Matrix expression of composite stream for multiple-level energy integration

王杭州 陈丙珍 何小荣

您当前的位置：

首页 >

文章列表页 >

Matrix expression of composite stream for multiple-level energy integration

更新时间：2025-12-10

- Matrix expression of composite stream for multiple-level energy integration
- CIESC Journal Issue 3, (2007)
- 作者机构：
  
  清华大学化学工程系
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：05 March 2007，
  
  Published：05 March 2007
- 稿件说明：
移动端阅览
王杭州陈丙珍何小荣. Matrix expression of composite stream for multiple-level energy integration[J]. CIESC Journal, 2007, (3).
DOI：

王杭州陈丙珍何小荣. Matrix expression of composite stream for multiple-level energy integration[J]. CIESC Journal, 2007, (3). DOI：

摘要

夹点技术在工厂的能量系统设计和改造过程中发挥着巨大的作用。用夹点技术分析能量系统时，原始物流的信息不断汇集，得到总组合曲线。当物流信息向上汇总时，通过组合曲线可在更高层面查看系统的能量集成情况。本文提出一种从总组合曲线中获取局部信息的方法——组合物流矩阵表示方法，利用此方法向下分解可获得组合曲线上某个温度区间中包含的原始物流的信息。最后举例说明组合物流矩阵在能量系统设计和改造过程中的作用。

Abstract

Pinch technology plays an important role in chemical process design for years.In pinch technology

composite curves are widely consumed

giving more convenience in analyzing how utilities are used and required. In order to give a more efficient process design

sometimes we need to know the detailed information in the composite curve. This paper proposes a method that uses the composite stream matrix to describe the detailed information about origin streams in composite curves.One case study is demonstrated to show the usage of the proposed method.

关键词

Keywords

references

Views

321

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Exothermic characteristics of energy carrier for calcium looping thermo-chemical energy storage

A two-stage dual-channel deep learning model for chemical fault classification

Progress in research of selective oxidation of low carbon polyols over nano-Au catalysts with precise electronic effect regulation

Performance analysis and application of thermal and humidity control system cascade driven by waste heat

Prediction of biomass pyrolysis product yield based on machine learning algorithms

Related Author

No data

Related Institution

No data

⁰