RAM analysis and performance optimization of paper manufacturing plant using nature-inspired algorithms
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...
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| Format: | Article |
| Language: | English |
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Springer
2025-03-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06649-3 |
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| author | Ashish Kumar Sumaira Rasool Monika Saini |
| author_facet | Ashish Kumar Sumaira Rasool Monika Saini |
| author_sort | Ashish Kumar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-06b144a5f18c4e668133dd4fc6b1ad39 |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-06b144a5f18c4e668133dd4fc6b1ad392025-08-20T03:41:40ZengSpringerDiscover Applied Sciences3004-92612025-03-017411810.1007/s42452-025-06649-3RAM analysis and performance optimization of paper manufacturing plant using nature-inspired algorithmsAshish Kumar0Sumaira Rasool1Monika Saini2Department of Mathematics & Statistics, Manipal University JaipurDepartment of Mathematics & Statistics, Manipal University JaipurDepartment of Mathematics & Statistics, Manipal University JaipurAbstract 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.https://doi.org/10.1007/s42452-025-06649-3Paper manufacturing plantPerformance analysisGenetic algorithmParticle swarm optimizationMarkovian approach |
| spellingShingle | Ashish Kumar Sumaira Rasool Monika Saini RAM analysis and performance optimization of paper manufacturing plant using nature-inspired algorithms Discover Applied Sciences Paper manufacturing plant Performance analysis Genetic algorithm Particle swarm optimization Markovian approach |
| title | RAM analysis and performance optimization of paper manufacturing plant using nature-inspired algorithms |
| title_full | RAM analysis and performance optimization of paper manufacturing plant using nature-inspired algorithms |
| title_fullStr | RAM analysis and performance optimization of paper manufacturing plant using nature-inspired algorithms |
| title_full_unstemmed | RAM analysis and performance optimization of paper manufacturing plant using nature-inspired algorithms |
| title_short | RAM analysis and performance optimization of paper manufacturing plant using nature-inspired algorithms |
| title_sort | ram analysis and performance optimization of paper manufacturing plant using nature inspired algorithms |
| topic | Paper manufacturing plant Performance analysis Genetic algorithm Particle swarm optimization Markovian approach |
| url | https://doi.org/10.1007/s42452-025-06649-3 |
| work_keys_str_mv | AT ashishkumar ramanalysisandperformanceoptimizationofpapermanufacturingplantusingnatureinspiredalgorithms AT sumairarasool ramanalysisandperformanceoptimizationofpapermanufacturingplantusingnatureinspiredalgorithms AT monikasaini ramanalysisandperformanceoptimizationofpapermanufacturingplantusingnatureinspiredalgorithms |