Analyzing DOE With Statistical Software Packages: Controversies and Proposals

Databionic Swarm Intelligence to Screen Wastewater Recycling Quality with Factorial and Hyper-Parameter Non-Linear Orthogonal Mini-Datasets

Electrodialysis (ED) may be designed to enhance wastewater recycling efficiency for crop irrigation in areas where water distribution is otherwise inaccessible. ED process controls are difficult to manage because the ED cells need to be custom-built to meet local requirements, and the wastewater influx often has heterogeneous ionic properties. Besides the underlying complex chemical phenomena, recycling screening is a challenge to engineering because the number of experimental trials must be maintained low in order to be timely and cost-effective. A new data-centric approach is presented that screens three water quality indices against four ED-process-controlling factors for a wastewater recycling application in agricultural development. The implemented unsupervised solver must: (1) be fine-tuned for optimal deployment and (2) screen the ED trials for effect potency. The databionic swarm intelligence classifier is employed to cluster the L9(34) OA mini-dataset of: (1) the removed Na+ content, (2) the sodium adsorption ratio (SAR) and (3) the soluble Na+ percentage. From an information viewpoint, the proviso for the factor profiler is that it should be apt to detect strength and curvature effects against not-computable uncertainty. The strength hierarchy was analyzed for the four ED-process-controlling factors: (1) the dilute flow, (2) the cathode flow, (3) the anode flow and (4) the voltage rate. The new approach matches two sequences for similarities, according to: (1) the classified cluster identification string and (2) the pre-defined OA factorial setting string. Internal cluster validity is checked by the Dunn and Davies–Bouldin Indices, after completing a hyper-parameter L8(4122) OA screening. The three selected hyper-parameters (distance measure, structure type and position type) created negligible variability. The dilute flow was found to regulate the overall ED-based separation performance. The results agree with other recent statistical/algorithmic studies through external validation. In conclusion, statistical/algorithmic freeware (R-packages) may be effective in resolving quality multi-indexed screening tasks of intricate non-linear mini-OA-datasets.

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 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.

Micro-Clustering and Rank-Learning Profiling of a Small Water-Quality Multi-Index Dataset to Improve a Recycling Process

The efficiency improvement of wastewater recycling has been prioritized by ‘Goal 6’ of the United Nations Sustainable Development initiative. A methodology is developed to synchronously profile multiple water-quality indices of a wastewater electrodialysis (ED) process. The non-linear multifactorial screener is exclusively synthesized by assembling proper R-based statistical freeware routines. In sync with current trends, the new methodology promotes convenient, open and rapid implementation. The new proposal unites the ‘small-and-fast’ data-sampling features of the fractional multifactorial designs to the downsizing, by microclustering, of the multiple water quality indices—using optimized silhouette-based classification. The non-linear multifactorial profiling process is catalyzed by the ‘ordinalization’ of the regular nominal nature of the resulting optimum clusters. A bump chart screening virtually eliminates weak performances. A follow-up application of the ordinal regression succeeds in assigning statistical significance to the resultant factorial potency. The rank-learning aptitude of the new profiler is tested and confirmed on recently published wastewater ED-datasets. The small ED-datasets attest to the usefulness to convert limited data in real world applications, wherever there is a necessity to improve the quality status of water for agricultural irrigation in arid areas. The predictions have been compared with other techniques and found to be agreeable.