Efficient energy management of CO2 capture plant using control-based optimization approach under plant and market uncertainties
This paper employs a control-based optimization algorithm encompassing of an intelligence model predictive control (MPC) scheme and mixed integer non-linear programming (MINLP) for coal-fired power plant retrofitted with flexible solvent-based post combustion CO 2 capture (PCC) plant (integrated pla...
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| Format: | Article |
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Elsevier Ltd.
2019
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| _version_ | 1796864456395849728 |
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| author | Abdul Manaf, Norhuda Qadir, Abdul Abbas, Ali |
| author_facet | Abdul Manaf, Norhuda Qadir, Abdul Abbas, Ali |
| author_sort | Abdul Manaf, Norhuda |
| collection | ePrints |
| description | This paper employs a control-based optimization algorithm encompassing of an intelligence model predictive control (MPC) scheme and mixed integer non-linear programming (MINLP) for coal-fired power plant retrofitted with flexible solvent-based post combustion CO 2 capture (PCC) plant (integrated plant). The agility and robustness of the developed control algorithm (MPC) is demonstrated through the control response time and efficiency of energy requirement including the financial and operational benefits of the plant subjected to plant and market uncertainties. While, the MINLP is utilized to forecast plant operational modes by ensuring the operational fidelity of integrated plant. This involves utilization of historical (2011) and forecast (2020) market conditions (electricity tariff and carbon price) subject to maximum plant net operating revenue. The outcomes show that the future power plant will operate in mixed operation modes, for instance in unit turndown and load following modes, which contribute to a minimum capture energy penalty at 3.13 MJ th /tonne CO 2 . Moreover, under the same year (2020), MPC exhibits superior control performance by satisfactorily obtain 94% actual CO 2 capture from the ideal cumulative CO 2 capture. Additionally, the integrated plant is capable to resume approximately 96% actual revenue from the ideal net operating revenue projected by the control-based optimization algorithm. The algorithm demonstrates that the installation of control system package (MPC) into the flexible PCC plant associated with coal-power generator could contribute to efficient energy management subjects to unprecedented uncertainties. |
| first_indexed | 2024-03-05T20:42:09Z |
| format | Article |
| id | utm.eprints-87511 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:42:09Z |
| publishDate | 2019 |
| publisher | Elsevier Ltd. |
| record_format | dspace |
| spelling | utm.eprints-875112020-11-08T04:05:29Z http://eprints.utm.my/87511/ Efficient energy management of CO2 capture plant using control-based optimization approach under plant and market uncertainties Abdul Manaf, Norhuda Qadir, Abdul Abbas, Ali TA Engineering (General). Civil engineering (General) This paper employs a control-based optimization algorithm encompassing of an intelligence model predictive control (MPC) scheme and mixed integer non-linear programming (MINLP) for coal-fired power plant retrofitted with flexible solvent-based post combustion CO 2 capture (PCC) plant (integrated plant). The agility and robustness of the developed control algorithm (MPC) is demonstrated through the control response time and efficiency of energy requirement including the financial and operational benefits of the plant subjected to plant and market uncertainties. While, the MINLP is utilized to forecast plant operational modes by ensuring the operational fidelity of integrated plant. This involves utilization of historical (2011) and forecast (2020) market conditions (electricity tariff and carbon price) subject to maximum plant net operating revenue. The outcomes show that the future power plant will operate in mixed operation modes, for instance in unit turndown and load following modes, which contribute to a minimum capture energy penalty at 3.13 MJ th /tonne CO 2 . Moreover, under the same year (2020), MPC exhibits superior control performance by satisfactorily obtain 94% actual CO 2 capture from the ideal cumulative CO 2 capture. Additionally, the integrated plant is capable to resume approximately 96% actual revenue from the ideal net operating revenue projected by the control-based optimization algorithm. The algorithm demonstrates that the installation of control system package (MPC) into the flexible PCC plant associated with coal-power generator could contribute to efficient energy management subjects to unprecedented uncertainties. Elsevier Ltd. 2019-02 Article PeerReviewed Abdul Manaf, Norhuda and Qadir, Abdul and Abbas, Ali (2019) Efficient energy management of CO2 capture plant using control-based optimization approach under plant and market uncertainties. Journal of Process Control, 74 . pp. 2-12. ISSN 0959-1524 http://dx.doi.org/10.1016/j.jprocont.2018.07.015 DOI:10.1016/j.jprocont.2018.07.015 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Abdul Manaf, Norhuda Qadir, Abdul Abbas, Ali Efficient energy management of CO2 capture plant using control-based optimization approach under plant and market uncertainties |
| title | Efficient energy management of CO2 capture plant using control-based optimization approach under plant and market uncertainties |
| title_full | Efficient energy management of CO2 capture plant using control-based optimization approach under plant and market uncertainties |
| title_fullStr | Efficient energy management of CO2 capture plant using control-based optimization approach under plant and market uncertainties |
| title_full_unstemmed | Efficient energy management of CO2 capture plant using control-based optimization approach under plant and market uncertainties |
| title_short | Efficient energy management of CO2 capture plant using control-based optimization approach under plant and market uncertainties |
| title_sort | efficient energy management of co2 capture plant using control based optimization approach under plant and market uncertainties |
| topic | TA Engineering (General). Civil engineering (General) |
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