EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 430 (2023) E3S Web Conf., 430 (2023) 01228 References
Open Access
Issue
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
Article Number 01228
Number of page(s) 11
DOI https://doi.org/10.1051/e3sconf/202343001228
Published online 06 October 2023
  1. M. S. Fard, & A. Shokri, Principles, operational challenges, and perspectives in boiler feedwater treatment process. Environmental Advances, 100389 (2023). [Google Scholar]
  2. M. A. R. C. E. L. O. NORIEGA DEL CASTILLO, Review and classification of industrial boilers maintenance and a reliability-centered maintenance methodology proposal for production plants (2019). [Google Scholar]
  3. R., Kapoor, P., Ghosh, B., Tyagi, V. K., Vijay, V., Vijay, I. S., Thakur & A., Kumar, Advances in biogas valorization and utilization systems: A comprehensive review. Journal of Cleaner Production, 273, 123052 (2020). [CrossRef] [Google Scholar]
  4. S., Djayanti, Energy Efficiency Improvement Strategies for Boilers: A Case Study in Pharmacy Industry. In E3S Web of Conferences (Vol. 125, p. 12002). EDP Sciences (2019). [CrossRef] [EDP Sciences] [Google Scholar]
  5. H., Pan & X., Xu, Research on Factors Affecting Boiler Feedwater Quality and Its Improvement. Open Journal of Applied Sciences, 12(6), 901-911 (2022). [Google Scholar]
  6. N., Gopal & D., Panchal, Fuzzy decision support system for sustainable operational performance optimization for boiler unit in milk process industry. Applied Soft Computing, 109983 (2023). [Google Scholar]
  7. J., Lee, I. Cameron & M., Hassall, Improving process safety: What roles for Digitalization and Industry 4.0?. Process safety and environmental protection, 132, 325-339 (2019). [CrossRef] [Google Scholar]
  8. Md Nor, N., Che, C. R., Hassan, & M. A. Hussain, A review of data-driven fault detection and diagnosis methods: Applications in chemical process systems. Reviews in Chemical Engineering, 36(4), 513-553 (2020). [CrossRef] [Google Scholar]
  9. C., Benson, C. D., Argyropoulos, C., Dimopoulos, C. V., Mikellidou, & G., Boustras, Safety and risk analysis in digitalized process operations warning of possible deviating conditions in the process environment. Process Safety and Environmental Protection, 149, 750-757 (2021). [CrossRef] [Google Scholar]
  10. H., Jagtap, A., Bewoor, Kumar, R., Ahmadi, M. H., & G., Lorenzini, Markov-based performance evaluation and availability optimization of the boiler–furnace system in coal-fired thermal power plant using PSO. Energy Reports, 6, 1124-1134 (2020). [CrossRef] [Google Scholar]
  11. R., Panday, N., Indrawan, L. J., Shadle & R. W., Vesel, Leak detection in a subcritical boiler. Applied Thermal Engineering, 185, 116371 (2021). [CrossRef] [Google Scholar]
  12. Y., Shi, T., Han, F., Cui, J., Wen, J., Jia, & X., Pang, A hybrid prediction approach for enhancing heat transfer efficiency of coal-fired power plant boiler. Energy Reports, 9, 658-668 (2023). [CrossRef] [Google Scholar]
  13. R., Clark, N., Zucker, & J., Urpelainen, The future of coal-fired power generation in Southeast Asia. Renewable and Sustainable Energy Reviews, 121, 109650 (2020). [CrossRef] [Google Scholar]
  14. M., Yang, Y., Zhou, J., Yang, J., Bao, D., Wang, & Q., Yu. Performance analysis of an efficient waste heat utilization system in an ultra-supercritical coal-fired power plant. Energy Reports, 8, 5871-5882 (2022). [CrossRef] [Google Scholar]
  15. X., Li. Design of energy-conservation and emission-reduction plans of China’s industry: Evidence from three typical industries. Energy, 209, 118358 (2020). [CrossRef] [Google Scholar]
  16. M., Ghosh. Effect of Flue Gas Constituents on Boiler Tube Failure of A Captive Power Plant. Engineering Failure Analysis, 107416 (2023). [Google Scholar]
  17. S., Huang, W., Zuo, D., Vrabie, & R., Xu. Modelica-based system modeling for studying control-related faults in chiller plants and boiler plants serving large office buildings. Journal of Building Engineering, 44, 102654 (2021). [CrossRef] [Google Scholar]
  18. Q., Zhang, Z., Tian, Y., Lu, J., Niu, & C., Ye. Experimental study on performance assessments of HVAC cross-domain fault diagnosis methods oriented to incomplete data problems. Building and Environment, 236, 110264 (2023). [CrossRef] [Google Scholar]
  19. X., Fang, G., Gong, G., Li, L., Chun, P., Peng, W., Li, & X., Shi. Cross temporal-spatial transferability investigation of deep reinforcement learning control strategy in the building HVAC system level. Energy, 263, 125679 (2023). [CrossRef] [Google Scholar]
  20. Q., Zhang, Z., Tian, J., Niu, J., Zhu, & Y., Lu. A study on transfer learning in enhancing performance of building energy system fault diagnosis with extremely limited labeled data. Building and Environment, 225, 109641 (2022). [CrossRef] [Google Scholar]
  21. F., Zhong, J. K., Calautit, & Y., Wu. Assessment of HVAC system operational fault impacts and multiple faults interactions under climate change. Energy, 258, 124762 (2022). [CrossRef] [Google Scholar]
  22. Y., Vladova, & Y. R., Vladov. Statistical analysis of the steam boiler elements’ maintenance results. IFAC-PapersOnLine, 55(9), 549-552 (2022). [Google Scholar]
  23. M., Behzad, H., Kim, M., Behzad, & H. A., Behambari. Improving sustainability performance of heating facilities in a central boiler room by condition-based maintenance. Journal of Cleaner Production, 206, 713-723 (2019). [CrossRef] [Google Scholar]
  24. M. E., Cholette, H., Yu, P., Borghesani, L., Ma, & G., Kent. Degradation modeling and condition-based maintenance of boiler heat exchangers using gamma processes. Reliability Engineering & System Safety, 183, 184-196 (2019). [CrossRef] [Google Scholar]
  25. A., Arjunwadkar, P., Basu, & B., Acharya. A review of some operation and maintenance issues of CFBC boilers. Applied Thermal Engineering, 102, 672-694 (2016). [CrossRef] [Google Scholar]
  26. R., Saidur, E. A., Abdelaziz, A., Demirbas, M. S., Hossain, & S., Mekhilef. A review on biomass as a fuel for boilers. Renewable and sustainable energy reviews, 15(5), 2262-2289 (2011). [CrossRef] [Google Scholar]
  27. J., Purbolaksono, A., Khinani, A. A., Ali, A. Z., Rashid, & N. F., Nordin. Iterative technique and finite element simulation for supplemental condition monitoring of water-tube boiler. Simulation Modelling Practice and Theory, 17(5), 897-910 (2009). [CrossRef] [Google Scholar]
  28. E., Ikonen, M., Liukkonen, A. H., Hansen, M., Edelborg, O., Kjos, I., Selek, & A., Kettunen. Fouling monitoring in a circulating fluidized bed boiler using direct and indirect model-based analytics. Fuel, 346, 128341(2023). [CrossRef] [Google Scholar]
  29. M. A., El-Bindary, A., Shahat, I. M., El-Deen, M. A., Khalil, & N., Hassan. Spectrophotometric and fluorometric methods for effective monitoring of rust state in boiler systems and Fe2+ ion in pharmaceutical samples. Journal of Molecular Liquids, 382, 121946 (2023). [CrossRef] [Google Scholar]
  30. T., Leffler, M., Eriksson, B., Leckner, F., Lind, F., Winquist, & P., Knutsson. Monitoring of bed material in a biomass fluidized bed boiler using an electronic tongue. Fuel, 340, 127598 (2023). [CrossRef] [Google Scholar]
  31. M. A., Rahman, C. L., Karmaker, T., Ahmed, M. I., Khan, A. M., Morshed, & S. M., Ali. Modelling the causes of boiler accidents: implications for economic and social sustainability at the workplace. Heliyon, 8(6) (2022). [Google Scholar]
  32. M., Trojan. Modeling of a steam boiler operation using the boiler nonlinear mathematical model. Energy, 175, 1194-1208 (2019). [CrossRef] [Google Scholar]
  33. T., Zhang, J., Chen, F., Li, K., Zhang, H., Lv, S., He, & E., Xu. Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions. ISA transactions, 119, 152-171 (2022). [CrossRef] [PubMed] [Google Scholar]
  34. Y., Feng, J., Chen, T., Zhang, S., He, E., Xu, & Z., Zhou. Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis. ISA transactions, 120, 383-401 (2022). [CrossRef] [PubMed] [Google Scholar]
  35. Z., Zhao, T., Li, J., Wu, C., Sun, S., Wang, R., Yan, & X., Chen. Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study. ISA transactions, 107, 224-255 (2020). [CrossRef] [PubMed] [Google Scholar]
  36. W., Zima, S., Grądziel, A., Cebula, M., Rerak, E., Kozak-Jagieła, & M., Pilarczyk. Mathematical model of a power boiler operation under rapid thermal load changes. Energy, 263, 125836 (2023). [CrossRef] [Google Scholar]
  37. D., Li, S., Xia, J., Geng, F., Meng, Y., Chen, & G., Zhu. Discriminability Analysis of Characterization Parameters in Micro-Leakage of Turbocharged Boiler’s Evaporation Tube. Energies, 15(22), 8636 (2022). [Google Scholar]
  38. V., Başhan, & H., Demirel. Application of fuzzy dematel technique to assess most common critical operational faults of marine boilers. Politeknik Dergisi, 22(3), 545-555 (2019). [Google Scholar]
  39. N., Indrawan, L. J., Shadle, R. W., Breault, R., Panday, & U. K., Chitnis. Data analytics for leak detection in a subcritical boiler. Energy, 220, 119667 (2021). [CrossRef] [Google Scholar]
  40. A. O., Ibrahim, O. O., Ighodaro, S. K., Fasogbon, E. F., Orumwense, & M. A., Waheed. Failure investigation of the tube of a dual fired steam boiler in a western nigerian food and beverage manufacturing plant. Engineering Failure Analysis, 143, 106906 (2023). [CrossRef] [Google Scholar]
  41. S., Rebello, H., Truong-Ba, & M. E., Cholette. Degradation modelling and lifetime assessment for boiler waterwall with incomplete inspection data. Process Safety and Environmental Protection, 173, 304-316 (2023). [CrossRef] [Google Scholar]
  42. J., Davies, H., Truong-Ba, M. E., Cholette, & G., Will. Optimal inspections and maintenance planning for anti-corrosion coating failure on ships using non-homogeneous Poisson Processes. Ocean Engineering, 238, 109695 (2021). [CrossRef] [Google Scholar]
  43. B., Jin, W., Yao, K., Liu, S., Lu, X., Luo, & Z., Liang. Self-optimizing control and safety assessment to achieve economic and safe operation for oxy-fuel combustion boiler island systems. Applied Energy, 323, 119397 (2022). [CrossRef] [Google Scholar]
  44. K., Macek, P., Endel, N., Cauchi, & A., Abate. Long-term predictive maintenance: A study of optimal cleaning of biomass boilers. Energy and Buildings, 150, 111-117 (2017). [CrossRef] [Google Scholar]
  45. A. S., Yeardley, J. O., Ejeh, L., Allen, S. F., Brown, & J., Cordiner. Integrating machine learning techniques into optimal maintenance scheduling. Computers & Chemical Engineering, 166, 107958 (2022). [CrossRef] [Google Scholar]
  46. K., Velmurugan, S., Saravanasankar, & S., Bathrinath. Smart maintenance management approach: Critical review of present practices and future trends in SMEs 4.0. Materials Today: Proceedings, 62, 2988-2995 (2022). [CrossRef] [Google Scholar]
  47. Y., Wu, C. T., Maravelias, M. J., Wenzel, M. N., ElBsat, & R. T., Turney. Predictive maintenance scheduling optimization of building heating, ventilation, and air conditioning systems. Energy and Buildings, 231, 110487 (2021). [CrossRef] [Google Scholar]
  48. B., Einabadi, A., Baboli, & M., Ebrahimi. Dynamic Predictive Maintenance in industry 4.0 based on real time information: Case study in automotive industries. IFAC-PapersOnLine, 52(13), 1069-1074 (2019). [CrossRef] [Google Scholar]
  49. N., Cauchi, K., Macek, & A., Abate. Model-based predictive maintenance in building automation systems with user discomfort. Energy, 138, 306-315 (2017). [CrossRef] [Google Scholar]
  50. M., Yulianto, E., Hartulistiyoso, L. O., Nelwan, S. E., Agustina, & C., Gupta. Thermal Characteristics of Coconut Shells as Boiler Fuel. International Journal of Renewable Energy Development, 12(2) (2023). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.