Harun Nur Haninah, Zainal Abidin Zurina, Majid Umar Adam, Abdul Hamid Mohamad Rezi, Abdullah Abdul Halim, Othaman Rizafizah, Harun Mohd Yusof
Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia.
Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, Serdang 43400, Malaysia.
Polymers (Basel). 2022 Aug 16;14(16):3325. doi: 10.3390/polym14163325.
This study aimed to optimize the removal of Cu(II) ions from an aqueous solution using a oil bio-based membrane blended with 0.50 wt% graphene oxide (JPU/GO 0.50 wt%) using a central composite model (CCD) design using response surface methodology. The input factors were the feed concentration (60-140) ppm, pressure (1.5-2.5) bar, and solution pH value (3-5). An optimum Cu(II) ions removal of 87% was predicted at 116 ppm feed concentration, 1.5 bar pressure, and pH 3.7, while the validated experimental result recorded 80% Cu(II) ions removal, with 95% of prediction intervals. A statistically non-significant term was removed from the analysis by the backward elimination method to improve the model's accuracy. Using the reduction method, the predicted R value was increased from -0.16 (-16%) to 0.88 (88%), suggesting that the reduced model had a good predictive ability. The quadratic regression model was significant (R = 0.98) for the optimization prediction. Therefore, the results from the reduction model implied acceptable membrane performance, offering a better process optimization for Cu(II) ions removal.
本研究旨在使用响应面方法的中心复合模型（CCD）设计，优化采用含0.50 wt%氧化石墨烯的油基生物膜（JPU/GO 0.50 wt%）从水溶液中去除铜（II）离子的过程。输入因素为进料浓度（60 - 140）ppm、压力（1.5 - 2.5）bar和溶液pH值（3 - 5）。预测在进料浓度116 ppm、压力1.5 bar和pH 3.7时，铜（II）离子的最佳去除率为87%，而经过验证的实验结果显示铜（II）离子去除率为80%，预测区间为95%。通过向后消除法从分析中去除了一个统计上不显著的项，以提高模型的准确性。使用约简方法，预测的R值从 - 0.16（-16%）提高到0.88（88%），这表明约简后的模型具有良好的预测能力。二次回归模型对于优化预测具有显著性（R = 0.98）。因此，约简模型的结果表明膜性能可接受，为铜（II）离子的去除提供了更好的工艺优化。
