Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA.
Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA.
Sci Total Environ. 2021 Jun 1;771:145423. doi: 10.1016/j.scitotenv.2021.145423. Epub 2021 Jan 26.
Filter media have oftentimes been used in fixed-bed column tests to examine their removal efficiencies for various pollutants, such as nutrients in stormwater runoff. With limited data sets from column studies, a response surface method (RSM), such as the Box-Behnken Design (BBD), and machine learning methods, can be used to transition from discrete mode assessment to continuous mode optimization, from which the key ingredients of filter media can be better synergized. In this study, similarly to drug discovery via chemometrics, RSM is used to generate meta-models and identify the optimum ratio between clay and iron-filings contents in Iron-filings-based Green Environmental Media (IFGEM) for nutrient removal in stormwater treatment. To achieve the continuous mode optimization, artificial neural network (ANN), deep belief network (DBN), and extreme learning machine (ELM) were selected as machine learning models to compare with BBD to explore the limited column data sets and improve the data science. While separate RSM can help realize the removal efficiencies of total nitrogen (TN), total phosphorus (TP), and ammonia based on varying ratios of clay and iron-filings contents in IFGEM, heterogeneous and inconsistent response surfaces generated from the four learners or classifiers (ANN, ELM, DBN, and BBD) complicate the selection of the final optimal recipe. The power of higher order singular value decomposition (HOSVD) helps synergize the optimal clay and iron filings matrixes of IFGEM in the context of continuous mode optimization via ANN, ELM, DBN, and BBD. With the aid of HOSVD, the optimal recipe for a holistic nutrient removal of TN, TP, and ammonia was determined to be 5% clay, 10% iron filings, 10% tire crumb, and 75% sand.
过滤介质常用于固定床柱试验中，以研究其对各种污染物的去除效率，如雨水径流中的养分。由于柱研究的数据集有限，可以使用响应面方法（RSM），如 Box-Behnken 设计（BBD）和机器学习方法，从离散模式评估过渡到连续模式优化，从而更好地协同过滤介质的关键成分。在这项研究中，类似于通过化学计量学发现药物，RSM 用于生成元模型，并确定基于铁粉的绿色环保介质（IFGEM）中粘土和铁粉含量的最佳比例，以实现雨水处理中的养分去除。为了实现连续模式优化，人工神经网络（ANN）、深度置信网络（DBN）和极限学习机（ELM）被选为机器学习模型，与 BBD 进行比较，以探索有限的柱数据集并提高数据科学水平。虽然单独的 RSM 可以帮助实现基于 IFGEM 中粘土和铁粉含量变化比例的总氮（TN）、总磷（TP）和氨的去除效率，但来自四个学习者或分类器（ANN、ELM、DBN 和 BBD）的异质和不一致的响应面使最终最佳配方的选择变得复杂。高阶奇异值分解（HOSVD）的强大功能有助于通过 ANN、ELM、DBN 和 BBD 在连续模式优化的背景下协同优化 IFGEM 的最佳粘土和铁粉矩阵。在 HOSVD 的帮助下，确定 TN、TP 和氨的整体养分去除的最佳配方为 5%粘土、10%铁粉、10%轮胎碎屑和 75%沙子。
