Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran.
Bioresour Technol. 2010 Apr;101(7):2252-8. doi: 10.1016/j.biortech.2009.11.079. Epub 2009 Dec 21.
The potential of a macroalgae Chara sp. was investigated as a viable biomaterial for biological treatment of Malachite Green (MG) solution. The effects of operational parameters such as temperature, pH, initial dye concentration, reaction time and amount of algae on biological decolorization efficiency were studied. Biological treatment of MG solution by live and dead algae was compared. The reusability and efficiency of the live algae in long-term repetitive operations were also examined. The batch experiments results revealed the ability of algal species in biological degradation of the dye. The biological degradation compounds formed in this process were analyzed by UV-Vis, FT-IR and GC-Mass techniques. The degradation pathway of MG was proposed based on the identified compounds. In addition, an artificial neural network model was developed to predict the biological degradation efficiency. The findings indicated that ANN provides reasonable predictive performance (R(2)=0.970). The influence of each parameter on the variable studied was assessed, reaction time being the most significant factor, followed by temperature of the solution.
研究了一种大型藻类 Chara sp. 作为生物处理孔雀石绿 (MG) 溶液的可行生物材料的潜力。研究了操作参数，如温度、pH 值、初始染料浓度、反应时间和藻类量对生物脱色效率的影响。比较了活藻和死藻对 MG 溶液的生物处理效果。还研究了活藻在长期重复操作中的可重复使用性和效率。分批实验结果表明，藻类物种具有生物降解染料的能力。通过 UV-Vis、FT-IR 和 GC-Mass 技术分析了在此过程中形成的生物降解化合物。根据鉴定的化合物提出了 MG 的降解途径。此外，还开发了人工神经网络模型来预测生物降解效率。研究结果表明，ANN 提供了合理的预测性能 (R(2)=0.970)。评估了每个参数对研究变量的影响，反应时间是最重要的因素，其次是溶液的温度。
