Wastewater Quality Screening Using Affinity Propagation Clustering and Entropic Methods for Small Saturated Nonlinear Orthogonal Datasets

Wastewater recycling efficiency improvement is vital to arid regions, where crop irrigation imperative. Analyzing small, unreplicated–saturated, multiresponse, multifactorial datasets from novel wastewater electrodialysis (ED) applications requires specialized screening/optimization techniques. A new approach proposed glean information structured Taguchi-type sampling schemes (nonlinear fractional factorial designs) in the case that direct uncertainty quantification not computable. It uses a double analysis–affinity propagation clustering and entropy simultaneously discern strong effects curvature type while profiling multiple water-quality characteristics. Three water quality indices, which are calculated real ED process experiments, analyzed by examining hierarchical behavior of four controlling factors: (1) dilute flow, (2) cathode (3) anode (4) voltage rate. The three indices are: removed sodium content, adsorption ratio, soluble percentage. factor influences overall separation performance according both analyses’ versions. caused maximum contrast difference heatmap visualization, it minimized relative at two operating end points. results confirmed with second published independent dataset. Furthermore, final outcome scrutinized found agree other classification nonparametric screening solutions. combination modern simple entropic methods offered through freeware R-packages might be effective for testing high-complexity ‘small-and-dense’ nonlinear OA datasets, highlighting an obfuscated experimental uncertainty.