Department of Civil Engineering, National Institute of Technology, Kurukshetra, India.
Int J Phytoremediation. 2020;22(11):1097-1109. doi: 10.1080/15226514.2020.1731729. Epub 2020 Feb 27.
The objective of this study was to investigate the reduction of phosphorus from rice mill wastewater by using free floating aquatic plants. Four free floating aquatic plants were used for this study, namely water hyacinth, water lettuce, salvinia, and duckweed. The aquatic plants reduced the total phosphorus (TP) content up to 80% and chemical oxygen demand (COD) up to 75% within 15 days. The maximum efficiency of TP and COD reduction was observed with water lettuce followed by water hyacinth, duckweed, and salvinia. The study also aims to predict phosphorus removal by three modeling techniques, for example, linear regression (LR), artificial neural network (ANN), and M5P. Prediction has been done considering hydraulic retention time (HRT), hydraulic loading rate (HLR), and initial concentration of phosphorus () as input variables whereas the reduction rate of TP () has been considered as a predicted variable. ANN shows promising results as compared to M5P tree and LR modeling. The model accuracy is analyzed using three statistical evaluation parameters which are coefficient of determination (), root mean square error (RMSE), and means absolute error (MAE).
本研究旨在探讨利用自由漂浮水生植物从米厂废水中除磷。本研究使用了四种自由漂浮水生植物，分别是凤眼蓝、空心菜、满江红和浮萍。这些水生植物在 15 天内将总磷（TP）含量降低了 80%，化学需氧量（COD）降低了 75%。在 TP 和 COD 的去除效率方面，空心菜最高，其次是凤眼蓝、浮萍和满江红。本研究还旨在通过三种建模技术（例如线性回归（LR）、人工神经网络（ANN）和 M5P）来预测磷的去除率。预测时将水力停留时间（HRT）、水力负荷率（HLR）和初始磷浓度（）作为输入变量，而 TP 的去除率（）则作为预测变量。与 M5P 树和 LR 模型相比，ANN 显示出有希望的结果。通过三个统计评估参数（决定系数（）、均方根误差（RMSE）和平均绝对误差（MAE））来分析模型的准确性。
