Optimisation of a purification method for metal-containing wastewater by use of a Taguchi experimental design

Barrado, E.; Vega, Marisol; Pardo, Rafael; Grande, Philipp M.; Valle, J. L. del

doi:10.1016/0043-1354(96)00119-4

Cited by 51 publications

(30 citation statements)

References 13 publications

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“…We select the particular published datasets on grounds of being a pioneering research in a field of escalating challenge; the instructing of concurrent screening and optimization for water purification from toxic metals in decontaminating wastewater. Managing the information generation in two otherwise sequential dataprocessing phases -screening and optimization -in parallel, as in the study by Barrado et al [31], also adds to the perplexity of the new developments. The essential reason for revisiting that specific case study becomes the transparency of the replicated data and the deployment of a non-linear OA at the saturation limit.…”

Section: A Need For a New Nature-inspired For Translating Inner/outermentioning

confidence: 99%

“…In a nutshell, the aforementioned article attempted an 'all-inclusive' optimization task with a broad spectrum outlook that even today is rather rare to access in current publications. Nevertheless, we will ponder over several assumptions that may not hold true in executing a typical multifactorial treatment using analysis of variance (ANOVA) as exemplified in the paper of Barrado et al [31]. Thus, this necessity commands a nature-inspired soft-computing alternative.…”

Section: A Need For a New Nature-inspired For Translating Inner/outermentioning

confidence: 99%

“…The new proposition will entail the dual arrangementinner/outer OA layout -as it has been recommended by the original Taguchi-DOX for probing simultaneously controlling factors against perturbing noise variables. To reinforce the credibility of our approach we will borrow for testing the experimental datasets of Barrado et al [31]. We select the particular published datasets on grounds of being a pioneering research in a field of escalating challenge; the instructing of concurrent screening and optimization for water purification from toxic metals in decontaminating wastewater.…”

Section: A Need For a New Nature-inspired For Translating Inner/outermentioning

confidence: 99%

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Screening dense and noisy DOX-datasets with NN-blending and “dizzy” swarm intelligence: Profiling a water quality process

Besseris

2017

Swarm and Evolutionary Computation

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A novel nature-inspired method is presented in this work for resolving product/process development or improvement with design of experiments (DOX). The technique is suitable for difficult Taguchi-type multifactorial screening and optimization studies that need to simultaneously contain the double hassle of controllable and uncontrollable noise intrusions. The three-part sequential processing routine requires: 1) a regressive data-compression preprocessing, 2) a smart-sample generation using general-regression neural networks (GRNN), and 3) a screening power prediction using 'reverse' swarm intelligence (SI). The approach is primed to confront potential non-linearity and data messiness in the examined effects. The Taguchi-type orthogonal-array (OA) sampler is tuned for retrieving information in controllable (outer OA) and uncontrollable (inner OA) noises. The OA-saturation condition is elicited for maximum data exploitation. GRNN-fuzzification consolidates into a single contribution the uncertainty from all possible sources. The resulting 'smart' sample is defuzzified by a robust-and-agile data reduction. Screeningsolution meta-power is controlled with a new SI-variant. The independent swarm groups, as many as the studied effects, are tracked toward preassigned targets, i.e. their ability to return to their host beehives. The technique is illustrated on a complex purification process where published multifactorial data had been collected for a critical wastewater paradigm and thus may be used to test the benchmark solution. However, environmental water-qualimetrics are profoundly dominated by messy data as justified in this work. We elucidate on several issues that regular Taguchi methods may be benefited by the proposed GRNN/SI processing while emphasizing the consequence of overlooking the underlying assumptions that govern standard comparison models. The new swarm itelligence method offered a practical way to estimate a first-time "soft" power measure for the inner/outer OA optimization case that was impossible with ordinary statistical multi-factorrial treatments. Key performance advantages in efficiency, robustness and convenience are highlighted against alternative approaches.

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Section: A Need For a New Nature-inspired For Translating Inner/outermentioning

confidence: 99%

Section: A Need For a New Nature-inspired For Translating Inner/outermentioning

confidence: 99%

Section: A Need For a New Nature-inspired For Translating Inner/outermentioning

confidence: 99%

See 1 more Smart Citation

Screening dense and noisy DOX-datasets with NN-blending and “dizzy” swarm intelligence: Profiling a water quality process

Besseris

2017

Swarm and Evolutionary Computation

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“…Barrado et al [32] expanded above-mentioned four phases into the following steps for implementing Taguchi experimental design:…”

Section: Finally In the Fourth Phase The Confirmation Test Is Run Witmentioning

confidence: 99%

Application of Taguchi-Based Design of Experiments for Industrial Chemical Processes

Davis¹,

John²

2018

Statistical Approaches With Emphasis on Design of Experiments Applied to Chemical Processes

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Design of experiment is the method, which is used at a very large scale to study the experimentations of industrial processes. It is a statically approach where we develop the mathematical models through experimental trial runs to predict the possible output on the basis of the given input data or parameters. The aim of this chapter is to stimulate the engineering community to apply Taguchi technique to experimentation, the design of experiments, and to tackle quality problems in industrial chemical processes that they deal with. Based on years of research and applications, Dr. G. Taguchi has standardized the methods for each of these DOE application steps. Thus, DOE using Taguchi approach has become a much more attractive tool to practicing engineers and scientists.And since the last four decades, there were limitations when conventional experimental design techniques were applied to industrial experimentation. And Taguchi, also known as orthogonal array design, adds a new dimension to conventional experimental design. Taguchi method is a broadly accepted method of DOE, which has proven in producing high-quality products at subsequently low cost.

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“…The usual steps to apply Taguchi experimental design [13] are: (a) to select the output variable(s) (response(s)) to be optimized; (b) to identify the factors (input variables) affecting output variable(s) and to choose the levels of these factors; (c) to select the appropriate Orthogonal Array; the arrays are found in literature [14]; (d) to assign factors and interactions to the columns of the array; (e) to perform experiments; at this step it is important to randomize the trials in order to minimize the systematic error; (f) to analyze the results using signal-to-noise ratio (S/N) analysis and analysis of variance (ANOVA); (g) to determine the optimal process parameters; (h) to perform confirmatory experiments, if it is necessary.…”

Section: • Orthogonal Arraysmentioning

confidence: 99%

Robust Design and Taguchi Method Application

Souza¹,

Silva²,

Moysés³

et al. 2013

Design of Experiments - Applications

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Optimisation of a purification method for metal-containing wastewater by use of a Taguchi experimental design

Cited by 51 publications

References 13 publications

Screening dense and noisy DOX-datasets with NN-blending and “dizzy” swarm intelligence: Profiling a water quality process

Screening dense and noisy DOX-datasets with NN-blending and “dizzy” swarm intelligence: Profiling a water quality process

Application of Taguchi-Based Design of Experiments for Industrial Chemical Processes

Robust Design and Taguchi Method Application

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