Design water-using network with multiple contaminants by step by step linear programming

LI Baohong; FEI Weiyang

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Design water-using network with multiple contaminants by step by step linear programming

更新时间：2025-12-10

- Design water-using network with multiple contaminants by step by step linear programming
- CIESC Journal Issue 2, Pages: 285-290(2005)
- 作者机构：
  
  清华大学化学工程联合国家重点实验室,北京,100084
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：25 February 2005，
  
  Published：25 February 2005
- 稿件说明：
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LI Baohong, FEI Weiyang. Design water-using network with multiple contaminants by step by step linear programming[J]. CIESC Journal, 2005, (2): 285-290.
DOI：

LI Baohong, FEI Weiyang. Design water-using network with multiple contaminants by step by step linear programming[J]. CIESC Journal, 2005, (2): 285-290. DOI：

摘要

针对含有多种污染物用水网络的设计问题，提出了一种新方法.该方法先基于各个用水过程的污染物最大出口浓度对过程排序，然后采用GAMS(general algebraic modeling system)语言建立线性规划模型，依次对序列中的每个过程的废水回用方案进行优化计算，以确定出其最小新鲜水用量和来自前驱的废水回用量.该方法虽然不能保证所得解的全局最优性，但它能快速地找到最优或近似最优解，而且方法简单.

Abstract

A rapid method for designing water-using network with multiple contaminants is developed.It first arranges all processes based on their maximum outlet concentration，that is

list the limiting outlet concentration of each contaminant for each process

and mark their serial number by Ni

s(irepresents the water-using process

and s the contaminant)

calculate the product of serial number of every contaminant in each process and list those processes by the product.For each process on the list

a linear programming model in GAMS (general algebraic modeling system) language is used to determine the minimum freshwater flowrate and reuse wastewater flowrate from its precursor set. Delete the process from the list after its requirement is satisfied and add its wastewater data to the available wastewater set. Repeat the process until the last process on the list has been taken care of. The new method has been tested in two cases. The obtained solutions are at least as good as reported ones.In general

global optimality is not guaranteed by the new method

but it is simple and can be used to rapidly find an optimal or suboptimal solution.

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