Response surface experiments: A meta-analysis

Ockuly, Rebecca A.; Weese, Maria L.; Smucker, Byran J.; Edwards, D.J.; Chang, Le

doi:10.1016/j.chemolab.2017.03.009

Cited by 28 publications

(15 citation statements)

References 22 publications

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“…Thus, the screening phase plays a very important role in the RSM, because the monetary and time costs of experimentation grow exponentially with the number of variables or factors in the subsequent phases of experimentation. How to identify the location effects (those that have effect on response mean) from unreplicated designs has been a widely researched topic, and three general guidelines (see Ockuly et al [57] for an empirical quantification of these guidelines in the RSM framework based on a meta-analysis) must be considered in the results interpretation: a) Only a small fraction of tested factors will be active (influence or have a statistically significant effect on response). This is called the effects sparsity principle.…”

Section: Doe-experimental Design Selection and Results Analysis Methodsmentioning

confidence: 99%

“…With this (small) set of variables, a two-level fractional factorial design (2 k-p design with k variables or factors and p independent generators) was selected. This design type is often used for screening in physical and simulation experiments when the number of variables is small (say up to about 15 to 20 variables, as it is common in physical experiments) due to their efficiency, effectiveness, and versatility for sequential experimentation [57,84]. In 2 k-p designs, each factor is tested at two levels and only a fraction (1/2 p ) of all factor-level combinations are run.…”

Section: Design and Analysis Of Experiments: Theoretical Framework Anmentioning

confidence: 99%

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Energy-Efficiency Assessment and Improvement—Experiments and Analysis Methods

Costa

Fontes

2020

Sustainability

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Some (non)manufacturing industries are becoming more energy efficient, but many of them are losing cost-effective energy-savings opportunities, namely, by lack of knowledge or underestimation of good engineering and management practices as well as guidance on techniques or tools for that purpose. This study points out that Design of Experiments is a tool that cannot be ignored by managers and other technical staff, namely, by those who have the responsibility to eliminate energy waste and promote energy-efficiency improvement in industry, mainly in energy-intensive manufacturing industries. A review on Design of Experiments for physical and simulation experiments, supported on carefully selected references, is provided, since process and product improvement at the design and manufacturing stages increasingly rely on virtual tests and digital simulations. However, the expense of running experiments in complex computer models is still a relevant issue, despite advances in computer hardware and software capabilities. Here, experiments were statistically designed, and several easy-to-implement yet effective data analysis methods were employed for identifying the variables that must be measured with more accurate devices and methods to better estimate the energy efficiency or improve it in a billets reheating furnace. A simulation model of this type of furnace was used to run the experiments and the results analysis shows that variables with practical effect on the furnace’s energy efficiency are the percentage of oxygen in the combustion gases, the fuel flow in the burners, and the combustion air temperature.

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Section: Doe-experimental Design Selection and Results Analysis Methodsmentioning

confidence: 99%

Section: Design and Analysis Of Experiments: Theoretical Framework Anmentioning

confidence: 99%

Energy-Efficiency Assessment and Improvement—Experiments and Analysis Methods

Costa

Fontes

2020

Sustainability

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“…Coefficients of determinations ( R 2 ) values of between 0.51 and 0.96 were obtained for the regression models. The insignificant lack of fit of the presented model for the SC, EEC, and PR responses shows that it is suitable for making the predictions (Ockuly, Weese, Smucker, Edwards, & Chang, ). Regression analyses indicated that the special cubic model was significant ( p < .05) for modeling the SC, and the cubic model was significant ( p < .05) for modeling the EEC and PR, but not significant ( p > .05) for the RC response.…”

Section: Resultsmentioning

confidence: 99%

“…Regression analyses indicated that the special cubic model was significant ( p < .05) for modeling the SC, and the cubic model was significant ( p < .05) for modeling the EEC and PR, but not significant ( p > .05) for the RC response. In these models, a positive partial regression coefficient value for nonlinear terms implies a synergistic effect of the constituents in the binary or ternary blends, whereas the corresponding negative value is indicative of antagonistic effects (Ockuly et al, ).…”

Section: Resultsmentioning

confidence: 99%

Microencapsulation optimization of cinnamon essential oil in the matrices of gum Arabic, maltodextrin, and inulin by spray‐drying using mixture design

Shahidi‐Noghabi¹,

Molaveisi²

2019

J Food Process Engineering

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A simple centroid mixture design was used to optimize the ratio of the wall materials for the production of the cinnamon essential oil microcapsules by spray-drying. Gum Arabic (GA), maltodextrin (MD), and inulin (IN) were used as wall materials. Four parameters were evaluated as responses, containing the surface cinnamaldehyde, the encapsulation efficiency of cinnamaldehyde, the release of cinnamaldehyde (RC), and powder recovery (PR). The cinnamon essential oil was purely formed of cinnamaldehyde (about 99.96%), based on results obtained from the gas chromatography device. The multiple response optimization analysis showed that the optimum compound concentration for walls will be 27.87% wt/wt GA, 27.59% MD, and 44.53% IN. It can be concluded that the optimal compound was suitable to the RC in the mouth medium due to its Tg. The microscopy images of the optimal compound showed a smooth surface, spherical, and slight crack in the microparticles. Furthermore, the optimum compound improved the stability of antioxidant activity compared to the pure oil during 30 days storage. It was observed that the Fickian diffusion was the main mechanism involved in the release process of the microencapsulated cinnamon essential oil in the mouth medium.Practical Applications: In the food industry, the major aromatic components utilized are essential oils, synthetic flavoring agents, and natural oleoresin. The key constituent of cinnamon essential oil is cinnamaldehyde. Cinnamaldehyde is often used as a flavoring agent, but its importance as a natural antimicrobial, antioxidant, and antiseptic agent has recently been established. The knowledge of encapsulation techniques allows the control of the bioactive components, the release time, and the dosage of essential oil in the structure of the food. Moreover, the encapsulation of essential oils helps to reduce the oxidation of process by exposure to the environment, increasing the shelf life and the solubility. The results of this work lead to the use of flavored components for implementation in similar products, thereby improving their flavor.

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“…Model selection methods that possess Property 3 can restrict the search space to specific sets of meaningful models such as those obeying weak or strong effect heredity (Hamada and Wu, 1992), where it is assumed that an interaction or a quadratic effect can be active only if its corresponding main effect(s) are also active. Based on a meta-analysis of a large number of two-level factorial experiments and response surface experiments, Li et al (2006) and Ockuly et al (2017) showed that effect heredity generally holds in practice.…”

Section: Introductionmentioning

confidence: 99%

A mixed integer optimization approach for model selection in screening experiments

Vazquez

Schoen

Goos

2020

Journal of Quality Technology

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After completing the experimental runs of a screening design, the responses under study are analyzed by statistical methods to detect the active effects. To increase the chances of correctly identifying these effects, a good analysis method should: (1) provide alternative interpretations of the data, (2) reveal the aliasing present in the design, and (3) search only meaningful sets of effects as defined by user-specified restrictions such as effect heredity or constraints that include all the contrasts of a multi-level factor in the model. Methods like forward selection, the Dantzig selector or LASSO do not posses all these properties. Simulated annealing model search cannot handle other constraints than effect heredity. This paper presents a novel strategy to analyze data from screening designs that posses properties (1)-(3) in full. It uses modern mixed integer optimization methods that returns the results in a few minutes. We illustrate our method by analyzing data from real and synthetic experiments involving two-level and mixed-level screening designs. Using simulations, we show the capability of our method to automatically select the set of active effects and compare it to the benchmark methods.

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Response surface experiments: A meta-analysis

Cited by 28 publications

References 22 publications

Energy-Efficiency Assessment and Improvement—Experiments and Analysis Methods

Energy-Efficiency Assessment and Improvement—Experiments and Analysis Methods

Microencapsulation optimization of cinnamon essential oil in the matrices of gum Arabic, maltodextrin, and inulin by spray‐drying using mixture design

A mixed integer optimization approach for model selection in screening experiments

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