National Institute of Technology, Tiruchirappalli 620015, India.
National Institute of Technology, Tiruchirappalli 620015, India.
Bioresour Technol. 2016 May;207:150-6. doi: 10.1016/j.biortech.2016.01.138. Epub 2016 Feb 13.
Statistical optimization designs were used to optimize the phenol degradation using Chlorella pyrenoidosa. The important factor influencing phenol degradation was identified by two-level Plackett-Burman Design (PBD) with five factors. PBD determined the following three factors as significant for phenol degradation viz. algal concentration, phenol concentration and reaction time. CCD and RSM were applied to optimize the significant factors identified from PBD. The results obtained from CCD indicated that the interaction between the concentration of algae and phenol, phenol concentration and reaction time and algal concentration and reaction time affect the phenol degradation (response) significantly. The predicted results showed that maximum phenol degradation of 97% could be achieved with algal concentration of 4g/L, phenol concentration of 0.8g/L and reaction time of 4days. The predicted values were in agreement with experimental values with coefficient of determination (R(2)) of 0.9973. The model was validated by subsequent experimentations at the optimized conditions.
采用统计优化设计,利用蛋白核小球藻优化苯酚降解。采用双水平 Plackett-Burman 设计(PBD)和五个因素确定影响苯酚降解的重要因素。PBD 确定了以下三个因素对苯酚降解具有重要意义:藻类浓度、苯酚浓度和反应时间。CCD 和 RSM 用于优化 PBD 确定的显著因素。CCD 得到的结果表明,藻类和苯酚浓度之间的相互作用、苯酚浓度和反应时间以及藻类浓度和反应时间对苯酚降解(响应)有显著影响。预测结果表明,在藻类浓度为 4g/L、苯酚浓度为 0.8g/L、反应时间为 4 天时,苯酚降解率最高可达 97%。预测值与实验值吻合,决定系数(R²)为 0.9973。通过在优化条件下进行后续实验对模型进行验证。