Mixture Optimization by the CARSO Procedure and DCM Strategies
2
Citation
8
Reference
10
Related Paper
Citation Trend
Abstract:
The paper illustrates a new way of using the CARSO procedure for response surfaces analyses derived from innovative experimental designs in multivariate spaces, based on Double Circulant Matrices (DCMs). We report a case study regarding a design based on a DCM for 4 variables. The final response surface model is obtained by the formerly developed CARSO method.Keywords:
Design of experiments
Design of experiments
Box–Behnken design
Central composite design
Chemometrics
Fractional factorial design
Cite
Citations (5,163)
Matrix (chemical analysis)
Cite
Citations (3)
In a previous study, 'response surface methodology (RSM) using a fullest balanced model' was proposed to improve the optimization of food processing when a standard second-order model has a significant lack of fit. However, that methodology can be used when each factor of the experimental design has five levels. In response surface experiments for optimization, not only five-level designs, but also three-level designs are used. Therefore, the present study aimed to improve the optimization of food processing when the experimental factors have three levels through a new approach to RSM. This approach employs three-step modeling based on a second-order model, a balanced higher-order model, and a balanced highest-order model. The dataset from the experimental data in a three-level, two-factor central composite design in a previous research was used to illustrate three-step modeling and the subsequent optimization. The proposed approach to RSM predicted improved results of optimization, which are different from the predicted optimization results in the previous research.
Design of experiments
Central composite design
Cite
Citations (3)
Design of experiments
Machinability
Central composite design
Process variable
Cite
Citations (231)
The work reported here focuses on the oil and grease removal from wastewater by the electrocoagulation process and using modeling and optimization for obtaining the results considering four major operating parameters, viz. current density, pH, electrode distance and reaction time. 31 experiments were designed by design of experiments (DOE) of response surface methodology (RSM) and the analysis of variance (ANOVA) studies confirmed the agreement of the experimental results. Artificial neural network (ANN) was also utilized to determine predicted response using neural networks for 4-10-1 arrangement. Both the responses predicted by RSM and ANN were in alignment with the experimental results. Maximum removal of 78% was attained under the working parameters of 80 A m-2, 3.6 pH, electrode distance of 0.005 m and reaction time of 20 min.
Electrocoagulation
Design of experiments
Grease
Box–Behnken design
Central composite design
Cite
Citations (0)
The experimental design and response surface methodology (RSM) is applied to a direct contact membrane distillation process. The factors considered for experimental design were the feed and permeate flow rate, the mean temperature, and the initial feed concentration of salt (NaCl) aqueous solution. The significant factors were optimized using a central composite design of orthogonal type. The quadratic models between the responses (permeate fluxes) and the independent parameters were built for both commercial and laboratory made membranes of different characteristics. The response surface models were tested with analysis of variance (ANOVA). For optimization purposes, the canonical analysis was employed. An algorithm was developed following the gradient method and using step adjustment in order to explore the response surface only inside the region of experimentation. The obtained optimal points were located in the valid region. The predicted permeate fluxes were compared with the experimental ones. In general, there is a good agreement between the experimental and the predicted permeate fluxes by RSM.
Membrane Distillation
Central composite design
Design of experiments
Cite
Citations (112)
In this paper, we investigate the structure of k -circulant matrices with odd order, and then present some new properties of k -circulant matrices under certain conditions.
Cite
Citations (0)
Several chemical and biological processes have been investigated and predicted using Response Surface Methodology (RSM) models. Response Surfaces Methodology is a useful instrument for designing laboratory-scale experiments that optimize and support the research outcomes with statistical analysis. It is a powerful statistical technique for complex variable study systems. The standard optimization (one component at a time) strategy is well-studied. However, it has significant drawbacks, such as requiring more experimental runs and time and failing to reveal the synergistic impact of processing parameters. It is a valuable instrument for process improvement. Recent research has shown, for instance, that RSM successfully optimizes biodiesel in fats and oils generated from diverse feedstocks. According to this study, Response Surface Methodology is the best cost-effective technique for maximizing environmentally friendly and sustainable methods applied to different experimental procedures. The current review reported RSM's application, theory, methodology, advantages, and limitations for different processes using different oil sources.
Design of experiments
Experimental data
Environmentally Friendly
Cite
Citations (25)
A design of experiment (DOE) is used in many industrial sectors in the development and optimization of manufacturing processes. DOE is a more effective way to determine the impact of two or more factors on a response. In this paper, a Two-Level Fractional Factorial design was employed to develop the important factors that significantly affect new promising OT Rubber recycling machine using a DOEs analysis. Central composite design (CCD) and response surface methodology (RSM) were used to optimize the isolating screen size. The CCD considered five factors with two-level full factorial design. CCD experiments using RSM was proved to be an optimal tool for optimization.
Fractional factorial design
Central composite design
Design of experiments
Factorial
Box–Behnken design
Cite
Citations (1)
Design of experiments
Naphtha
Experimental data
Cite
Citations (80)