Department of Land Management, Faculty of Agriculture, University Putra Malaysia, Serdang 43400, Selangor, Malaysia.
Department of Agricultural Technology, Faculty of Agriculture, University Putra Malaysia, Serdang 43400, Selangor, Malaysia.
Biomed Res Int. 2020 Feb 18;2020:2734135. doi: 10.1155/2020/2734135. eCollection 2020.
The release of wastewater from textile dyeing industrial sectors is a huge concern with regard to pollution as the treatment of these waters is truly a challenging process. Hence, this study investigates the triazo bond Direct Blue 71 (DB71) dye decolorization and degradation dye by a mixed bacterial culture in the deficiency source of carbon and nitrogen. The metagenomics analysis found that the microbial community consists of a major bacterial group of (30%), (11%), (10%), (10%), (8%), Porphyromonadaceae (6%), and Enterobacteriaceae (4%). The richest phylum includes Proteobacteria (78.61%), followed by Bacteroidetes (14.48%) and Firmicutes (3.08%). The decolorization process optimization was effectively done by using response surface methodology (RSM) and artificial neural network (ANN). The experimental variables of dye concentration, yeast extract, and pH show a significant effect on DB71 dye decolorization percentage. Over a comparative scale, the ANN model has higher prediction and accuracy in the fitness compared to the RSM model proven by approximated and AAD values. The results acquired signify an efficient decolorization of DB71 dye by a mixed bacterial culture.
纺织印染工业部门排放的废水是一个令人关注的污染问题，因为这些水的处理确实是一个具有挑战性的过程。因此，本研究调查了在碳氮源缺乏的情况下，混合细菌培养物对三氮键直接蓝 71（DB71）染料的脱色和降解作用。宏基因组分析发现，微生物群落主要由细菌群组成，其中（30%）、（11%）、（10%）、（10%）、（8%）、拟杆菌科（6%）和肠杆菌科（4%）。最丰富的门包括变形菌门（78.61%），其次是拟杆菌门（14.48%）和厚壁菌门（3.08%）。通过响应面法（RSM）和人工神经网络（ANN）有效地进行了脱色过程的优化。染料浓度、酵母提取物和 pH 的实验变量对 DB71 染料的脱色率有显著影响。在比较的范围内，ANN 模型在拟合度和 AAD 值方面比 RSM 模型具有更高的预测和准确性。结果表明，混合细菌培养物对 DB71 染料具有高效的脱色作用。
