A Study on Adaptive Hourly Optimization for the Mechanical Vapor Recompression Evaporative Crystallization System

JIANG Hua; QIN Hua; GONG Wuqi

doi:10.7652/xjtuxb202403005

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A Study on Adaptive Hourly Optimization for the Mechanical Vapor Recompression Evaporative Crystallization System

更新时间：2025-07-09

- A Study on Adaptive Hourly Optimization for the Mechanical Vapor Recompression Evaporative Crystallization System
- Vol. 58, Issue 3, Pages: 49-57(2024)
- 作者机构：
  
  1. 西安科技大学能源学院,西安,710054
  2. 西安交通大学能源与动力工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202403005
  CLC： TQ051.62;X703
- Online First：10 March 2024，
  
  Published：2024
- 稿件说明：
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JIANG Hua, QIN Hua, GONG Wuqi. A Study on Adaptive Hourly Optimization for the Mechanical Vapor Recompression Evaporative Crystallization System[J]. 2024, 58(3): 49-57.
DOI：

JIANG Hua, QIN Hua, GONG Wuqi. A Study on Adaptive Hourly Optimization for the Mechanical Vapor Recompression Evaporative Crystallization System[J]. 2024, 58(3): 49-57. DOI： 10.7652/xjtuxb202403005.

摘要

为实现工业废水处理过程中进料流量和进料浓度波动状态下的机械蒸汽再压缩系统逐时优化

建立了基于自适应SPAE2算法的逐时优化模型。采用SPEA2算法并结合自适应交叉概率、变异概率以及组合权重法的多目标逐时优化

以系统总功耗和总换热面积为优化目标

得到蒸发温度、压缩温升的最优组合。在原始研究的基础上

改进了SPEA2算法

并且将原始数据与优化结果进行对比

自适应SPEA2算法具有更强的全局寻优能力

优化结果具有更高准确性。利用最小二乘法对优化结果进行数据拟合

优化结果与恒定蒸发工况下结果相比:系统总功耗平均降低123.7 kW

换热面积平均减少36.3 m

; 性能系数和效率分别平均提高8.8%和26.6%

损失平均降低102.3 kW。研究结果表明

所建立的系统逐时优化模型可以得到系统进料波动状态下各设备参数逐时变化值

提高了目标系统的能量利用率和热力学完善度。

Abstract

To achieve the hourly optimization of the mechanical vapor recompression(MVR)system under the condition of fluctuating feed flow and concentration in industrial wastewater treatment processes

an hourly optimization model based on the adaptive SPAE2 algorithm is proposed in this study. The SPEA2 algorithm is employed along with adaptive crossover probability

mutation probability

and a combination weighting method for multi-objective hourly optimization. The total power consumption and total heat transfer area of the system are considered as optimization object

ives to obtain the optimal combination of evaporation temperature and compression temperature rise. Building on the original research

the SPEA2 algorithm is improved

and the original data is compared with the optimization results. It is concluded that the adaptive SPEA2 algorithm exhibits stronger global optimization ability

leading to more accurate optimization results. The least squares method is used to fit the optimization results. When compared with the results under constant evaporation conditions

the optimization results show the following improvements on average: the total power consumption decreases by 123.7 kW

the heat transfer area decreases by 36.3 m

the coefficient of performance(COP)and energy efficiency increase by 8.8% and 26.6%

and the energy loss decreases by 102.3 kW. These results indicate that the hourly optimization model established for the system can obtain the hourly variations of equipment parameters under fluctuating feed conditions. This improves the system's energy utilization efficiency and thermodynamic performance.

关键词

Keywords

references

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