[1]刘颖,王信洁,李温涛,等.基于机器学习的电渗析单位能耗原位监测[J].中国给水排水,2026,42(13):26-34.
　LiuYing,WangXinjie,LiWentao,et al.Machine Learning-based In?situ Monitoring of Specific Energy Consumption in Electrodialysis Processes[J].China Water & Wastewater,2026,42(13):26-34.
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基于机器学习的电渗析单位能耗原位监测

中国给水排水[ISSN:1000-4062/CN:12-1073/TU] 卷: 第42卷 期数: 2026年第13期 页码: 26-34 栏目: 出版日期: 2026-07-01
Title:
Machine Learning-based In?situ Monitoring of Specific Energy Consumption in Electrodialysis Processes
作者:
刘颖1， 王信洁1， 李温涛1， 张新波1， 赵云峰2， 陈锋3， 古金霞4， 李志猛5
（1.天津城建大学 环境与市政工程学院，天津 300384；2.国家管网集团北方管道有限责任公司，河北 廊坊 065000；3.北华航天工业学院 建筑工程学院，河北 廊坊 065000；4.天津城建大学 理学院，天津 300384；5.天津城建大学 控制与机械工程学院，天津 300384）
Author(s):
Liu　Ying1， Wang　Xinjie1， Li　Wentao1， Zhang　Xinbo1， Zhao　Yunfeng2， Chen　Feng3， Gu　Jinxia4， Li　Zhimeng5
（1. School of Environmental and Municipal Engineering， Tianjin Chengjian University， Tianjin 300384， China； 2. National Pipe Network Group Northern Pipeline Co. Ltd.， Langfang 065000，China； 3. School of Civil Engineering and Architecture， North China Institute of Aerospace Engineering， Langfang 065000， China； 4. School of Science， Tianjin Chengjian University， Tianjin 300384，China； 5. School of Control and Mechanical Engineering， Tianjin Chengjian University， Tianjin 300384， China）
关键词:
电渗析;  单位能耗;  机器学习;  原位监测;  多层感知机
Keywords:
electrodialysis;  specific energy consumption;  machine learning;  in-situ monitoring;  multilayer perceptron
摘要:
针对双极膜电渗析（BMED）过程单位能耗（SEC）难以在线监测的问题，提出一种基于物联网（IoT）架构的机器学习（ML）监测方法。基于离线配制的NaOH溶液数据，对比了多层感知机（MLP）、支持向量回归（SVR）、梯度提升回归（GBR）和长短期记忆网络（LSTM）四种碱浓度预测模型，其中MLP表现最优，在测试集上的决定系数（R2）均值为1.000 0。将该模型迁移应用于BMED系统，通过碱室中原位部署的pH、温度、电导率、液位及膜堆电压传感器实时采集数据，在线预测碱浓度并推演SEC。实验表明，浓度预测的R2为0.905 0，且由此推演的SEC表现出“初始快速上升至峰值，经回落与小幅调整后，长期运行缓慢上升”的动态演化规律。该系统集成感知、传输、平台三层IoT架构，实现了从离线建模到在线验证的全流程智能监测，为BMED节能调控提供了有效工具。
Abstract:
To address the challenge of online monitoring of specific energy consumption (SEC) in bipolar membrane electrodialysis (BMED) processes, a machine learning (ML)-assisted monitoring approach based on the Internet of Things (IoT) was proposed. Using data obtained from offline prepared NaOH solutions, four predictive models for alkali concentration including multilayer perceptron (MLP), support vector regression (SVR)， gradient boosting regression (GBR) and long short-term memory (LSTM) were compared. The MLP model demonstrated the best performance, achieving a coefficient of determination (R2) of 1.000 0 on the test dataset.The developed model was subsequently transferred for online prediction of alkali concentration during the BMED process. Real-time data were acquired by in-situ sensors deployed within the alkali compartment, which monitored parameters including pH, temperature, conductivity, liquid level, and membrane stack voltage. These data streams enabled the online prediction of alkali concentration and the subsequent derivation of SEC. Experimental results showed that the R2 for online concentration prediction reached 0.905 0, and the derived SEC exhibited a characteristic dynamic evolution pattern of initially rapidly rising to a peak, followed by a decline and minor adjustments, and then slowly increasing over long-term operation. The proposed system integrated a three-tier IoT architecture comprising the perception layer, transmission layer and platform layer, enabling full-process intelligent monitoring that spans from offline model development to online validation. This framework provides a viable and effective tool for facilitating energy-efficient operation and dynamic regulation of the BMED process.

相似文献/References:

[1]周明飞,连坤宙,王璟,等.电渗析技术处理脱硫废水的效果分析[J].中国给水排水,2020,36(21):80.
　ZHOU Ming-fei,LIAN Kun-zhou,WANG Jing,et al.Efficiency Analysis of Desulfurization Wastewater Treated by Electrodialysis Technology[J].China Water & Wastewater,2020,36(13):80.

更新日期/Last Update: 2026-07-01