Meta-Model Development

Ait-Amir, Bouzid; Pougnet, Philippe; Hami, Abdelkhalak El

doi:10.1016/b978-1-78548-014-0.50006-2

Cited by 59 publications

(36 citation statements)

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“…Center points indicate the middle level of all factors to be studied and provide an error estimation of experiments, model adequacy checking, and curvature detection in the fitted data [ 41 ]. Axial points construct new extreme levels, i.e., low and high for each factor, and provide experimental error assessment [ 42 , 43 ]. In the present study, the CCD comprises of N experimental runs, i.e., N = 2 k + 2k + n, where k is the number of continuous numerical factors, 2 k is the number of the factorial points at the corners of the cube (2 2 ), 2k is the number of the axial points of each numerical factor on the axis at a distance of ±α from the center of the cube (2 × 2), and n is the number of center points (n = 5).…”

Section: Methodsmentioning

confidence: 99%

Multi-Response Optimization of Tensile Creep Behavior of PLA 3D Printed Parts Using Categorical Response Surface Methodology

et al. 2020

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Three-dimensional printed plastic products developed through fused deposition modeling (FDM) endure long-term loading in most of the applications. The tensile creep behavior of such products is one of the imperative benchmarks to ensure dimensional stability under cyclic and dynamic loads. This research dealt with the optimization of the tensile creep behavior of 3D printed parts produced through fused deposition modeling (FDM) using polylactic acid (PLA) material. The geometry of creep test specimens follows the American Society for Testing and Materials (ASTM D2990) standards. Three-dimensional printing is performed on an open-source MakerBot desktop 3D printer. The Response Surface Methodology (RSM) is employed to predict the creep rate and rupture time by undertaking the layer height, infill percentage, and infill pattern type (linear, hexagonal, and diamond) as input process parameters. A total of 39 experimental runs were planned by means of a categorical central composite design. The analysis of variance (ANOVA) results revealed that the most influencing factors for creep rate were layer height, infill percentage, and infill patterns, whereas, for rupture time, infill pattern was found significant. The optimized levels obtained for both responses for hexagonal pattern were 0.1 mm layer height and 100% infill percentage. Some verification tests were performed to evaluate the effectiveness of the adopted RSM technique. The implemented research is believed to be a comprehensive guide for the additive manufacturing users to determine the optimum process parameters of FDM which influence the product creep rate and rupture time.

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Section: Methodsmentioning

confidence: 99%

Multi-Response Optimization of Tensile Creep Behavior of PLA 3D Printed Parts Using Categorical Response Surface Methodology

et al. 2020

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“…The FC-CCD design composed of factorial (3 k ), axial (2k), and replicated central (k) runs for the k number of investigated factors. The CCD “star” points outside this experimental domain and the design points at the center of the experimental domain make it possible to estimate the curvature of the response surface [ 33 , 38 ]. Moreover, the FC-CCD has advantages over other forms of the CCD, as its star points are at the center of each face of the factorial space (i.e., α = ± 1), it requires fewer levels of each factor (3 against 5 for other design), and also it can also be achieved by augmenting an existing factorial or resolution V design data with appropriate star points [ 33 , 38 ].…”

Section: Methodsmentioning

confidence: 99%

“…The CCD “star” points outside this experimental domain and the design points at the center of the experimental domain make it possible to estimate the curvature of the response surface [ 33 , 38 ]. Moreover, the FC-CCD has advantages over other forms of the CCD, as its star points are at the center of each face of the factorial space (i.e., α = ± 1), it requires fewer levels of each factor (3 against 5 for other design), and also it can also be achieved by augmenting an existing factorial or resolution V design data with appropriate star points [ 33 , 38 ]. Table 1 provides the experimental data points required to implement the FC-CCD for the phenol uptake onto SBAC-MgAlFe-LDH, which ran in triplicate.…”

Section: Methodsmentioning

confidence: 99%

Process Optimization and Modeling of Phenol Adsorption onto Sludge-Based Activated Carbon Intercalated MgAlFe Ternary Layered Double Hydroxide Composite

2021

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A sewage sludge-based activated carbon (SBAC) intercalated MgAlFe ternary layered double hydroxide (SBAC-MgAlFe-LDH) composite was synthesized via the coprecipitation method. The adsorptive performance of the composite for phenol uptake from the aqueous phase was evaluated via the response surface methodology (RSM) modeling technique. The SBAC-MgAlFe-LDH phenol uptake capacity data were well-fitted to reduced RSM cubic model (R2 = 0.995, R2-adjusted = 0.993, R2-predicted = 0.959 and p-values < 0.05). The optimum phenol adsorption onto the SBAC-MgAlFe-LDH was achieved at 35 °C, 125 mg/L phenol, and pH 6. Under the optimal phenol uptake conditions, pseudo-first-order and Avrami fractional-order models provided a better representation of the phenol uptake kinetic data, while the equilibrium data models’ fitting follows the order; Liu > Langmuir > Redlich–Peterson > Freundlich > Temkin. The phenol uptake mechanism was endothermic in nature and predominantly via a physisorption process (ΔG° = −5.33 to −5.77 kJ/mol) with the involvement of π–π interactions between the phenol molecules and the functionalities on the SBAC-LDH surface. The maximum uptake capacity (216.76 mg/g) of SBAC-MgAlFe-LDH was much higher than many other SBAC-based adsorbents. The improved uptake capacity of SBAC-LDH was attributed to the effective synergetic influence of SBAC-MgAlFe-LDH, which yielded abundant functionalized surface groups that favored higher aqueous phase uptake of phenol molecules. This study showcases the potential of SBAC-MgAlFe-LDH as an effective adsorbent material for remediation of phenolic wastewater

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“…The Box-Behnken design is characterized by a specific technique to reduce the experimental effort [38][39][40]. The central composite design is augmented with star points for curvature estimation of the response surface [41][42][43]. The evaluation of the results with a DoE software leads not just to information about parameter interactions but also to information regarding optimal process conditions that can be derived from the resulting hyper surface plot.…”

Section: Strategy For the Optimization Routinementioning

confidence: 99%

Inline Spectroscopy‐Based Optimization of Chemical Reactions Considering Dynamic Process Conditions

Trunina

Liauw

2020

Chemie Ingenieur Technik

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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.The demand for sustainable energy sources, like biomass, solar energy, hydro and wind power, is connected to challenges like energy storage and fluctuating energy supply. Regarding the second challenge, industry has to evolve their existing processes from steady state processes to dynamic ones. This work is concerned with the conception of an inline spectroscopy-based optimization routine for chemical reactions under dynamic process conditions, which implements the search for a well applicable optimization algorithm. The studied reaction to reach this goal is a nitroaldol condensation.

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Meta-Model Development

Cited by 59 publications

References 0 publications

Multi-Response Optimization of Tensile Creep Behavior of PLA 3D Printed Parts Using Categorical Response Surface Methodology

Multi-Response Optimization of Tensile Creep Behavior of PLA 3D Printed Parts Using Categorical Response Surface Methodology

Process Optimization and Modeling of Phenol Adsorption onto Sludge-Based Activated Carbon Intercalated MgAlFe Ternary Layered Double Hydroxide Composite

Inline Spectroscopy‐Based Optimization of Chemical Reactions Considering Dynamic Process Conditions

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