Process synthesis for the separation of coal-to-ethanol products
The coal-to-ethanol process, as the clean coal utilization, faces challenges from the energy-intensive distillation that separates multi-component effluents for pure ethanol. Referring to at least eight columns, the synthesis of the ethanol distillation system is impracticable for exhaustive compari...
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| Veröffentlicht in: | Chinese journal of chemical engineering Jg. 69; H. 5; S. 263 - 278 |
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| Sprache: | Englisch |
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Elsevier B.V
01.05.2024
School of Chemical Engineering and Technology,Tianjin University,Tianjin 300072,China%Sustainable Energy and Environment Thrust,The Hong Kong University of Science and Technology(Guangzhou),Nansha 511400,China |
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| ISSN: | 1004-9541, 2210-321X |
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| Abstract | The coal-to-ethanol process, as the clean coal utilization, faces challenges from the energy-intensive distillation that separates multi-component effluents for pure ethanol. Referring to at least eight columns, the synthesis of the ethanol distillation system is impracticable for exhaustive comparison and difficult for conventional superstructure-based optimization as rigorous models are used. This work adopts a superstructure-based framework, which combines the strategy that adaptively selects branches of the state-equipment network and the parallel stochastic algorithm for process synthesis. High-performance computing significantly reduces time consumption, and the adaptive strategy substantially lowers the complexity of the superstructure model. Moreover, parallel computing, elite search, population redistribution, and retention strategies for irrelevant parameters are used to improve the optimization efficiency further. The optimization terminates after 3000 generations, providing a flowsheet solution that applies two non-sharp splitting options in its distillation sequence. As a result, the 59-dimension superstructure-based optimization was solved efficiently via a differential evolution algorithm, and a high-quality solution with a 28.34% lower total annual cost than the benchmark was obtained. Meanwhile, the solution of the superstructure-based optimization is comparable to that obtained by optimizing a single specific configuration one by one. It indicates that the superstructure-based optimization that combines the adaptive strategy can be a promising approach to handling the process synthesis of large-scale and complex chemical processes.
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| AbstractList | The coal-to-ethanol process, as the clean coal utilization, faces challenges from the energy-intensive distillation that separates multi-component effluents for pure ethanol. Referring to at least eight columns, the synthesis of the ethanol distillation system is impracticable for exhaustive comparison and difficult for conventional superstructure-based optimization as rigorous models are used. This work adopts a superstructure-based framework, which combines the strategy that adaptively selects branches of the state-equipment network and the parallel stochastic algorithm for process synthesis. High-performance computing significantly reduces time consumption, and the adaptive strategy substantially lowers the complexity of the superstructure model. Moreover, parallel computing, elite search, population redistribution, and retention strategies for irrelevant parameters are used to improve the optimization efficiency further. The optimization terminates after 3000 generations, providing a flowsheet solution that applies two non-sharp splitting options in its distillation sequence. As a result, the 59-dimension superstructure-based optimization was solved efficiently via a differential evolution algorithm, and a high-quality solution with a 28.34% lower total annual cost than the benchmark was obtained. Meanwhile, the solution of the superstructure-based optimization is comparable to that obtained by optimizing a single specific configuration one by one. It indicates that the superstructure-based optimization that combines the adaptive strategy can be a promising approach to handling the process synthesis of large-scale and complex chemical processes.
[Display omitted] The coal-to-ethanol process,as the clean coal utilization,faces challenges from the energy-intensive distillation that separates multi-component effluents for pure ethanol.Referring to at least eight col-umns,the synthesis of the ethanol distillation system is impracticable for exhaustive comparison and difficult for conventional superstructure-based optimization as rigorous models are used.This work adopts a superstructure-based framework,which combines the strategy that adaptively selects branches of the state-equipment network and the parallel stochastic algorithm for process synthesis.High-performance computing significantly reduces time consumption,and the adaptive strategy substan-tially lowers the complexity of the superstructure model.Moreover,parallel computing,elite search,population redistribution,and retention strategies for irrelevant parameters are used to improve the optimization efficiency further.The optimization terminates after 3000 generations,providing a flow-sheet solution that applies two non-sharp splitting options in its distillation sequence.As a result,the 59-dimension superstructure-based optimization was solved efficiently via a differential evolution al-gorithm,and a high-quality solution with a 28.34％lower total annual cost than the benchmark was obtained.Meanwhile,the solution of the superstructure-based optimization is comparable to that ob-tained by optimizing a single specific configuration one by one.It indicates that the superstructure-based optimization that combines the adaptive strategy can be a promising approach to handling the process synthesis of large-scale and complex chemical processes. |
| Author | Qu, Qingping Liu, Daoyan Lyu, Hao Sun, Jinsheng |
| AuthorAffiliation | School of Chemical Engineering and Technology,Tianjin University,Tianjin 300072,China%Sustainable Energy and Environment Thrust,The Hong Kong University of Science and Technology(Guangzhou),Nansha 511400,China |
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| Cites_doi | 10.1039/C4CY01580K 10.1016/0098-1354(95)87019-9 10.1109/TEVC.2010.2059031 10.1016/j.compchemeng.2003.06.003 10.1016/j.compchemeng.2019.05.028 10.1007/s11705-023-2301-4 10.1021/acs.iecr.2c03388 10.1016/j.cattod.2017.05.070 10.1016/j.cep.2021.108731 10.1016/S1570-7946(03)80113-X 10.1016/j.seppur.2021.118520 10.1016/j.compchemeng.2020.106808 10.1021/ef700411x 10.1016/S0098-1354(99)00003-4 10.1021/ie00080a013 10.1021/ef900080g 10.1016/j.cherd.2021.02.023 10.1002/cite.201400037 10.1016/j.compchemeng.2008.08.001 10.1016/j.seppur.2020.117907 10.1016/j.apenergy.2022.118791 10.1016/j.apenergy.2020.115574 10.1016/j.cjche.2023.08.010 10.1016/j.seppur.2022.121968 10.1016/0098-1354(90)87033-L |
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| Keywords | Distillation Process synthesis Differential evolution algorithm Superstructure-based optimization Coal-to-ethanol |
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| SubjectTerms | Coal-to-ethanol Differential evolution algorithm Distillation Process synthesis Superstructure-based optimization |
| Title | Process synthesis for the separation of coal-to-ethanol products |
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