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人工神经网络优化电活化硫酸盐降解微囊藻毒素

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董皓月, 范莎莎, 金春姬, 高孟春, 佘宗莲, 赵阳国. 人工神经网络优化电活化硫酸盐降解微囊藻毒素[J]. 环境化学, 2020, (12): 3390-3401. doi: 10.7524/j.issn.0254-6108.2019092501
引用本文: 董皓月, 范莎莎, 金春姬, 高孟春, 佘宗莲, 赵阳国. 人工神经网络优化电活化硫酸盐降解微囊藻毒素[J]. 环境化学, 2020, (12): 3390-3401. doi: 10.7524/j.issn.0254-6108.2019092501
shu
DONG Haoyue, FAN Shasha, JIN Chunji, GAO Mengchun, SHE Zonglian, ZHAO Yangguo. Degradation of microcystin-IR by electrochemically activated sulfate:Variable evaluation and model construction using artificial neural network[J]. Environmental Chemistry, 2020, (12): 3390-3401. doi: 10.7524/j.issn.0254-6108.2019092501
Citation: DONG Haoyue, FAN Shasha, JIN Chunji, GAO Mengchun, SHE Zonglian, ZHAO Yangguo. Degradation of microcystin-IR by electrochemically activated sulfate:Variable evaluation and model construction using artificial neural network[J]. Environmental Chemistry, 2020, (12): 3390-3401. doi: 10.7524/j.issn.0254-6108.2019092501
shu

人工神经网络优化电活化硫酸盐降解微囊藻毒素

  • 1. 中国海洋大学环境科学与工程学院, 青岛, 266100;

  • 2. 中国海洋大学海洋环境与生态教育部重点实验室, 青岛, 266100

    • 通讯作者: 金春姬, E-mail: jinhou@ouc.edu.cn ; 
  • 基金项目:

    中央高校基本科研业务费专项基金（201964004）资助.

Degradation of microcystin-IR by electrochemically activated sulfate:Variable evaluation and model construction using artificial neural network

  • 1. College of Environmental Science and Engineering, Ocean University of China, Qingdao, 266100, China;

  • 2. Key Laboratory of Marine Environmental Science and Ecology, Ministry of Education, Ocean University of China, Qingdao, 266100, China

    • Corresponding author: JIN Chunji, jinhou@ouc.edu.cn ; 
  • Fund Project: Supported by the Fundamental Research Funds for the Central Universities (201964004).

  • 摘要: 微囊藻毒素-LR（microcystin-LR，MC-LR）是一种威胁饮用水安全且难降解的蓝藻毒素.掺硼金刚石（boron-doped diamond，BDD）阳极电化学氧化水中MC-LR的反应机理和电解质种类密切相关.本研究通过比较两种电解质（Na2SO4和惰性的NaNO3）条件下，电流密度、循环流速、电解质浓度、反应时间对MC-LR去除效果的影响来分析Na2SO4条件下的反应机理.研究发现Na2SO4作电解质对MC-LR去除效率优于NaNO3，最大反应速率常数达到0.1374 min-1，最短半衰期为5.04 min，这是由于体系产生了硫酸根自由基.电子自旋共振谱图与过硫酸盐检测结果进一步证明，BDD/Na2SO4体系电化学氧化MC-LR实际上包含电活化硫酸盐产生硫酸根自由基的过程.前向型人工神经网络模型被用以反映传质对电活化硫酸盐工艺的影响.经过基因算法优化，模型对于MC-LR浓度变化具有一定的泛化能力.模型表明，传质控制下增大MC-LR浓度，循环流速的权重降低，最佳循环流速降低.使用粒子群算法寻优：电流密度45 mA·cm-2，循环流速180 min·L-1，电解质浓度140 mmol·L-1，处理30 min得到MC-LR最大去除率98.36%，表明人工神经网络优化电活化硫酸盐降解MC-LR具有应用前景.
    Abstract: Microcystin-LR (MC-LR) is a refractory cyanotoxin and a threat to the safety of drinking water. The reaction mechanism of electrochemical oxidation of MC-LR in aqueous solution using a boron-doped diamond anode is closely related to the type of electrolyte. In this study, the effects of the treatment time, supporting electrolyte concentration (SEC), flow rate, applied current density (jappl) on the degradation of MC-LR in systems using sodium sulfate (Na2SO4) and inert sodium nitrate (NaNO3) were compared to explore the reaction mechanism of the BDD/Na2SO4. The results indicated that Na2SO4 was superior to NaNO3 with regard to degradation rate. The maximum reaction rate constant reached 0.1374 min-1, and the shortest half-life was 5.04 min. The reason was the generation of sulfate radicals by the electrochemically activated sulfate confirmed by the electron spin resonance spectra and the detection of persulfate. An artificial neural network (ANN) model was used to explore the effects of mass transfer on the degradation of MC-LR by sulfate radicals. After optimization by the genetic algorithm, the model could resist the change of MC-LR concentration. The optimized model showed that the weight of the flow rate and the optimal rate were reduced when the MC-LR concentration was increased under mass transfer control. The particle swarm optimization results showed that 140 mmol·L-1 Na2SO4 provided the best overall result for a high treatment time (30 min), high jappl (45 mA·cm-2) and high flow rate (180 min·L-1) level; under these conditions, the MC-LR degradation was 98.36%. As a result, the artificial neural network was suitable in optimizing the degradation of MC-LR by electrochemically activated sulfate, thus showing great promise for applications.
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  • 收稿日期:  2019-09-25
董皓月, 范莎莎, 金春姬, 高孟春, 佘宗莲, 赵阳国. 人工神经网络优化电活化硫酸盐降解微囊藻毒素[J]. 环境化学, 2020, (12): 3390-3401. doi: 10.7524/j.issn.0254-6108.2019092501
引用本文: 董皓月, 范莎莎, 金春姬, 高孟春, 佘宗莲, 赵阳国. 人工神经网络优化电活化硫酸盐降解微囊藻毒素[J]. 环境化学, 2020, (12): 3390-3401. doi: 10.7524/j.issn.0254-6108.2019092501
shu
DONG Haoyue, FAN Shasha, JIN Chunji, GAO Mengchun, SHE Zonglian, ZHAO Yangguo. Degradation of microcystin-IR by electrochemically activated sulfate:Variable evaluation and model construction using artificial neural network[J]. Environmental Chemistry, 2020, (12): 3390-3401. doi: 10.7524/j.issn.0254-6108.2019092501
Citation: DONG Haoyue, FAN Shasha, JIN Chunji, GAO Mengchun, SHE Zonglian, ZHAO Yangguo. Degradation of microcystin-IR by electrochemically activated sulfate:Variable evaluation and model construction using artificial neural network[J]. Environmental Chemistry, 2020, (12): 3390-3401. doi: 10.7524/j.issn.0254-6108.2019092501
shu

人工神经网络优化电活化硫酸盐降解微囊藻毒素

    通讯作者: 金春姬, E-mail: jinhou@ouc.edu.cn ; 
  • 1. 中国海洋大学环境科学与工程学院, 青岛, 266100;
  • 2. 中国海洋大学海洋环境与生态教育部重点实验室, 青岛, 266100
  • 收稿日期:  2019-09-25
基金项目:

中央高校基本科研业务费专项基金（201964004）资助.

摘要: 微囊藻毒素-LR（microcystin-LR，MC-LR）是一种威胁饮用水安全且难降解的蓝藻毒素.掺硼金刚石（boron-doped diamond，BDD）阳极电化学氧化水中MC-LR的反应机理和电解质种类密切相关.本研究通过比较两种电解质（Na2SO4和惰性的NaNO3）条件下，电流密度、循环流速、电解质浓度、反应时间对MC-LR去除效果的影响来分析Na2SO4条件下的反应机理.研究发现Na2SO4作电解质对MC-LR去除效率优于NaNO3，最大反应速率常数达到0.1374 min-1，最短半衰期为5.04 min，这是由于体系产生了硫酸根自由基.电子自旋共振谱图与过硫酸盐检测结果进一步证明，BDD/Na2SO4体系电化学氧化MC-LR实际上包含电活化硫酸盐产生硫酸根自由基的过程.前向型人工神经网络模型被用以反映传质对电活化硫酸盐工艺的影响.经过基因算法优化，模型对于MC-LR浓度变化具有一定的泛化能力.模型表明，传质控制下增大MC-LR浓度，循环流速的权重降低，最佳循环流速降低.使用粒子群算法寻优：电流密度45 mA·cm-2，循环流速180 min·L-1，电解质浓度140 mmol·L-1，处理30 min得到MC-LR最大去除率98.36%，表明人工神经网络优化电活化硫酸盐降解MC-LR具有应用前景.

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