Department of Civil Engineering, National Institute of Technology, Kurukshetra, India.
Department of Civil Engineering, Punjab Technical University, Jalandhar, India.
Water Environ Res. 2023 Oct;95(10):e10932. doi: 10.1002/wer.10932.
Nitrogen pollution in water bodies has become a pressing environmental and public health issue worldwide, demanding the implementation of effective nitrogen removal strategies. This research paper delves into the performance evaluation of hybrid constructed wetlands (HCWs) as a sustainable and innovative approach for nitrogen removal, employing a comprehensive year-long dataset gathered from a practical setup. The study collected data under diverse operating conditions to investigate the effectiveness of HCWs in removing nitrogen. Results revealed that HCWs achieved nitrogen removal efficiencies ranging from 28% to 65%, influenced by temperature and hydraulic retention time. Optimal removal occurred at an average temperature of 28°C and a 4-day hydraulic retention time. Notably, performance declined during colder periods, with temperatures below 15°C. The study also aims to predict nitrogen removal by three modeling techniques, that is, artificial neural networks (ANNs), support vector machines Pearson VII kernel function (SVM PUK), and multiple linear regression (MLR). Prediction has been done considering temperature (TEMP), hydraulic loading rate (HLR), initial concentration of chemical oxygen demand (COD) (CODin), initial concentration of total nitrogen (TN ), initial concentration of total phosphorous (TP ), and initial concentration of turbidity (TB ) as input parameters, whereas reduction of total nitrogen (RED TN) is regarded as output parameter. The performance of the soft computing techniques has been compared in terms of coefficient of determination (R ), root mean square error (RMSE), and mean absolute error (MAE). The analysis revealed that the performance of the SVM (PUK) model (R : 0.572, RMSE: 0.0359, MAE: 0.0294) for the prediction of TN reduction is superior followed by MLR (R : 0.562, RMSE: 0.0365, MAE: 0.0294) and ANN (R : 0.597, RMSE: 0.0377, MAE: 0.0301). The present study concludes that the treated effluent by the HCWs, using water hyacinth and water lettuce, is of fair quality, thus having potential application for the treatment of rice mill wastewater in warmer climates. Further, machine learning approaches employed in estimating the total nitrogen reduction by HCWs technology have shown promising applicability and utilization in such studies. PRACTITIONER POINTS: Hybrid constructed wetlands (HCWs) are effective in removing nitrogen from wastewater. The performance of HCWs in nitrogen removal can vary due to physical, chemical, and biological processes. The performance of the HCWs highly depends on temperature and hydraulic retention time. Artificial neural networks (ANNs) and support vector machines (SVMs) provided better predictions of nitrogen removal with high accuracy and low root mean square error.
水体中的氮污染已成为全球范围内紧迫的环境和公共卫生问题，需要实施有效的氮去除策略。本研究论文深入探讨了混合人工湿地 (HCWs) 作为一种可持续和创新的氮去除方法的性能评估，该方法使用从实际设置中收集的全面长达一年的数据集。该研究在不同的运行条件下收集数据，以调查 HCWs 去除氮的效果。结果表明，氮去除效率在 28%至 65%之间，受温度和水力停留时间的影响。在平均温度为 28°C 和水力停留时间为 4 天时，去除效果最佳。值得注意的是，在温度低于 15°C 时，性能会下降。本研究还旨在通过三种建模技术，即人工神经网络 (ANNs)、支持向量机 Pearson VII 核函数 (SVM PUK) 和多元线性回归 (MLR)，对氮去除进行预测。考虑到温度 (TEMP)、水力负荷率 (HLR)、化学需氧量 (COD) 的初始浓度 (CODin)、总氮 (TN) 的初始浓度、总磷 (TP) 的初始浓度和浊度 (TB) 的初始浓度作为输入参数，而总氮的减少 (RED TN) 被视为输出参数。比较了软计算技术的性能，以确定系数 (R)、均方根误差 (RMSE) 和平均绝对误差 (MAE)。分析表明，SVM (PUK) 模型 (R：0.572，RMSE：0.0359，MAE：0.0294) 对 TN 减少的预测性能较好，其次是 MLR (R：0.562，RMSE：0.0365，MAE：0.0294) 和 ANN (R：0.597，RMSE：0.0377，MAE：0.0301)。本研究得出结论，水葫芦和水芹菜处理后的混合人工湿地 (HCWs) 出水水质良好，因此在温暖气候下具有处理米厂废水的潜力。此外，用于估计 HCWs 技术去除总氮的机器学习方法在这些研究中表现出了很好的适用性和实用性。
以上是一段未经编辑的译文，可能存在不通顺或不地道的表达，仅供参考。
