环境科学与技术

规整化铁炭填料微电解深度处理制浆废水

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  • 华南理工大学 制浆造纸工程国家重点实验室,广东 广州 510640
莫立焕( 1970-) ,男,高级工程师,博士,主要从事制浆清洁生产与污染控制研究

收稿日期: 2018-01-22

  修回日期: 2018-03-26

  网络出版日期: 2018-05-07

基金资助

国家水体污染控制与治理科技重大专项项目( 2014ZX07213001) ; 广州应用型科技研发专项资金项目 ( 201707020011) ;制浆造纸工程国家重点实验室开放基金资助项目( 201831) 

Preparation and Characterization of Starch Functional Film
 

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  •  1. School of Food Science and Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China;
    2. Sino-Singapore International Joint Research Institute,Guangzhou Knowledge City,Guangzhou 510640,Guangdong,China
莫立焕( 1970-) ,男,高级工程师,博士,主要从事制浆清洁生产与污染控制研究

Received date: 2018-01-22

  Revised date: 2018-03-26

  Online published: 2018-05-07

Supported by

Supported by the National Water Pollution Control and Treatment Science and Technology Major Project ( 2014ZX07213001) , and the State Key Laboratory of Pulp and Paper Engineering( 201831) 

摘要

以铁粉、活性炭和粘土为主要组分,制得新型规整化球型铁炭填料. 采用响应面 法通过建立出水 CODCr与各因素之间的 Box-Behnken 数学模型,对新型规整化球型铁炭 填料处理制浆废水工艺进行优化,并采用 UV、 FTIR、 GC-MS 分别对在最优条件下微电解 处理的蔗渣制浆废水进行表征分析. 结果表明: 在最佳实验条件( 初始 pH =2. 37、填料添 加量为32. 67g/L、反应时间为 92. 49 min、曝气流量为 0. 3 m3 /h) 下,CODCr去除率可达 69. 23%,新型规整化球型铁碳填料对废水中的共轭双键、羰基具有较好的降解效果,对芳 香族类有机物具有良好的去除和转化能力. 

本文引用格式

莫立焕 杨爽 谈金强 李军 . 规整化铁炭填料微电解深度处理制浆废水[J]. 华南理工大学学报(自然科学版), 2018 , 46(6) : 130 -136 . DOI: 10.3969/j.issn.1000-565X.2018.06.018

Abstract

Starch-based films incorporated with different concentration of tea polyphenols ( TP) were developed
with the aid of solution casting method. The physical and functional properties of the films were systematically cha
racterized. The results shows that the TP produces no significant effect on the surface and moisture content of the films,but it reduces the transparency and contact angle. It is also noted that, the mechanical properties and water vapor permeability of the film show no linear changes with the increase of the content of TP. In addition, the presence of TP increases the antioxidant and antimicrobial activity of the starch-based films,and the inhibitory effect is more evident against Staphylococcus aureus. In the antibacterial test,a new automatic counting method based on computer vision and machine learning algorithm is established to count the colonies,which perform much faster without subjective uncertainty. 

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