Document Type : Research Paper
Authors
1 Associate Professor, Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
2 Master of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran.
Abstract
In combined cycle power plants, instead of releasing gases produced from burning fossil fuels, after turning the gas turbines, they enter into heat recovery steam generator (HRSG) boilers to produce steam. The produced steam by these boilers is used to generate electricity in steam turbines and thus, electricity generation efficiency is dramatically increased. In this way, the efficiency of electricity production increases significantly. These boilers are made at a great cost and also, any failures of them cause a power plant to stop and create enormous costs, so optimizing their reliability is very important. This paper deals with the modeling of the HRSG feed water system by using a block diagram for two states (i.e., half-time and full load), to evaluate the difference between the proposed alternative designs, by considering their reliability. The method used in this paper can be applied to evaluate and optimize the reliability of many other industrial systems.
Introduction
In power generation, the reliability of industrial control systems is crucial, as failures can disrupt services, leading to accidents and damages. This study focuses on the reliability of Heat Recovery Steam Generator (HRSG) boilers in combined cycle power plants. These plants optimize electricity generation by redirecting gases from burning fossil fuels into heat recovery steam generators. HRSG boiler reliability is pivotal due to high construction costs and the potential for extensive downtime and expenses in case of malfunctions. Addressing this challenge, the research employs the underutilized Reliability Block Diagram (RBD) model, providing a graphical representation of system components and interactions. Specifically tailored to the needs of the Mapna Boiler Company, the study aims to assess and optimize the reliability of the steam production unit, i.e., the boiler, within combined cycle power plants.
Research Background
Reliability, in conjunction with factors such as availability and safety, stands as a cornerstone in ensuring the practical quality of any system. The application of Reliability Block Diagrams (RBD) is a well-established method for modeling and calculating the reliability of industrial systems. Numerous studies have applied RBDs across diverse domains, ranging from power substation automation and wind turbine reliability to error calculations in intelligent submarine power systems. However, despite the versatility of RBDs, a noticeable gap exists in the literature regarding their use for modeling boiler reliability, especially as a multi-state system.
Research Methodology
To undertake a comprehensive reliability analysis of HRSG boilers, the study focuses on distinct subsystems, including:
Feed-Water Storage System
Feed-Water System (FWS)
High-Pressure (HP) Section
Low-Pressure (LP) Section
Condensate System
Chemical Dosing System.
The Feed-Water System (FWS) is crucial for immediate boiler operation. The initial design involves a Four-Pump System (A2 design) for the FWS. A modification is proposed, removing one feed-water pump, prompting an examination of its impact on boiler reliability. Critical components are identified based on their role in potential disruptions, emphasizing parts causing immediate boiler shutdowns. Using expert knowledge and diagrams, a Reliability Block Diagram (RBD) is developed, visually highlighting weak points. The RBD assesses FWS reliability, comparing two configurations for optimization.
Calculation of HRSG Boiler Reliability as a Multistate System
Configurations of three-pump and four-pump setups for the Feed-Water System (FWS) are illustrated and analyzed using the Reliability Block Diagram (RBD). The reliability analysis entails a detailed process of data gathering, failure rate determination, and overall reliability calculation for diverse system configurations. The study incorporates probabilities for various operational states and introduces mathematical formulations to calculate the Mean Time Between Failures (MTBF) for water feed system configurations.
Fig1: Configuration of HRSG boiler water supply system in 3 pump mode
Fig2: Configuration of HRSG boiler water supply system in 4 pump mode
Steps of optimizing the operational reliability (OPR)
Step 1: Identifying Components Used in FWS
Step 2: Determining Failure Rates for Each Component
Step 3: Drawing a Reliability Block Diagram (RBD)
Step 4: Evaluating Component Reliability
Step 5: Calculating Overall Reliability for Each Configuration.
Results
Tables present Mean Time Between Failures (MTBF) for water feed system configurations, offering insights into the trade-offs between complete shutdowns and demi-capacity operations. The analysis suggests that the four-pump configuration, while experiencing fewer complete shutdowns, operates at half capacity more frequently compared to the three-pump configuration. The data-driven results highlight the nuances of system reliability and its dynamic nature.
Research Findings
The reliability assessment for boiler construction, considering the failure rates of components over a one-year period, indicates that the four-pump configuration is superior when component reliability is high; otherwise, the three-pump configuration may have an advantage. However, the decision to choose between these configurations necessitates an economic evaluation, accounting for construction costs, shutdown expenses, and half-capacity operation costs. The study underscores the importance of integrating economic considerations with reliability assessments for informed decision-making.
Discussion and Conclusion
This research offers valuable insights into vulnerable areas of the HRSG boiler water feeding system, guiding maintenance attention and informing decision-making processes. The study emphasizes the need for future research to consider repair times and incorporate fuzzy reliability values to enhance the robustness of reliability calculations. The holistic approach adopted in this study, combining technical assessments with economic considerations, lays the groundwork for a more comprehensive understanding of system reliability in industrial settings.
Suggestions for Future Research
As industries evolve, future research should tailor reliability models to specific contexts. Exploring different failure distribution functions beyond the constant-rate assumption opens avenues for investigation. Models like the Weibull mixture model, competitive risk models, compound models, and hybrid models offer promising directions. For instance, the study proposes exploring the application of a compound renewal model, known as complementary risk, for systems with parallel performance and independent components. The limited exploration of this model in the literature presents an opportunity for future research to uncover its potential applications and contributions to reliability modeling.
Keywords
Main Subjects
- Alaswad, S., & Xiang, Y. (2017). A review on condition-based maintenance optimization models for stochastically deteriorating system. Reliability Engineering & System Safety, 157, 54-63. https://doi.org/10.1016/j.ress.2016.08.009
- Chojaczyk, A. A., Teixeira, A. P., Neves, L. C., Cardoso, J. B., & Soares, C. G. (2015). Review and application of artificial neural networks models in reliability analysis of steel structures. Structural Safety, 52, 78-89. https://doi.org/10.1016/j.strusafe.2014.09.002
- Ding, Y., Lin, Y., Peng, R., & Zuo, M. J. (2019). Approximate Reliability Evaluation of Large-Scale Multistate Series-Parallel Systems. IEEE Transactions on Reliability, 68(2), 539-553. https://doi.org/10.1109/TR.2019.2898459
- Gilberto, F., & Hoang, P. (2012). Thermal Power Plant Performance Analysis. Springer Series in Reliability Engineering, ISBN 978-1-4471-2308-8. https://doi.org/10.1007/978-1-4471-2309-5
- Kaushik, B., & Banka, H. (2015). Performance evaluation of approximated artificial neural network (AANN) algorithm for reliability improvement. Applied Soft Computing, 26, 303-314. https://doi.org/10.1016/j.asoc.2014.10.002
- Lehký, D., Slowik, O., & Novák, D. (2017). Reliability-based design: Artificial neural networks and double-loop reliability-based optimization approaches. Advances in Engineering Software, 117, 123-135. https://doi.org/10.1016/j.advengsoft.2017.06.013
- Levitin, G., & Xing, L. (2010). Reliability and performance of multi-state systems with propagated failures having selective effect. Reliability Engineering & System Safety, 95(6), 655-661. https://doi.org/10.1016/j.ress.2010.02.003
- Liu, J., & Zio, E. (2017). System dynamic reliability assessment and failure prognostics. Reliability Engineering & System Safety, 160, 21-36. https://doi.org/10.1016/j.ress.2016.12.003
- Patelli, E., Feng, G., Coolen, F. P., & Coolen-Maturi, T. (2017). Simulation methods for system reliability using the survival signature. Reliability Engineering & System Safety. https://doi.org/10.1016/j.ress.2017.06.018.
- Saro, L., Zanettin, C., & Božič, V. (2018). Reliability Analysis and Calculations for Different Power System Architectures based on Modular UPS. 2018 IEEE International Telecommunications Energy Conference (INTELEC), 1-8. https://doi.org/10.1109/INTLEC.2018.8612341
- Sayed, A., El-Shimy, M., El-Metwally, M., & Elshahed, M. (2019). Reliability, Availability and Maintainability Analysis for Grid-Connected Solar Photovoltaic Systems. Energies, 12(7), 12-13. https://doi.org/10.3390/en12071213
- Schneider, R., Thöns, S., & Straub, D. (2017). Reliability analysis and updating of deteriorating systems with subset simulation. Structural Safety, 64, 20-36. https://doi.org/10.1016/j.strusafe.2016.09.002
- Wang, W., Di Maio, F., & Zio, E. (2017). Three-Loop Monte Carlo Simulation Approach to Multi-State Physics Modeling for System Reliability Assessment. Reliability Engineering & System Safety, 167, 276-289. https://doi.org/10.1016/j.ress.2017.06.003
- Salari, S., Karbasian, M., Nilipour, A., & Khayambashi, B. (1392). Analysis of the reliability of an offset system in the intelligent submarine power generation system using error tree method. First National Symposium on Intelligent Marine Systems. University of Science and Technology Submarine Complex. Shahin Shahr. https://civilica.com/doc/236797 [In Persian]
- Ghanizadeh, M., & Poladi, J. (1395). Survey of wind turbine reliability function connected to the network-based block diagram method. Second International Conference on New Research Findings in Science, Engineering and Technology, Ministry of Science, Research and Technology, Isfahan University of Technology, Faculty of Electrical and Computer Sciences. https://civilica.com/doc/550435 [In Persian]
- Mahdavi, Sh., & Miri Lavasani, M. R. (2013). Evaluation of Reliability of CNG Fuel Location by Block Diagram Method. Health of Iran, 3. 22-29. https://ioh.iums.ac.ir/article-1-1049-fa.html [In Persian]
- Hamdani Golshan, M. I., Haqi Pham, M. R., Shayanfar, H. A., & Hajian Hosseinabadi, H. (2012). Modeling and evaluating the reliability of post automation systems. Ministry of Science, Research and Technology, Isfahan University of Technology, Faculty of Electrical and Electronics Engineering.https://library.iut.ac.ir/dL /search/ default.aspx?Term=8115&Field=0&DTC=107 [In Persian]
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