Profit and Reliability Analysis of a Gas Production Unit with the Concept of Optimal Age Replacement Policy: A Copula Approach
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Surabhi Sengar
Abstract
This paper presents a comparative study of reliability analysis of a gas production plant, before and after applying optimal age replacement policy for a specific machine. The gas production plant has six machines connected in the series configuration a sulfur (S) shaft, coal pit, reaction furnace, sulfur separation machine, adiabatic reactor, and cooling machine. Here, the optimal age replacement policy has been applied to the cooling machine as this machine is working under a wear-out period, i.e., it has completed its prescribed age (say T) and is still operational. Two different situations have been discussed to analyze the performance variation of the system (i) when the model is working with high risk because the cooling machine is operational even after completion of its prescribed age (ii) when the old cooling machine is replaced by a new one after completing the age, T. The failure of any machine can cause the model failure. Additionally, a situation of employee walkout is also considered a responsible factor for model failure. Supplementary variable technique and copula methodology have been applied to solve the Markov model.
References
[1] T. Altiok, Performance Analysis of Manufacturing Systems, Springer, New York, 1997. 10.1007/978-1-4612-1924-8Search in Google Scholar
[2] N. Arora and D. Kumar, Operational behaviour of the pulping system in a paper plant, Proceeding of ICSOC, Wiley, New York (1994), 144–153. Search in Google Scholar
[3] P. Chandrasekhar and R. Natrajan, Reliability analysis of a complex one unit system, Opsearch 33 (1996), no. 3, 167–173. Search in Google Scholar
[4] B. S. Dhillon and R. B. Mishra, Reliability evaluation of system with critical human error, Microelectronics Reliab. 24 (1994), 743–756. 10.1016/0026-2714(84)90222-1Search in Google Scholar
[5] B. Gnedenko and A. U. Igor, Probabilistic Reliability Engineering, John Wiley & Sons, New York, 1995. 10.1002/9780470172421Search in Google Scholar
[6] S. T. Ioannis and M. Chalikias, On reliability structures with two common failure criteria under age-based maintenance policy, Reliab. Theory Appl. 2 (2021), no. 64, 27–34. Search in Google Scholar
[7] A. Kumar and P. Kumar, Reliability assessment for multi-state automatic ticket vending machine (ATVM) through software and hardware failures, J. Qual. Maintenance Eng. 28 (2022), no. 2, 448–473. 10.1108/JQME-08-2020-0089Search in Google Scholar
[8] A. Kumar and P. Kumar, Time dependent performance analysis of a smart trash bin using state-based Markov model and reliability approach, Cleaner Log. Supply Chain 9 (2023), 1–14. 10.1016/j.clscn.2023.100122Search in Google Scholar
[9] A. Kumar and M. Ram, Process modeling for decomposition unit of a UFP for reliability indices subject to fail-back mode and degradation, J. Qual. Maintenance Eng. 29 (2023), no. 3, 606–621. 10.1108/JQME-01-2022-0008Search in Google Scholar
[10] S. Sengar, Sensitivity analysis of a complex manufacturing system with the application of wait-in-line theory, Reliab. Theory Appl. 4 (2020), no. 59, 27–34. Search in Google Scholar
[11] S. Sengar, 7 Reliability analysis and cost optimization in manufacturing systems, System reliability Engineering, De Gruyter, Berlin (2021), 101–118. 10.1515/9783110617375-007Search in Google Scholar
[12] S. Sengar and M. Ram, Reliability and performance analysis of a complex manufacturing system with inspection facility using copula methodology, Reliab. Theory Appl. 4 (2022), no. 71, 494–508. Search in Google Scholar
[13] S. Sengar, M. Ram and Y. Kasancaglu, Stochastic analysis of complex redundant system having problem of waiting line in repair using copula methodology, Reliab. Theory Appl. 16 (2021), 383–391. Search in Google Scholar
[14] S. Sengar and S. B. Singh, Operational behavior and reliability measures of a viscose staple fiber plant including deliberate failures, Int. J. Reliab. Appl. 13 (2012), no. 1, 1–17. Search in Google Scholar
[15] A. A. Wasiu and O. Olalekan, Reliability analysis of a 3-machine power station using state space approach, Int. J. Eng. Res. Appl. 4 (2014), no. 7, 208–217. Search in Google Scholar
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Articles in the same Issue
- Frontmatter
- Profit and Reliability Analysis of a Gas Production Unit with the Concept of Optimal Age Replacement Policy: A Copula Approach
- A Comprehensive Analysis Using Maximum Likelihood Estimation and Artificial Neural Networks for Modeling Arthritic Pain Relief Data
- A Comparative Study of Six Process Capability Indices and Their Applications to Electronic and Food Industries
- Estimation of a New Asymmetry Based Process Capability Index 𝐶𝑐 for Gamma Distribution
- Double and Group Acceptance Sampling Inspection Plans Based on Truncated Life Test for the Quasi-Xgamma Distribution
- E-Bayesian Estimation of the Weighted Power Function Distribution with Application to Medical Data
- Comparing Ridge Regression Estimators: Exploring Both New and Old Methods
