A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design

孙力; Helen H. Lou

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A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design

Updated：2026-01-06

- A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design
- A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design
- 中国化学工程学报（英文版） 2008年16卷第1期页码：39-42
- Affiliations：
  
  1. Department of Chemical Engineering, Dalian University of Technology,Dalian,China,116023
  2. Department of Chemical Engineering, Lamar University, Beaumont,Texas,USA,77710
- Author bio：
- Funds：
  
  Dalian University of Technology, the US National Science Foundation (No.CTS-0407494);the Texas Ad-vanced Technology Program (No.003581-0044-2003)
- DOI：
  中图分类号：
- 纸质出版：2008
- Accepted：
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孙力, Helen H. Lou. A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design[J]. 中国化学工程学报（英文版）, 2008,16(1):39-42.

SUN Li, Helen H. Lou. A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design[J]. , 2008, 16(1): 39-42.
孙力, Helen H. Lou. A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design[J]. 中国化学工程学报（英文版）, 2008,16(1):39-42. DOI：

SUN Li, Helen H. Lou. A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design[J]. , 2008, 16(1): 39-42. DOI：

摘要

Abstract

In many circumstances

chemical process design can be formulated as a multi-objective optimization(MOO) problem. Examples include bi-objective optimization problems

where the economic objective is maxi-mized and environmental impact is minimized simultaneously. Moreover

the random behavior in the process

property

market fluctuation

errors in model prediction and so on would affect the performance of a process.Therefore

it is essential to develop a MOO methodology under uncertainty. In this article

the authors propose ageneric and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a casestudy based on the design of a condensate treatment unit in an ammonia plant.

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