Knowledge Resource Center for Ecological Environment in Arid Area
| DOI | 10.3390/w14081238 |
| Wastewater Quality Screening Using Affinity Propagation Clustering and Entropic Methods for Small Saturated Nonlinear Orthogonal Datasets | |
| Besseris, George | |
| 通讯作者 | Besseris, G |
| 来源期刊 | WATER
 |
| EISSN | 2073-4441 |
| 出版年 | 2022 |
| 卷号 | 14期号:8 |
| 英文摘要 | Wastewater recycling efficiency improvement is vital to arid regions, where crop irrigation is imperative. Analyzing small, unreplicated-saturated, multiresponse, multifactorial datasets from novel wastewater electrodialysis (ED) applications requires specialized screening/optimization techniques. A new approach is proposed to glean information from structured Taguchi-type sampling schemes (nonlinear fractional factorial designs) in the case that direct uncertainty quantification is not computable. It uses a double information analysis-affinity propagation clustering and entropy to simultaneously discern strong effects and curvature type while profiling multiple water-quality characteristics. Three water quality indices, which are calculated from real ED process experiments, are analyzed by examining the hierarchical behavior of four controlling factors: (1) the dilute flow, (2) the cathode flow, (3) the anode flow, and (4) the voltage rate. The three water quality indices are: the removed sodium content, the sodium adsorption ratio, and the soluble sodium percentage. The factor that influences the overall wastewater separation ED performance is the dilute flow, according to both analyses' versions. It caused the maximum contrast difference in the heatmap visualization, and it minimized the relative information entropy at the two operating end points. The results are confirmed with a second published independent dataset. Furthermore, the final outcome is scrutinized and found to agree with other published classification and nonparametric screening solutions. A combination of modern classification and simple entropic methods which are offered through freeware R-packages might be effective for testing high-complexity 'small-and-dense' nonlinear OA datasets, highlighting an obfuscated experimental uncertainty. |
| 英文关键词 | nonlinear factorial screening wastewater recycling water quality index electrodialysis affinity propagation clustering surprise entropy heatmaps |
| 类型 | Article |
| 语种 | 英语 |
| 开放获取类型 | gold |
| 收录类别 | SCI-E |
| WOS记录号 | WOS:000785215800001 |
| WOS关键词 | DESIGN ; OPTIMIZATION ; GREEN |
| WOS类目 | Environmental Sciences ; Water Resources |
| WOS研究方向 | Environmental Sciences & Ecology ; Water Resources |
| 资源类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/394779 |
| 推荐引用方式 GB/T 7714 | Besseris, George. Wastewater Quality Screening Using Affinity Propagation Clustering and Entropic Methods for Small Saturated Nonlinear Orthogonal Datasets[J],2022,14(8). |
| APA | Besseris, George.(2022).Wastewater Quality Screening Using Affinity Propagation Clustering and Entropic Methods for Small Saturated Nonlinear Orthogonal Datasets.WATER,14(8). |
| MLA | Besseris, George."Wastewater Quality Screening Using Affinity Propagation Clustering and Entropic Methods for Small Saturated Nonlinear Orthogonal Datasets".WATER 14.8(2022). |
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