RAM analysis and performance optimization of paper manufacturing plant using nature-inspired algorithms

Abstract

Abstract The present study is designed to develop an efficient stochastic model to optimize the performance of paper manufacturing plants (PMP) using nature-inspired algorithms. The paper manufacturing plant is a very complex entity configured using several subsystems. All the subsystems configured in the series structure and the failure of anyone causes the complete system failure. To achieve the objective of the proposed study, initially RAMD investigation of each subsystem and a complete power plant is performed and later a stochastic model is developed for performance prediction of the paper plant using nature-inspired algorithms. The Markov birth–death process is used to develop the Chapman–Kolmogorov differential-difference equations. Simple probabilistic arguments are used to simplify the proposed stochastic model. All the failure and repair rates are considered exponentially distributed random variables. The random variables are statistically independent. The repairs are perfect and sufficient repair facilities are available in the plant. Various system effectiveness measures are derived for a particular case at various population sizes at several iterations. The numerical results highlight the importance of the proposed model.

Keywords

• Paper manufacturing plant
• Performance analysis
• Genetic algorithm
• Particle swarm optimization
• Markovian approach