Long-term predictive maintenance: A study of optimal cleaning of biomass boilers
Combustion in a biomass-fired boiler causes build-up of soot, which reduces the heat transfer and decreases the efficiency of operation. In order to mitigate this natural occurrence, cleaning via soot blowing is an important maintenance action. The objective of this study is to develop long-term opt...
| Main Authors: | , , , |
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| Format: | Journal article |
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Elsevier
2017
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| _version_ | 1797064999418462208 |
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| author | Macek, K Endel, P Cauchi, N Abate, A |
| author_facet | Macek, K Endel, P Cauchi, N Abate, A |
| author_sort | Macek, K |
| collection | OXFORD |
| description | Combustion in a biomass-fired boiler causes build-up of soot, which reduces the heat transfer and decreases the efficiency of operation. In order to mitigate this natural occurrence, cleaning via soot blowing is an important maintenance action. The objective of this study is to develop long-term optimal maintenance strategies, which are model-based and specifically employ the dynamics of boiler efficiency and of anticipated heating demand, both of which are identified from empirical data. An approximate dynamic programming algorithm is set up, resulting in the optimal maintenance actions over time, so that the total operational costs of the biomass boiler plus the cleaning costs are minimised. A practical case study with real data is used to elucidate the benefits of the new approach. |
| first_indexed | 2024-03-06T21:22:22Z |
| format | Journal article |
| id | oxford-uuid:41e51766-122f-43e7-b82b-7d4c38d9a7dd |
| institution | University of Oxford |
| last_indexed | 2024-03-06T21:22:22Z |
| publishDate | 2017 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:41e51766-122f-43e7-b82b-7d4c38d9a7dd2022-03-26T14:46:18ZLong-term predictive maintenance: A study of optimal cleaning of biomass boilersJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:41e51766-122f-43e7-b82b-7d4c38d9a7ddSymplectic Elements at OxfordElsevier2017Macek, KEndel, PCauchi, NAbate, ACombustion in a biomass-fired boiler causes build-up of soot, which reduces the heat transfer and decreases the efficiency of operation. In order to mitigate this natural occurrence, cleaning via soot blowing is an important maintenance action. The objective of this study is to develop long-term optimal maintenance strategies, which are model-based and specifically employ the dynamics of boiler efficiency and of anticipated heating demand, both of which are identified from empirical data. An approximate dynamic programming algorithm is set up, resulting in the optimal maintenance actions over time, so that the total operational costs of the biomass boiler plus the cleaning costs are minimised. A practical case study with real data is used to elucidate the benefits of the new approach. |
| spellingShingle | Macek, K Endel, P Cauchi, N Abate, A Long-term predictive maintenance: A study of optimal cleaning of biomass boilers |
| title | Long-term predictive maintenance: A study of optimal cleaning of biomass boilers |
| title_full | Long-term predictive maintenance: A study of optimal cleaning of biomass boilers |
| title_fullStr | Long-term predictive maintenance: A study of optimal cleaning of biomass boilers |
| title_full_unstemmed | Long-term predictive maintenance: A study of optimal cleaning of biomass boilers |
| title_short | Long-term predictive maintenance: A study of optimal cleaning of biomass boilers |
| title_sort | long term predictive maintenance a study of optimal cleaning of biomass boilers |
| work_keys_str_mv | AT macekk longtermpredictivemaintenanceastudyofoptimalcleaningofbiomassboilers AT endelp longtermpredictivemaintenanceastudyofoptimalcleaningofbiomassboilers AT cauchin longtermpredictivemaintenanceastudyofoptimalcleaningofbiomassboilers AT abatea longtermpredictivemaintenanceastudyofoptimalcleaningofbiomassboilers |