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  1. Article ; Online: Response of macrofaunal assemblages to different pollution pressures of two types of ports

    Zhang, Mingwei / Liu, Chunying / Zhang, Caijie / Zhu, Haiyun / Wan, Jiteng / Liu, Xiaoshou

    Ecological Indicators. 2023 Feb., v. 146 p.109858-

    2023  

    Abstract: Pollution status and benthic ecological quality of the two types of ports were assessed based on heavy metals and macrofaunal assemblages. Macrofaunal abundance and biomass in the industrial port were significantly higher than those in the fishing port. ... ...

    Abstract Pollution status and benthic ecological quality of the two types of ports were assessed based on heavy metals and macrofaunal assemblages. Macrofaunal abundance and biomass in the industrial port were significantly higher than those in the fishing port. The dominant species of the two ports were Echinocardium cordatum and Nephtys oligobranchia. The AZTI marine biotic index (AMBI), multivariate AMBI, and Shannon-Wiener diversity index demonstrated that the benthic ecological quality of the two ports was moderate to good. The benthic ecological quality of the distant port sites was better than those of the nearby port sites. The geoaccumulation index and the Hakanson potential ecological risk index indicated that mercury posed a serious threat to port sediment pollution. AMBI, multivariate AMBI, and Shannon-Wiener diversity index were not good indicators for heavy metal pollution. The dominant species and Pielou's evenness index were significantly correlated with heavy metal pollution and were good biological indicators.
    Keywords Nephtys ; biomass ; dominant species ; fauna ; heavy metals ; mercury ; risk ; sediment contamination ; Macrofauna ; Ports ; Biotic index ; Pollution assessment
    Language English
    Dates of publication 2023-02
    Publishing place Elsevier Ltd
    Document type Article ; Online
    Note Use and reproduction
    ZDB-ID 2036774-0
    ISSN 1872-7034 ; 1470-160X
    ISSN (online) 1872-7034
    ISSN 1470-160X
    DOI 10.1016/j.ecolind.2022.109858
    Database NAL-Catalogue (AGRICOLA)

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  2. Article ; Online: Modeling and optimizing an electrochemical oxidation process using artificial neural network, genetic algorithm and particle swarm optimization

    Liu Banghai / Jin Chunji / Wan Jiteng / Li Pengfang / Yan Huanxi

    Journal of the Serbian Chemical Society, Vol 83, Iss 3, Pp 379-

    2018  Volume 390

    Abstract: This study proposes a novel hybrid of artificial neural network (ANN), genetic algorithm (GA), and particle swarm optimization (PSO) to model and optimize the relevant parameters of an electrochemical oxidation (EO) Acid Black 2 process. The back ... ...

    Abstract This study proposes a novel hybrid of artificial neural network (ANN), genetic algorithm (GA), and particle swarm optimization (PSO) to model and optimize the relevant parameters of an electrochemical oxidation (EO) Acid Black 2 process. The back propagation neural network (BPNN) was used as a modelling tool. To avoid over-fitting, GA was applied to improve the generalized capability of BPNN by optimizing the weights. In addition, an optimization model was developed to assess the performance of the EO process, where total organic carbon (TOC) removal, mineralization current efficiency (MCE), and the energy consumption per unit of TOC (ECTOC) were considered. The operation conditions of EO were further optimized via PSO. The validation results indicted the proposed method to be a promising method to estimate the efficiency and to optimize the parameters of the EO process.
    Keywords electrochemical oxidation ; artificial neural network ; genetic algorithm ; particle swarm optimization ; Acid Black 2 ; Chemistry ; QD1-999
    Subject code 620
    Language English
    Publishing date 2018-01-01T00:00:00Z
    Publisher Serbian Chemical Society
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Electrochemical oxidation of sulfamethoxazole using Ti/SnO2-Sb/Co-PbO2 electrode through ANN-PSO

    Wan Jiteng / Jin Chunji / Liu Banghai / She Zonglian / Gao Mengchun / Wang Zhengyang

    Journal of the Serbian Chemical Society, Vol 84, Iss 7, Pp 713-

    2019  Volume 727

    Abstract: Even in a trace amounts, the presence of antibiotics in aqueous solution is getting more and more attention. Accordingly, appropriate technologies are needed to efficiently remove these compounds from aqueous environments. In this study, we have examined ...

    Abstract Even in a trace amounts, the presence of antibiotics in aqueous solution is getting more and more attention. Accordingly, appropriate technologies are needed to efficiently remove these compounds from aqueous environments. In this study, we have examined the electrochemical oxidation (EO) of sulfamethoxazole (SMX) on a Co modified PbO2 electrode. The process of EO of SMX in aqueous solution followed the pseudo-first-order kinetics, and the removal efficiency of SMX reached the maximum value of 95.1 % within 60 min. The effects of major factors on SMX oxidation kinetics were studied in detail by single-factor experiments, namely current density (1–20 mA cm-2), solution pH value (2–10), initial concentration of SMX (10–500 mg L-1) and concentration of electrolytes (0.05–0.4 mol L-1). An artificial neural network (ANN) model was used to simulate this EO process. Based on the obtained model, particle swarm optimization (PSO) was used to optimize the operating parameters. The maximum removal efficiency of SMX was obtained at the optimized conditions (e.g., current density of 12.37 mA cm-2, initial pH value of 4.78, initial SMX concentration of 74.45 mg L-1, electrolyte concentration of 0.24 mol L-1 and electrolysis time of 51.49 min). The validation results indicated that this method can ideally be used to optimize the related parameters and predict the anticipated results with acceptable accuracy.
    Keywords Sulfamethoxazole ; electrochemical oxidation ; artificial neural networks ; particle swarm optimization ; Chemistry ; QD1-999
    Subject code 660 ; 620
    Language English
    Publishing date 2019-01-01T00:00:00Z
    Publisher Serbian Chemical Society
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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