Top PDF An Optimizing Method for Screening in A Mixture Design Experiment

An Optimizing Method for Screening in A Mixture Design Experiment

An Optimizing Method for Screening in A Mixture Design Experiment

For the motor octane example, we can see that we end up with the same solution as Cornell in which we find a single non-significant component seeing our confidence interval in our analysis. In example number two of the residual glass, we could see that we found some differences between the components that were significant. We saw that in the results of Piepel the components that were not significant were MnO and SiO2 with p values of 0.139 and 0.157 respectively. compared to this new method as we saw earlier we also find two non-significant components, SiO2 and U3O2, the latter being different from one of the components of Piepel MnO. One of the reasons why this could have happened is because Piepel used a different encoding than the one we use.
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Mauro: A Novel Strategy for Optimizing Mixture Properties

Mauro: A Novel Strategy for Optimizing Mixture Properties

terms, plus all possible bifactorial interactions. The equa- tion coefficients are usually derived by Multiple Linear Regression on the expanded X matrix when the data are collected according to a rigorous orthogonal design. Since data extracted by a D-optimal criterion are usually well rounded, but not strictly orthogonal, the regression method to be used is PLS. The quadric regression coeffi- cients can be derived thereafter from PLS loadings, equalizing the PLS system to the regression equation.

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A Screening Experiment on a Diabetes Mellitus Herbal Formulation

A Screening Experiment on a Diabetes Mellitus Herbal Formulation

( 0, 1 ⁄ − 1 , … 1 ⁄ − 1 )) giving 3q + 1 or 2q + 1 design points all together, when it is possible to experiment over the total composition range of all components (0–100%). They recommended the fitting of a linear model for investigation where the relative effects are assessed by ranking the estimated coefficients ( of the standardised ′ in order by absolute value. Cornell [7] recommended the use of axial designs in screening experiments and when component effects are to be measured particularly when first degree models are to be fitted .The number of design points needed to fit the quadratic model in the case of six or more mixture components is quite large, especially considering that at the early stage of experimentation, one is not sure of the most effective components and/or the right component levels [7]. Cornell [6] acknowledges that construction of screening designs as well as the setting up of the appropriate screening models for mixture experiments quite often begins with the Scheffe first degree model. Weese [24] uses the Cox mixture models to develop a general method for screening a subset of components of mixtures with complex constraints. Goos and Bradley [11] noted that for the purpose of factor screening, many investigators use the main-effects model as the prior model with the assumption that higher-order effects, such as the two-factor effects, can be assumed. Kolthoff [13] recommended an axial mixture design with replicated centre points for screening experiments.
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OPTIMIZATION OF RP-HPLC METHOD FOR SIMULTANEOUS ESTIMATION OF DOLUTEGRAVIR AND RILPIVIRINE IN BINARY MIXTURE BY USING DESIGN OF EXPERIMENTS

OPTIMIZATION OF RP-HPLC METHOD FOR SIMULTANEOUS ESTIMATION OF DOLUTEGRAVIR AND RILPIVIRINE IN BINARY MIXTURE BY USING DESIGN OF EXPERIMENTS

After initial screening of the conditions like stationary phase, mobile phase the method has been gone for optimization by using Design Expert 10.0.0.8 trail version software. In this software, Central composite design under the category of Response surface methodology (RSM) have been employed to design a set of experimental runs by concerning the three independent variables i.e., flow rate, wavelength and % of buffer in the mobile phase. Here the independent variables ranges were entered along with their actual levels. The levels used were tabulated in (Table.1). In the experimental design central composite design was followed with three factors, 2 levels, responses and 20 experimental runs. These three factors have an effect on the dependent variables i.e., retention time, resolution and peak area for both Rilpivirine and Dolutegravir.
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Comparison of Taguchi method and central 
		composite design for optimizing process parameters in Vertical Double 
		Gate MOSFET

Comparison of Taguchi method and central composite design for optimizing process parameters in Vertical Double Gate MOSFET

Experimental validation is the final step in the design of experiment (DoE) process. The main purpose of the experimental validation is to validate the results retrieved during analysis phase [28]. In this case, the experimental validation was performed by conducting an actual simulation test by using the overall best level setting of process parameters that have been previously predicted by the CCD and Taguchi method. The results retrieved from both CCD and Taguchi method were compared to the prediction of International Technology Roadmap Semiconductor 2013 (ITRS 2013) for low power (LP) multi-gate (MG) technology requirement in the year 2020 [30]. Table-18 shows the results of the experimental validation for both CCD and Taguchi method.
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DESIGN OF EXPERIMENT IN THE BIO ANALYTICAL DETERMINATION OF QUETIAPINE FUMARATE IN HUMAN PLASMA BY A RP HPLC METHOD

DESIGN OF EXPERIMENT IN THE BIO ANALYTICAL DETERMINATION OF QUETIAPINE FUMARATE IN HUMAN PLASMA BY A RP HPLC METHOD

Instrumentation: The HPLC system (Cyberlab LC 100) consisting of binary gradient pump, micro- sorb-MV 100-5 C- 18 column (250 × 4.6mm, 5 µm), UV detector was employed for analysis. Chromatographic data was acquired using WS-100 Workstation software. Microsorb MV 100-5 C-18 column (150 mm×4.6 mm, 5µm) was used as a stationary phase. A Millipore glass filter (Millipore filter cellulose nitrate gridded with 0.22 µ size and 47 mm diameter) assembly attached with vacuum pump was used to filter mobile phase. Samples were sonicated for 20 min using ultrasonic cleaner- Equitron-Medica Instrument Mfg. Co., Mumbai, India) to remove dissolved gases from the mobile phase. A Genie-2 Spinix model vortex mixer, a cooling centrifuge (Remi, Germany) was used for extraction of the drug from human plasma. Eqiuptronics pH meter Instrument using a glass electrode was used to adjust the pH of buffer. Chromatographic Conditions: The isocratic mobile phase consisting of a mixture of phosphate buffer (pH 3.0 adjusted with ortho-phosphoric acid) and acetonitrile in the ratio of 50:50 (v/v) was used on microsorb MV 100-5 C-18 column (250 mm × 4.6 mm, 5 µm) as a stationary phase. The flow rate of the mobile phase was 1.0 ml/min. Detector signal was monitored at a wavelength of 292 nm using UV detector while keeping the 10 min run time for chromatographic analysis. Prior to the injection of the drug solution, the column was equilibrated for at least 30 min. The column temperature was kept ambient and injection volume was 20 µl.
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Experimental Investigation of Diesel Engine Operating Parameters for a Mixture of Acetylene and Turpentine Oil with Diesel by Design of Experiment

Experimental Investigation of Diesel Engine Operating Parameters for a Mixture of Acetylene and Turpentine Oil with Diesel by Design of Experiment

In this research work turpentine and diesel blend is used in the diesel engine where acetylene introduce at 3 liters per minute at constant flow rate and performed the experiment based on design of experiment matrix (L9 orthogonal array), after performing the experiment according to run set got the value of FC is 0.49kg/hr and specific fuel consumption is 0.35 kg/kwh at 60% diesel and 40% turpentine blend ratio (D60T40) at which injection pressure was 190 bar and load was 5 k.g. and 24.31% is the Brake thermal efficiency, confirmation experiment is also performed on the predicted result by Taguchi analysis to evaluate the Predictive Capability of the Taguchi Method for Diesel engine performance. Parameters which has been predicted by Taguchi are blend ratio D60T40, injection pressure bar 190 bar and load 5kg and prediction value of brake thermal efficiency is 35.88 % and experiment performed on same range and after experiment got the 34.45% brake thermal efficiency and percentage of error is 3.99.
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Development of HPLC Method for Stress Testing of Combination of Two Drugs Using Design of Experiment Concept

Development of HPLC Method for Stress Testing of Combination of Two Drugs Using Design of Experiment Concept

The forced degraded sample solutions were preliminary screened for degradation behaviour. After the initial screening, the samples parameters- concentration of stressor, time period of study- were finalized. This also gave us knowledge regarding HPLC parameters effects. The finalized samples were then subjected to DoE. The number of runs and parameters for each run were calculated from the Design expert software (trial version 9). The limit for variable primary run parameters selected (organic modifier, buffer pH, initial hold time, gradient time) [20- 23] are shown in table 3. The suggested 32 runs were
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Experimental Design for Optimizing a Mixture of Materials plus an Evaporating Solvent

Experimental Design for Optimizing a Mixture of Materials plus an Evaporating Solvent

Following our previous paper published last November [1], where we have showed a new strategy for collecting the data needed for defining a response surface on the basis of an innovative strategy that requires only a very low number of experimental data, based on Double Circulant Matrices (DCMs), thus obtaining better results with respect to the previous method based on D-optimal design [2], for the objective of industrial research in reaching the best level of technological properties the mixture is expected to exhibit. The DCMs have similar

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Optimizing the injection parameter of water atomised SS316L powder with design of experiment method for best sintered density

Optimizing the injection parameter of water atomised SS316L powder with design of experiment method for best sintered density

approach. With an appropriate DOE, one can quickly and with fewer number of trials, find out whether the variables have an effect on the output quality. The Taguchi approach is mostly used in the industrial environment, but it can also be used for scientific research. The method is based on balanced orthogonal arrays [12]. In this paper, L 27 (3 13 ) orthogonal

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The Statistical Mixture Design of Rice Polishing Cylinder

The Statistical Mixture Design of Rice Polishing Cylinder

During milling of all mixtures, it was found that the longer the contact time between abrasive polishing cylinder and the rubber, the more abrasive efficiency. The analysis of variance are presented in Table I, II, III, IV, V and VI. The application of mixture design in this experiment was complicated and time consuming. Since all of the coefficients have to be interpreted under the restriction that a third factor is varied at the same time as the two which are actively used. The only significant contributions are from the product of x 1 and x 3 and from the

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OPTIMIZING MULTIPLIER DESIGN USING VEDIC MATHEMATICS

OPTIMIZING MULTIPLIER DESIGN USING VEDIC MATHEMATICS

A Simple ALU which perform multiplication and division has been presented. Multiplier is designed using Urdhva Tiryakbhyam( Vertical and cross wise) sutra where addition of partial product is carried out using kogge stone adder. In this paper the comparison of vedic mathematics and conventional mathematical method is carried out to know the best architecture for ALU design w,r,t power and delay characteristics. The design of architecture are done in Verilog language and the tool used for simulation is Xilinx 9.1 ISE

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Using a discrete choice experiment to inform the design of programs to promote colon cancer screening for vulnerable populations in North Carolina

Using a discrete choice experiment to inform the design of programs to promote colon cancer screening for vulnerable populations in North Carolina

We chose to represent out of pocket follow-up costs with percentages rather than absolute amounts because the amounts of out of pocket follow-up costs vary so much according to whether the person always screens negative (and hence has no additional costs), requires diagnostic colonoscopy with or without polypectomy, or is diagnosed with cancer and requires treatment. Given this uncertainty, we sought to simulate how insurance pro- grams address costs, ranging from no coverage (100% paid by the person) to a 20% copayment, to full coverage (0% out of pocket expense). Not surprisingly, this attribute was considered very important, and reinforces the need for policymakers to consider follow-up care costs when de- signing screening programs.
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Security Based Optimizing Design To Migrate Cloud

Security Based Optimizing Design To Migrate Cloud

Conclusion and Future Work: Cloud Computing is facilitating users round the world for the most effective of the services obtainable across the planet on their machines through internet. It’s helpful for each the service suppliers (they get huge clientele) and shoppers (they get all obtainable services). Virtual Machine and application migration are one among the basic challenges. This proposed work design can give a reliable design for the applying application migrations which might be changed for virtual machine migration in future also. The proposed work is found to provide high performance with security and dependability and additionally it’s progressing to be providing the stable and rigid system for the users of cloud.
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Security based Optimizing Design to Migrate Cloud

Security based Optimizing Design to Migrate Cloud

When turning to the cloud environment, since the applications deployed in the cloud are usually complicated and consist of a large number of components, only emp[r]

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Design of the FutureGrid Experiment Management Framework

Design of the FutureGrid Experiment Management Framework

It is obvious that such a use case can easily be managed through our data structure. The Information related to the allocation is managed by the teacher. He delegates the addition of users to his system administrator that has a list of students in the class (in future additional convenience features may be available through the InCommon framework). The teaching assistant is added as staff member to the allocation and is allowed to generate new projects, experiments, and experiment apparatus that are referred to within the experiments. The teacher decides to use the model where each week a new experiment is conducted, that contains for each student in the next level of the data structure hierarchy a placeholder for an experiment managed by a student for that week. Each student is in charge of uploading and managing its own experiment meta data. An apparatus that has been created by the teaching assistant can be shared amongst the students. This helps executing experiments on similar environments. At the end of the class paper and talks will be uploaded to the experiment management service.
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Experiment Design for the  Location Allocation Problem

Experiment Design for the Location Allocation Problem

We are betting on determining values in the SA parameters so they can be compared in a fair way with VNS, being the time ( ) T of execution the common parameter for the 2 heuristics under study. To get close to convenient parameters, the first part of the tests was experimental and random. Considering the times obtained in the Table 1 for VNS, diverse instances were created for SA relying on their respective BB (to trust in the suitability of the model). The next design was achieved and its model and instances can be seen in the Table 2 and the Figure 2 shows that the data behaves normally and that there’s no effect between runs in the experi- ment:
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A Fast Iteration Method for Mixture Regression Problem

A Fast Iteration Method for Mixture Regression Problem

The Equation (1.1), a mixture regression model, can also be treated as a model-based clustering [2], which can be solved by an EM Algorithm [3]-[5]. In fact, EM Algorithm is a statistical method for maximizing the li- kelihood function by iterative method. The algorithm can be divided into two steps. The one is E-step which is used for estimating the exception for the parameters. The other one is M-step, which is used for maximize the likelihood function under the parameters predicted in E-step. The iteration will continue until the change of like- lihood function is less than a given value (i.e. 10 −6 ).
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Design of the FutureGrid Experiment Management Framework

Design of the FutureGrid Experiment Management Framework

– Often it is desirable to conduct parameter studies or repetitive experiments with the same setup in regards to resources used. We refer to such a configuration as an ``experiment apparatus''. Such an apparatus allows the users to conveniently reuse the same setup without reconfiguration of the FutureGrid resources for different experiments.

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Optimization Process By Using Design Of Experiment (DOE) Method To Increase Performance Of Fiber Optic Glucose Sensor

Optimization Process By Using Design Of Experiment (DOE) Method To Increase Performance Of Fiber Optic Glucose Sensor

This study also included the approach of design of Experimental (DOE) where the analysis has carried out to indicate the validity of the process. Therefore, the new approach of DOE will be used. The DOE is a method where the team identifies the parameters that can be controlled and the parameter factors it wishes to investigate. The team then designs, conducts, and analyses experiments to help determine the parameter set points to achieve robust performance (Karl T. Ulrich, Steven D. Eppinger 2011). The Design of Experiments (DOE) has depicted a noteworthy commitment in science and innovation since the time Sir Ronald Aylmer Fisher presented this idea in the 1940s (Belavendram 2011). Therefore, it was broadly utilized by specialists and researchers for item outline and improvement and also handles advancement and change. The idea of test configuration is to yield the most data from the least keeps running of a trial. By applying exploratory outlines in a fiber optic glucose sensor, the improvement lead time and expense could significantly be diminished. Additionally, it would likewise enhance the procedures and items to perform better and accomplish more prominent unwavering quality.
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