Predicting uncertain behavior of the press unit in a paper mill using PSOBLT technique
The purpose of this paper is to present a hybridized technique for analyzing the behavior of an industrial system stochastically by utilizing vague, imprecise, and uncertain data. In the present study two important tools namely Lambda-Tau methodology and particle swarm optimization are used to formulate the hybridized technique PSOBLT (Particle swarm optimization based Lambda-Tau) for analyzing the behavior of the complex industrial system stochastically up to a desired degree of accuracy. Expressions of reliability indices like failure rate, repair time, mean time between failures (MTBF), expected number of failures (ENOF), reliability and availability for the system are obtained by using Lambda-Tau methodology and particle swarm optimization is used to construct the membership function. Fault tree is used to model the system. The press unit of a paper mill situated in a northern part of India, producing approximately 200 tons of paper per day, has been considered to demonstrate the proposed approach. Sensitivity analysis of a system's behavior has also been done. The behavior analysis results computed by PSOBLT technique have a reduced region of prediction in comparison of existing Lambda-Tau and GABLT (Genetic algorithm based Lambda-Tau) technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties. Thus the paper suggests an approach to improve the systems' performance.
paper mill psoblt uncertain data lambda-tau methodology particle swarm optimization