A multi-objective process optimization procedure under uncertainty for sustainable process design

Li Sun; JiPing Pan; Anqi Wang

doi:10.1109/ICBBE.2008.591

A multi-objective process optimization procedure under uncertainty for sustainable process design

Li Sun, JiPing Pan, Anqi Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Sustainable chemical process design can be formulated as a multi-objective optimization (MOO) problem covering economic, environmental and societal aspects. Moreover, uncertainties are unavoidable during the process design. So, uncertainties should be involved in the optimization. In this work, authors work on the basis of stochastic programming to deal with uncertainty factors, and integrate MOO deterministic algorithms to identify the optimal process design for the improvement of sustainability from a number of alternatives. The efficacy of the procedure is demonstrated by design of 1-hexene separation process.

Original language	English
Title of host publication	2008 2nd International Conference on Bioinformatics and Biomedical Engineering
Publisher	IEEE
Pages	4373-4376
Number of pages	4
ISBN (Print)	9781424417476
DOIs	https://doi.org/10.1109/ICBBE.2008.591
Publication status	Published - 2008
Externally published	Yes
Event	2008 2nd International Conference on Bioinformatics and Biomedical Engineering - Shanghai , China Duration: 16 May 2008 → 18 May 2008

Conference

Conference	2008 2nd International Conference on Bioinformatics and Biomedical Engineering
Country/Territory	China
City	Shanghai
Period	16/05/08 → 18/05/08

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10.1109/ICBBE.2008.591

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title = "A multi-objective process optimization procedure under uncertainty for sustainable process design",

abstract = "Sustainable chemical process design can be formulated as a multi-objective optimization (MOO) problem covering economic, environmental and societal aspects. Moreover, uncertainties are unavoidable during the process design. So, uncertainties should be involved in the optimization. In this work, authors work on the basis of stochastic programming to deal with uncertainty factors, and integrate MOO deterministic algorithms to identify the optimal process design for the improvement of sustainability from a number of alternatives. The efficacy of the procedure is demonstrated by design of 1-hexene separation process.",

author = "Li Sun and JiPing Pan and Anqi Wang",

year = "2008",

doi = "10.1109/ICBBE.2008.591",

language = "English",

isbn = "9781424417476",

pages = "4373--4376",

booktitle = "2008 2nd International Conference on Bioinformatics and Biomedical Engineering",

publisher = "IEEE",

address = "United States",

note = "2008 2nd International Conference on Bioinformatics and Biomedical Engineering ; Conference date: 16-05-2008 Through 18-05-2008",

}

Sun, L, Pan, J & Wang, A 2008, A multi-objective process optimization procedure under uncertainty for sustainable process design. in 2008 2nd International Conference on Bioinformatics and Biomedical Engineering. IEEE, pp. 4373-4376, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering, Shanghai , China, 16/05/08. https://doi.org/10.1109/ICBBE.2008.591

TY - GEN

T1 - A multi-objective process optimization procedure under uncertainty for sustainable process design

AU - Sun, Li

AU - Pan, JiPing

AU - Wang, Anqi

PY - 2008

Y1 - 2008

N2 - Sustainable chemical process design can be formulated as a multi-objective optimization (MOO) problem covering economic, environmental and societal aspects. Moreover, uncertainties are unavoidable during the process design. So, uncertainties should be involved in the optimization. In this work, authors work on the basis of stochastic programming to deal with uncertainty factors, and integrate MOO deterministic algorithms to identify the optimal process design for the improvement of sustainability from a number of alternatives. The efficacy of the procedure is demonstrated by design of 1-hexene separation process.

AB - Sustainable chemical process design can be formulated as a multi-objective optimization (MOO) problem covering economic, environmental and societal aspects. Moreover, uncertainties are unavoidable during the process design. So, uncertainties should be involved in the optimization. In this work, authors work on the basis of stochastic programming to deal with uncertainty factors, and integrate MOO deterministic algorithms to identify the optimal process design for the improvement of sustainability from a number of alternatives. The efficacy of the procedure is demonstrated by design of 1-hexene separation process.

U2 - 10.1109/ICBBE.2008.591

DO - 10.1109/ICBBE.2008.591

M3 - Conference contribution

SN - 9781424417476

SP - 4373

EP - 4376

BT - 2008 2nd International Conference on Bioinformatics and Biomedical Engineering

PB - IEEE

T2 - 2008 2nd International Conference on Bioinformatics and Biomedical Engineering

Y2 - 16 May 2008 through 18 May 2008

ER -

A multi-objective process optimization procedure under uncertainty for sustainable process design

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