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光伏供电的纺织电芬顿废水处理系统的污染物分析与生命周期评估

Pollutants Analysis and Life Cycle Assessment of a Photovoltaic Powered Textile Electro-Fenton Wastewater Treatment System.

作者信息

Zhang Chenyang, Zhang Wei, Zeng Xiding, Guo Jiahong, Wang Qing, Bai Zepu

机构信息

College of Architecture and Environment, Sichuan University, Chengdu 610065, China.

出版信息

ACS Omega. 2024 Sep 17;9(39):40477-40484. doi: 10.1021/acsomega.4c03397. eCollection 2024 Oct 1.

DOI:10.1021/acsomega.4c03397
PMID:39372014
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11447860/
Abstract

Textile wastewater poses a substantial environmental challenge due to the persistence of organic dyes. This study introduces a novel approach using photovoltaic (PV) powered electro-Fenton (EF) technology for effective treatment of textile wastewater. Acid orange 7 (AO7), methylene blue (MB), and malachite green (MG) were selected as representative organic dyes to validate the method under varying experimental conditions. Analysis of variance (ANOVA) highlighted the significant influence of pollutant type, pH levels, and current density on the degradation efficiency of the system, with optimal conditions observed at pH = 3 and high current density. To underscore the environmental benefits, a comprehensive life cycle assessment (LCA) was conducted. The PV-powered EF system, when implemented in a textile mill, exhibited an energy payback time (EPBT) of 9.53 years, a greenhouse gas payback time (GPBT) of 4.45 years, and a life cycle cost (LCC) of 1.9 × 10 RMB. Comparative analysis with conventional Fenton and EF processes revealed substantial energy savings, with carbon emissions reduced by 95% and 78%, and energy consumption reduced by 87% and 52%, respectively.

摘要

由于有机染料的持久性，纺织废水对环境构成了重大挑战。本研究引入了一种新方法，即使用光伏（PV）供电的电芬顿（EF）技术来有效处理纺织废水。选择酸性橙7（AO7）、亚甲基蓝（MB）和孔雀石绿（MG）作为代表性有机染料，以在不同实验条件下验证该方法。方差分析（ANOVA）强调了污染物类型、pH值和电流密度对系统降解效率的显著影响，在pH = 3和高电流密度下观察到最佳条件。为了突出环境效益，进行了全面的生命周期评估（LCA）。当在纺织厂实施时，光伏供电的EF系统的能源回收时间（EPBT）为9.53年，温室气体回收时间（GPBT）为4.45年，生命周期成本（LCC）为1.9×10元人民币。与传统芬顿和EF工艺的比较分析表明，可大幅节省能源，碳排放量分别减少95%和78%，能源消耗分别减少87%和52%。

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