scholarly journals Central Composite Design for Response Surface Methodology and Its Application in Pharmacy

2021 ◽  
Author(s):  
Sankha Bhattacharya
Keyword(s):  
Central Composite Design ◽  
Response Surface ◽  
Response Surface Model ◽  
Fractional Factorial ◽  
Surface Model ◽  
Experimental Conditions ◽  
Design Models ◽  
Central Composite

The central composite design is the most commonly used fractional factorial design used in the response surface model. In this design, the center points are augmented with a group of axial points called star points. With this design, quickly first-order and second-order terms can be estimated. In this book chapter, different types of central composite design and their significance in various experimental design were clearly explained. Nevertheless, a calculation based on alpha (α) determination and axial points were clearly described. This book chapter also amalgamates recently incepted central composite design models in various experimental conditions. Finally, one case study was also discussed to understand the actual inside of the central composite design.

scholarly journals Optimization of chlorite and talc flotation using experimental design methodology: Case study of the MCG plant, Morocco

2020 ◽  
Vol 120 (12) ◽  
Author(s):  
K. Boujounoui ◽  
A. Abidi ◽  
A. Baçaoui ◽  
K. El Amari ◽  
A. Yaacoubi
Keyword(s):  
Response Surface Methodology ◽  
Central Composite Design ◽  
Response Surface ◽  
Desirability Functions ◽  
Sulphide Ore ◽  
Experimental Values ◽  
Experimental Design Methodology ◽  
Central Composite ◽  
Good Agreement

SYNOPSIS Response surface methodology (RSM), central composite design (CCD), and desirability functions were used for modelling and optimization of the operating factors in chlorite and talc (collectively termed 'mica') flotation. The influence of pulp pH, cyanide (NaCN) consumption, and particle size was studied with the aim of optimizing ssilicate flotation while minimizing recoveries of galena, chalcopyrite, and sphalerite. Flotation tests were carried out on a representative sample of a complex sulphide ore from Draa Sfar mine (Morocco). The model predictions for the flotation of each of the minerals concerned were found to be in good agreement with experimental values, with R2 values of 0.91, 0.98, 0.99, and 0.90 for mica, galena, chalcopyrite, and sphalerite recoveries, respectively. RSM combined with desirability functions and CCD was successfully applied for the modelling of mica flotation, considering simultaneously the four flotation responses to achieve the maximum recovery of mica and minimal loss of Pb, Cu, and Zn to the flotation concentrate. Keywords: chlorite, talc, flotation, response surface methodology, central composite design, optimization.

Response Surface Model for the Estimation of Escherichia coli O 157:H7 Growth under Different Experimental Conditions

2005 ◽  
Vol 70 (1) ◽  
pp. M30-M36 ◽  
Author(s):  
Rose Maria García-Gimeno ◽  
Elena Barco ◽  
Francisco Rincón ◽  
Gonzalo Zurera-Cosano
Keyword(s):  
Escherichia Coli ◽  
Response Surface ◽  
Response Surface Model ◽  
Surface Model ◽  
Experimental Conditions ◽  
O 157

Response Surface Algorithms for Engine Mount Optimization in Motorcycles

2009 ◽  
Author(s):  
Sudhir Kaul ◽  
Anoop K. Dhingra
Keyword(s):  
Response Surface ◽  
Optimization Problem ◽  
Vibration Isolation ◽  
Design Space ◽  
Substantial Reduction ◽  
Response Surface Model ◽  
Fractional Factorial ◽  
Surface Model ◽  
Design Variables ◽  
Engine Mount

This paper presents a Response Surface Modeling (RSM) approach for solving the engine mount optimization problem for a motorcycle application. A theoretical model that captures the structural dynamics of a motorcycle engine mount system is first used to build the response surface model. The response surface model is then used to solve the engine mount optimization problem for enhanced vibration isolation. Design of Experiments (DOE), full factorial and fractional factorial formulations, are used to construct the governing experiments. Normal probability plots are used to determine the statistical significance of the variables and the significant variables are then used to build the response surface. The design variables for the engine mount optimization problem include mount stiffness, position vectors and orientation vectors. It is seen that RSM leads to a substantial reduction in computational effort and yields a simplified input-output relationship between the variables of interest. However, as the number of design variables increases and as the response becomes irregular, conventional use of RSM is not viable. Two algorithms are proposed in this paper to overcome the issues associated with the size of the governing experiments and problems associated with modeling of the orientation variables. The proposed algorithms divide the design space into sub-regions in order to manage the size of the governing experiments without significant confounding of variables. An iterative procedure is used to overcome high response irregularity in the design space, particularly due to orientation variables.

Earphone terminal quality improvement through sequential experimental design

2015 ◽  
Vol 32 (7) ◽  
pp. 693-702 ◽  
Author(s):  
Zhen He ◽  
Xu-tao Zhang ◽  
Gui-qing Xie ◽  
Min Zhang
Keyword(s):  
Factorial Design ◽  
Response Surface ◽  
Response Surface Model ◽  
Full Factorial Design ◽  
Fractional Factorial ◽  
Surface Model ◽  
Quality Performance ◽  
Content Type ◽  
Input Variables ◽  
Full Factorial

Purpose – The purpose of this paper is to improve the key quality performance of the terminal of earphone in an electronic company. Design/methodology/approach – Sequential experimental designs are employed. Significant input variables are found through a full factorial design. Then a response surface model is constructed considering curvature in the linear model. Findings – Optimized key input variables’ parameters are found using the response surface model. The key quality performance, coplanarity of the terminal of earphone has been improved. Research limitations/implications – Instead of running a full factorial design in the first stage, a fractional factorial may be used to reduce experimental runs. Practical implications – The paper presents a good solution for reducing defects caused by large coplanarity of a kind of earphone terminal. Originality/value – The methodology used in this case can be easily extended to similar cases.

scholarly journals Simultaneous Production of Bioemulsifier and Exopolysaccharides by Thermophilic Brevibacillus Borstelensis Using Response Surface Model, Genetic Algorithm and NSGA

2020 ◽  
Author(s):  
Swasti Dhagat ◽  
Satya Eswari Jujjavarapu
Keyword(s):  
Genetic Algorithm ◽  
Central Composite Design ◽  
Response Surface ◽  
Monosodium Glutamate ◽  
Thermophilic Bacterium ◽  
High Yield ◽  
Surface Model ◽  
Optimization Study ◽  
Central Composite ◽  
Modelling And Optimization

Abstract Bioemulsifier and exopolysaccharides are two of the important biomolecules produced as secondary metabolites by many microorganisms. Bioemulsifiers emulsify hydrophobic substrates allowing their consumption by microorganisms whereas exopolysaccharides provide structural scaffold for the microorganisms to attach to any surface for biofilm formation. Both of these biomolecules have various applications in pharmaceutical and biomedical sectors and hence their production with high yield becomes important. A thermophilic bacterium, Brevibacillus borstelensis, has shown to produce bioemulsifier and exopolysaccharides simultaneously with limited yields. Central composite design-response surface methodology was employed as experimental design strategy to maximize the yields of both of these products with concentrations of glucose, monosodium glutamate, yeast extract and magnesium sulphate in the medium as process variables. The concentration of bioemulsifiers, exopolysaccharides and biomass were chosen as responses of the study. A set of 30 experiments were performed as a part of optimization study with 24 runs as non-centre points and 6 runs as centre points. The model obtained from central composite design was further optimized by genetic algorithm and naïve sorted genetic algorithm. The best results obtained from the three modelling and optimization tools were compared and the models were validated.

Sign in / Sign up

Export Citation Format

Share Document