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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Vydáno v:Chinese journal of chemical engineering Ročník 69; číslo 5; s. 263 - 278
Hlavní autoři: Qu, Qingping, Liu, Daoyan, Lyu, Hao, Sun, Jinsheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: 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. [Display omitted]
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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crossref_primary_10_1016_j_esr_2025_101698
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Issue 5
Keywords Distillation
Process synthesis
Differential evolution algorithm
Superstructure-based optimization
Coal-to-ethanol
Language English
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Snippet The coal-to-ethanol process, as the clean coal utilization, faces challenges from the energy-intensive distillation that separates multi-component effluents...
The coal-to-ethanol process,as the clean coal utilization,faces challenges from the energy-intensive distillation that separates multi-component effluents for...
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StartPage 263
SubjectTerms Coal-to-ethanol
Differential evolution algorithm
Distillation
Process synthesis
Superstructure-based optimization
Title Process synthesis for the separation of coal-to-ethanol products
URI https://dx.doi.org/10.1016/j.cjche.2023.12.019
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