Chapter A Framework for Learning System for Complex Industrial Processes
Due to the intense price-based global competition, rising operating cost, rapidly changing economic conditions and stringent environmental regulations, modern process and energy industries are confronting unprecedented challenges to maintain profitability. Therefore, improving the product quality an...
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| Principais autores: | , , , , , |
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| Formato: | Online |
| Idioma: | inglês |
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InTechOpen
2021
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| Acesso em linha: | ONIX_20210602_10.5772/intechopen.92899_497 |
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| _version_ | 1869528433802870784 |
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| author | Rahman, Moksadur Fentaye, Amare Desalegn Zaccaria, Valentina Aslanidou, Ioanna Dahlquist, Erik Kyprianidis, Konstantinos |
| author_browse | Aslanidou, Ioanna Dahlquist, Erik Fentaye, Amare Desalegn Kyprianidis, Konstantinos Rahman, Moksadur Zaccaria, Valentina |
| author_facet | Rahman, Moksadur Fentaye, Amare Desalegn Zaccaria, Valentina Aslanidou, Ioanna Dahlquist, Erik Kyprianidis, Konstantinos |
| author_sort | Rahman, Moksadur |
| collection | Directory of Open Access Books |
| description | Due to the intense price-based global competition, rising operating cost, rapidly changing economic conditions and stringent environmental regulations, modern process and energy industries are confronting unprecedented challenges to maintain profitability. Therefore, improving the product quality and process efficiency while reducing the production cost and plant downtime are matters of utmost importance. These objectives are somewhat counteracting, and to satisfy them, optimal operation and control of the plant components are essential. Use of optimization not only improves the control and monitoring of assets, but also offers better coordination among different assets. Thus, it can lead to extensive savings in the energy and resource consumption, and consequently offer reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks. In this chapter, a generic learning system architecture is presented that can be retrofitted to existing automation platforms of different industrial plants. The architecture offers flexibility and modularity, so that relevant functionalities can be selected for a specific plant on an as-needed basis. Various functionalities such as soft-sensors, outputs prediction, model adaptation, control optimization, anomaly detection, diagnostics and decision supports are discussed in detail. |
| format | Online |
| id | doab-20.500.12854ir-70354 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | InTechOpen |
| publisherStr | InTechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-703542025-08-13T14:11:55Z Chapter A Framework for Learning System for Complex Industrial Processes Rahman, Moksadur Fentaye, Amare Desalegn Zaccaria, Valentina Aslanidou, Ioanna Dahlquist, Erik Kyprianidis, Konstantinos learning system, soft-sensors, model predictive control, fault detection, isolation and identification, information fusion thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general Due to the intense price-based global competition, rising operating cost, rapidly changing economic conditions and stringent environmental regulations, modern process and energy industries are confronting unprecedented challenges to maintain profitability. Therefore, improving the product quality and process efficiency while reducing the production cost and plant downtime are matters of utmost importance. These objectives are somewhat counteracting, and to satisfy them, optimal operation and control of the plant components are essential. Use of optimization not only improves the control and monitoring of assets, but also offers better coordination among different assets. Thus, it can lead to extensive savings in the energy and resource consumption, and consequently offer reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks. In this chapter, a generic learning system architecture is presented that can be retrofitted to existing automation platforms of different industrial plants. The architecture offers flexibility and modularity, so that relevant functionalities can be selected for a specific plant on an as-needed basis. Various functionalities such as soft-sensors, outputs prediction, model adaptation, control optimization, anomaly detection, diagnostics and decision supports are discussed in detail. 2021-02-10T12:58:18Z 2021-06-02T10:13:41Z 2021 chapter ONIX_20210602_10.5772/intechopen.92899_497 https://library.oapen.org/handle/20.500.12657/49383 https://directory.doabooks.org/handle/20.500.12854/70354 eng open access image/jpeg image/jpeg image/jpeg n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/49383/1/74393.pdf https://library.oapen.org/bitstream/20.500.12657/49383/1/74393.pdf https://library.oapen.org/bitstream/20.500.12657/49383/1/74393.pdf InTechOpen 10.5772/intechopen.92899 10.5772/intechopen.92899 035ecc65-6737-43cf-a13a-6bdf67ce01f4 open access |
| spellingShingle | learning system, soft-sensors, model predictive control, fault detection, isolation and identification, information fusion thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general Rahman, Moksadur Fentaye, Amare Desalegn Zaccaria, Valentina Aslanidou, Ioanna Dahlquist, Erik Kyprianidis, Konstantinos Chapter A Framework for Learning System for Complex Industrial Processes |
| title | Chapter A Framework for Learning System for Complex Industrial Processes |
| title_full | Chapter A Framework for Learning System for Complex Industrial Processes |
| title_fullStr | Chapter A Framework for Learning System for Complex Industrial Processes |
| title_full_unstemmed | Chapter A Framework for Learning System for Complex Industrial Processes |
| title_short | Chapter A Framework for Learning System for Complex Industrial Processes |
| title_sort | chapter a framework for learning system for complex industrial processes |
| topic | learning system, soft-sensors, model predictive control, fault detection, isolation and identification, information fusion thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general |
| topic_facet | learning system, soft-sensors, model predictive control, fault detection, isolation and identification, information fusion thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general |
| url | ONIX_20210602_10.5772/intechopen.92899_497 |
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