation model (Av.) has been developed with the help of mathematical formulation based on Markov Birth-Death process using probabilistic approach. For this purpose, first differential equations have been generated. These equations are then solved using normalizing condition so as to determine the steady state availability of power generation system. In fact, availability analysis is very much effective in finding critical subsystems and deciding their preventive maintenance program for improving availability of the power plant as well as the power supply. From the graphs illustrated, the optimum values of failure/repair rates for maximum availability, of each subsystem is analyzed and then maintenance priorities are decided for all subsystems.The present paper highlights that in this system, Turbine governing subsystem is most sensitive demands more improvement in maintainability as compared to the other subsystems. While Turbine lubrication subsystem is least sensitive.

The long term operation and planning of power plant depend upon an effective availability analysis and assessment of various systems in the plant concerned. The plant is expected to remain operational in a continual manner to achieve the desired production targets. Hence, the availability analysis of the boiler air circulation system plays an important role in this direction. For this purpose, the concerned system mathematical model based on Markov Birth-Death process has been developed. The system consists of four subsystems. The transition diagram represents reduced capacity, full working and failed state of the system. The differential equations associated with the transition diagram based on probabilistic approach have been solved recursively in order to develop the system steady state availability. Availability matrices represented measures the performance of the system concerned. In addition, different combinations of failures and repair rates provide various availability levels of the system. Maintenance decisions are taken based upon these values for improving availability of the power plant as well as the power supply. The result shows that the failure of the primary air fan affects the system availability at most, while failure of air heater affect it at least for different failures and repair rate combination of subsystems under study.