Energy Conservation Optimization Research on Compression Water Chiller

doi:10.7652/xjtuxb201806020

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Energy Conservation Optimization Research on Compression Water Chiller

更新时间：2025-07-09

- Energy Conservation Optimization Research on Compression Water Chiller
- Vol. 52, Issue 6, Pages: 135-142(2018)
- 作者机构：
  
  天津大学电气自动化与信息工程学院,天津,300072
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201806020
  CLC： TB61
- Online First：10 June 2018，
  
  Published：2018
- 稿件说明：
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Energy Conservation Optimization Research on Compression Water Chiller[J]. 2018, 52(6): 135-142.
DOI：

Energy Conservation Optimization Research on Compression Water Chiller[J]. 2018, 52(6): 135-142. DOI： 10.7652/xjtuxb201806020.

摘要

针对现有优化方法未能充分考虑蒸发器过热度对压缩式冷水机组运行效率与稳定性的影响

导致过热度优化值偏高、系统运行能效不能达到最优的问题

提出了一种能够最小化过热度的节能优化方法

以进一步挖掘冷水机组节能潜力。通过分析制冷循环

建立了冷水机组稳态机理模型以及能耗模型

并提出了最小稳定过热度曲线的实验获取方法; 然后根据最小稳定过热度曲线在线调整过热度约束

以系统总能耗最小为优化目标

利用遗传算法对冷水机组不同负荷下设定点进行寻优计算

实现系统优化运行。对比实验结果表明:改进的过热度约束在保证系统稳定的前提下

有效地扩大了寻优范围

在夏季典型工况下的工作时段

可比现有优化方法平均节能1.92%。该研究对压缩式冷水机组优化节能控制具有重要的现实意义。

Abstract

In view of the fact that conventional optimization methods fail to take into full consideration the influence of the evaporator superheat on the efficiency and stability of compression type water chiller

leading to a high superheat set point and non-optimum energy efficiency

an energy saving optimization method which can minimize the degree of superheat is proposed to further exploit the energy saving potential of water chillers. By analyzing the mechanism of water chiller refrigeration cycle

the steady-state model and energy consumption model of the system are established. Besides

the experimental method for obtaining the minimum stable heat curve is also put forward. In this paper

a steady-state model of cold water chiller is established

in which the minimization of the total energy conservation is taken as the optimization goal

and genetic algorithm is used to calculate the optimal set points. Based on the minimum stable superheat curve

the superheat constraint is adjusted online under different heat loads. The comparison of experiment results shows that the proposed method can effectively expand the optimization range

obtaining lower superheat value and an average energy saving of 1.92% than existing optimization methods under typical working conditions in summer.

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references

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