Application of General Full Factorial Statistical Experimental Design’s Approach for the Development of Sustainable Clay-Based Ceramics Incorporated with Malaysia’s Electric Arc Furnace Steel Slag Waste

Teo, Pao Ter; Zakaria, Shahsuzan; Sharif, Nurulakmal Mohd; Seman, Anasyida Abu; Taib, Mustaffa Ali Azhar; Mohamed, Julie Juliewatty; Yusoff, Mahani; Yusoff, Abdul Hafidz; Mohamad, Mardawani; Ali, Arlina; Masri, Mohamad Najmi

doi:10.3390/cryst11040442

Cited by 14 publications

(10 citation statements)

References 85 publications

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“…C1, C2 and C3-based clays are found in this cluster. Samples in this cluster show the lowest values of linear firing shrinkage (negatives) and low values of weight loss, with high values of density, due to the glassy phase formed by melted slag [27]. Ba, Cr and Mo release exhibit the highest values in this cluster.…”

Section: Laboratory Pressed Clay Mixtures With Ws-fsd Incorporationmentioning

confidence: 91%

“…Waste additions and processing conditions effect on the physico-chemical, technological and environmental properties of the final product have been extensively studied using classical multivariate statistical techniques [19][20][21][22][23][24][25][26][27]. Otherwise, Artificial Neural Networks (ANN) and Self-Organizing Maps (SOMs) have thus far been used in a more limited way as an alternative to assess the abovementioned relationships.…”

Section: Introductionmentioning

confidence: 99%

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Self-Organizing Maps to Assess the Recycling of Waste in Ceramic Construction Materials

et al. 2021

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Circular economy promotes the use of waste materials into new production processes as a key factor for resource efficiency. The construction sector, and specifically the fired clay industry, is able to assimilate large amounts of waste in their processes, without significantly altering the technical properties of products. The introduction of different waste in ceramic products at the laboratory level has been extensively studied in the literature, but most of these studies have not yet been scaled-up to industrial production. Differences in processing with respect to laboratory conditions introduces uncertainty in relation to the expected properties of the final products. This paper uses a Self-Organizing Map (SOM)-based methodology for analysing and assessing the incorporation of industrial waste, Waelz slag (WS) and foundry sand dust (FSD), in ceramic products obtained sequentially at laboratory, semi-industrial and industrial level, over technological properties and metals release. As a result, from the SOM analysis, a clustered map of the samples developed is obtained that highlights the most important parameters affecting the technological and environmental properties to be the type of clay and therefore, the firing temperature; secondly, the Waelz slag content, being independent of the foundry sand addition; and finally, the type and level of processing (laboratory-pressing, semi-industrial extruder, industrial extruder).

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Section: Laboratory Pressed Clay Mixtures With Ws-fsd Incorporationmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Self-Organizing Maps to Assess the Recycling of Waste in Ceramic Construction Materials

et al. 2021

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“…The adequacy checking verifies three important assumptions of residuals, (i) the normality assumption of the residuals, (ii) the constant variance of the residuals, and (iii) the independent assumption of the residuals. The adequacy of these assumptions is managed by comparing the residual values resulting from the difference between the experimental data and the data generated from the regression model or predicted response values [30]. These assumptions could be validated via several statistical residual plots.…”

Section: Model Adequacy Checkingmentioning

confidence: 99%

“…Various methods and approaches have been used to design or simulate a representative model for different aspects of ceramic products, especially for porcelain tiles. These are the sintering process by thermokinetics simulation [19,20], numerical/mathematical approaches [21][22][23][24][25][26][27], finite element analysis [28,29], statistical approaches [12,30], Computational Fluid Dynamics (CFD) Simulation [31], and data-driven approaches or artificial neural network (ANN) modeling [32][33][34][35][36] to name a few. Most of the simulation/modeling approaches to determine the relationship between raw materials and final properties consider the typical raw material and/or mineralogical ratios of body formulation and neglect the effect of chemical (oxide) content.…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of porcelain tiles’ technical properties: full factorial design investigation on oxide ratios and temperature

Zamani,

Yapicioglu,

Kara

et al. 2023

Phys. Scr.

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This study focuses on optimizing the composition and firing temperature of porcelain tiles using statistical analysis techniques. A full factorial design, including model adequacy checking, analysis of variance, Pareto charts, interaction plots, regression model, and response optimizer is employed. The key factors were the Seger ratios of SiO2/Al2O3, Na2O/K2O, MgO/CaO, and firing temperature. The response variables investigated were bulk density, water absorption, linear shrinkage, coefficient of thermal expansion (at 500 °C), and strength. The statistical analysis revealed highly significant results, which were further validated, confirming their reliability for practical use in the production of porcelain tiles. The study demonstrated the effectiveness of utilizing Seger formulas and properties of typical raw materials to accurately predict the final properties of ceramic tiles. By employing SiO2/Al2O3 = 5.2, Na2O/K2O = 1.50, MgO/CaO = 3.0, and firing temperature of 1180 °C, optimized properties, such as maximum strength, maximum bulk density, and minimum water absorption, was achieved with a composite desirability of 0.9821.

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“…Multilevel Factorial Design was applied to investigate the main effects and interaction of factors at a different number of levels [32]. Three key factors were chosen, bacterial cell count, urea concentration and calcium chloride concentration, which can be controlled on larger scales.…”

Section: Multilevel Factorial Designmentioning

confidence: 99%

Factors Affecting Calcite Bio-precipitation via Urea Hydrolyzing Bacterial Consortium

Abdelgalil

2022

Advances in Environmental and Life Sciences

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Microbially induced calcite precipitation (MICP) refers to the biomineralization process involving the synthesizing of calcium carbonate by microorganisms. The production of urease by urea-hydrolyzing bacteria, which results in carbonate precipitation, is affected by environmental factors including calcium concentration, bacterial concentration, pH and temperature. This study aims to investigate some factors affecting calcite bio-precipitation via the urea-hydrolyzing consortium. A consortium of three ureasepositive, calcite-precipitating strains of Bacillus was selected. XRD analysis of the precipitant confirmed the polymorph type. The studied factors included bacterial cell count, pH, temperature, urea concentration, and calcium concentration. In order to identify the optimum range of each studied factor, the dry weight of the precipitated calcite was measured for all investigated ranges of the studied factors after two weeks of incubation gravimetrically. A multi-level factorial experiment was designed to investigate the impact of interaction between different levels of factors using the statistical software package MINITAB 17. Three main factors were selected, bacterial cell count, urea and calcium concentration. The fourth run, among twelve runs of a multi-level designed experiment, which had the following concentrations (0.3 M urea, 15 g/l CaCl 2 .H 2 O, and low bacterial inoculum (10 5 CFU/50ml)), precipitated 0.43 g of calcite, while the rest of the runs precipitated lower dry weights. It was found that there was no significant increase in the precipitated calcite weight for the performed runs. According to the analysis of factorial design results, the effect of urea concentration as a factor on precipitation rate in the current study was found to be more effective than other studied factors.

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Application of General Full Factorial Statistical Experimental Design’s Approach for the Development of Sustainable Clay-Based Ceramics Incorporated with Malaysia’s Electric Arc Furnace Steel Slag Waste

Cited by 14 publications

References 85 publications

Self-Organizing Maps to Assess the Recycling of Waste in Ceramic Construction Materials

Self-Organizing Maps to Assess the Recycling of Waste in Ceramic Construction Materials

Statistical analysis of porcelain tiles’ technical properties: full factorial design investigation on oxide ratios and temperature

Factors Affecting Calcite Bio-precipitation via Urea Hydrolyzing Bacterial Consortium

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