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Cluster heatmap of the nine OA runs simultaneously taking in account the response of the three water quality indices.
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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 fro...
Citations
... Moreover, it has been suggested that there is value in aligning DOE techniques-in the Lean Six Sigma methodologies-to artificial intelligence technology in industrial applications [54]. This work expands on several recent attempts to adopt and apply the broad know-how from modern algorithmic engines in order to profile state-of-the-art wastewater processes, counting on small-structured datasets to describe the influence of multiple inputs on multiple outputs [55][56][57]. ...
Increasing wastewater treatment efficiency is a primary aim in the circular economy. Wastewater physicochemical and biochemical processes are quite complex, often requiring a combination of statistical and machine learning tools to empirically model them. Since wastewater treatment plants are large-scale operations, the limited opportunities for extensive experimentation may be offset by miniaturizing experimental schemes through the use of fractional factorial designs (FFDs). A recycling quality improvement study that relies on non-linear multi-objective multi-parameter FFD (NMMFFD) datasets was reanalyzed. A published NMMFFD ultrafiltration screening/optimization case study was re-examined regarding how four controlling factors affected three paper mill recycling characteristic responses using a combination of statistical and machine learning methods. Comparative machine learning screening predictions were provided by (1) quadratic support vector regression and (2) optimizable support vector regression, in contrast to quadratic linear regression. NMMFFD optimization was performed by employing Pareto fronts. Pseudo-screening was applied by decomposing the replicated NMMFFD dataset to single replicates and then testing their replicate repeatability by introducing belief functions that sought to maximize credibility and plausibility estimates. Various versions of belief functions were considered, since the novel role of the three process characteristics, as independent sources, created a high level of conflict during the information fusion phase, due to the inherent divergent belief structures. Correlations between two characteristics, but with opposite goals, may also have contributed to the source conflict. The active effects for the NMMFFD dataset were found to be the transmembrane pressure and the molecular weight cut-off. The modified adjustment was pinpointed to the molecular weight cut-off at 50 kDa, while the optimal transmembrane pressure setting persisted at 2.0 bar. This mixed-methods approach may provide additional confidence in determining improved recycling process adjustments. It would be interesting to implement this approach in polyfactorial wastewater screenings with a greater number of process characteristics.
... The most striking feature is the negation of the necessity for a global objective function to guide the solver procedure. This lessens the possibility of subjectively selecting a route to narrow down the screening/optimization path; it makes the solution more abstract and it definitely differentiates it from other smart aquametric approaches [78][79][80]. ...
Water scarcity is a challenging global risk. Urban wastewater treatment technologies, which utilize processes based on single-stage ultrafiltration (UF) or nanofiltration (NF), have the potential to offer lean-and-green cost-effective solutions. Robustifying the effectiveness of water treatment is a complex multidimensional characteristic problem. In this study, a non-linear Taguchi-type orthogonal-array (OA) sampler is enriched with an emergent stigmergic clustering procedure to conduct the screening/optimization of multiple UF/NF aquametric performance metrics. The stochastic solver employs the Databionic swarm intelligence routine to classify the resulting multi-response dataset. Next, a cluster separation measure, the Davies–Bouldin index, is used to evaluate input and output relationships. The self-organized bionic-classifier data-partition appropriateness is matched for signatures between the emergent stigmergic clustering memberships and the OA factorial vector sequences. To illustrate the proposed methodology, recently-published multi-response multifactorial L9(3⁴) OA-planned experiments from two interesting UF-/NF-membrane processes are examined. In the study, seven UF-membrane process characteristics and six NF-membrane process characteristics are tested (1) in relationship to four controlling factors and (2) to synchronously evaluate individual factorial curvatures. The results are compared with other ordinary clustering methods and their performances are discussed. The unsupervised robust bionic prediction reveals that the permeate flux influences both the UF-/NF-membrane process performances. For the UF process and a three-cluster model, the Davies–Bouldin index was minimized at values of 1.89 and 1.27 for the centroid and medoid centrotypes, respectively. For the NF process and a two-cluster model, the Davies–Bouldin index was minimized for both centrotypes at values close to 0.4, which was fairly close to the self-validation value. The advantage of this proposed data-centric engineering scheme relies on its emergent and self-organized clustering capability, which retraces its appropriateness to the fractional factorial rigid structure and, hence, it may become useful for screening and optimizing small-data wastewater operating conditions.
... The amount of water availability per person naturally decreases with the increasing population [101]. ...
Access to safe drinking water and improved sanitation are important fundamental rights of people around the world to maintain good health. However, freshwater resources are threatened by many anthropogenic activities. Therefore, sustainable water supply is a challenge. Limited access to safe drinking water and unimproved sanitation facilities in some of its urban and rural areas are two of the major challenges for Bhutan in the 21st century. The water quality in the natural water systems in the cities and suburbs has significantly decreased while the urban infrastructure is being improved in Bhutan. Therefore, this study presents the state-of-the-art of water resources in Bhutan and the challenges for a sustainable water supply system. The current water status, drinking water sources and accessibility, factors affecting water quality degradation in urban and rural areas, water treatment methods, and implementation of sustainable drinking water accessibility with population growth in Bhutan are discussed in detail. Results of the review revealed that the water quality has deteriorated over the last decade and has a high challenge to provide safe water to some of the areas in Bhutan. Geographic changes, financial difficulties, urban expansion, and climate change are the reasons for the lack of safe drinking water accessibility for people in town areas. It is, therefore, recommended to have a comprehensive integrate water resources management (IWRM) approach while considering all stakeholders to find sustainable solutions for the challenges showcased in this paper.
... The unreplicated-saturated non-linear multi-response ED dataset of Abu-Shady [16] was analyzed using descriptive and graphical Taguchi methods; a 'one-response-at-a-time' analysis is a rudimentary data reduction phase in aquametrics. There were further attempts to add significance to the original experimental outcomes using: (1) a non-parametric multifactorial technique [59], (2) a combination method of a micro-clustered dimension-reduction with rank learning [60] and (3) a combination method of unsupervised clustering with entropic methods [61]. The proposed work implements a recently published algorithmic classification method, the bionic swarm intelligence (DBSc) method [62], to structured multidimensional mini datasets. ...
... Consequently, the overall factorial screening prediction suggests the predominance of factor A (dilute flow), as it is now clearly construed from Figures 7 and 9. The final outcome agrees with: (1) a non-parametric statistical method prediction [59], (2) a combination of semi-unsupervised (silhouette method)/statistical method with confirmation data [60] and (3) a combination of unsupervised (affinity propagation clustering)/information-entropic methods with confirmation data [61]. Table 7 (function 'LenthPlot' in R-package 'BsMD' (30 April 2020)). ...
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.
