Mathematical Modeling and Performance Optimization of Stock Preparation Unit in Paper Manufacturing Plants using GA and PSO

The prominent objective of present study is to develop an efficient mathematical model for performance optimization of stock preparation unit of paper plants using the concept of redundancy. Stock preparation in paper manufacturing involves converting raw stock into finished stock for the paper machine. This process involves several subsystems like storage tanks, repulping/Slushing, deflaking, storage and mixing chests, and the paper machine itself in various redundancy strategies. For the system performance analysis, a mathematical model is developed using Markov birth death process along with reliability, availability, maintainability and dependability (RAMD) investigation of components. The Chapman-Kolmogorov differential-difference equations derived under the exponential behavior of failure and repair rates. The prediction of prominent system effectiveness measure is made using genetic algorithm and particle swarm optimization at various population sizes. Decision matrices are derived for a particular value of parameters. It is observed that predicted optimal availability of stock preparation unit is 0.9207 at a population size of 2500 after 80 iterations. It is revealed that genetic algorithm outperformed over particle swarm optimization in availability prediction of stock preparation unit. The derived results are helpful for system designers and maintenance personnel for effective decision-making for plant operations.