Optimization design expert software

The D esign O ptimization E xpert S ystem DOES provides structure, integrity, security, visibility, auditability, and reusability to a diverse range of model-based design optimization activities within an enterprise. It enhances the synergy between human and artificial intelligence. Design Expert 12 - Student version for Mac and Windows prices starting from Customer Type select: please select Student.

Operating System select: please select Mac Windows. Type of License select: please select Single User, rental license 12 months run-time Single User, rental license 6 months run-time. Arguments for Design Expert: Rotatable 3D graphics, interactive contour diagrams isolines , best ternary representation, All classical experimental designs, d-optimal for screening and i-optimal for RSM, Latest split-plot designs and definitive screening designs, Best optimizing function multiple target variables and optimizing with respect to factor settings , Fitted function exported as a formula to Excel Error propagation propagation of error helps you to find robust settings Indispensable for formulation optimizing.

Recommended products Minitab Minitab is a powerful statistical software for quality improvement and Six Sigma processes. Design-Expert — Best of breed in Design of Experiments! Powerful, Yet Easy to Use Designed as a specialized DOE software package, Design-Expert offers features for ease of use, functionality and power that you won't find in general statistical packages. Set flags to reveal the predicted response at any location Drag 2-D contours using your mouse Rotate 3-D graphics and see projected 2-D contours Edit colors, text and more to produce professional reports See all effects on one graph with trace and perturbation plots Plot the standard error of your design on any graph type contour, 3-D, etc.

Greatly improved context-sensitive help provides immediate response Better guidance helps you choose the best model A bonus help section provides "quick start" advice to novices Special user tips offer hints not normally found in help systems Further Information StatEase Inc. Important changes and improvements in Design Expert: Hard-to-change factors handled via split plots Two-level, general and optimal factorial split-plot designs : Make it far easier as a practical matter to experiment when some factors cannot be easily randomized.

Half-normal selection of effects from split-plot experiments with test matrices that are balanced and orthogonal : The vital effects, both whole-plot created for the hard-to-change factors and sub-plot factors that can be run in random order , become apparent at a glance! Power calculated for split plots versus the alternative of complete randomization : See how accommodation of hard-to-change factors degrades the ability to detect certain effects.

Other new design capabilities Definitive screening designs : If you want to cull out the vital few from many numeric process factors, this fractional three-level DOE choice resolves main effects clear of any two-factor interactions and squared terms see screen shot of correlation matrix - more on that later.

Multiple cycles improve the odds of finding multiple local optimums, some of which are higher in desirability than others.

Design-Expert then sorts the results from most desirable to least. Due to random starting conditions, your results are likely to be slightly different from those in the report above. The ramp display combines individual graphs for easier interpretation. The colored dot on each ramp reflects the factor setting or response prediction for that solution. The height of the dot shows how desirable it is.

View different solutions from the Solutions drop-down menu on the Factors tools ; cycle through some of them and watch the dots. They may move only very slightly from one solution to the next. However, if you look closely at temperature, you should find two distinct optimums, the first few near 90 degrees; further down the solution list, others near 80 degrees. The Solutions Toolbar provides three views of the same optimization. Click Report.

Solution to multiple response optimization — desirability bar graph. Select the Graphs tab to view a contour graph of the overall desirability and all of your responses. On the Factors Tool palette, right-click C:Catalyst. Make it the X2 axis. Temperature then becomes a constant factor at 90 degrees this level is picked automatically by the selected solution 1. You can also use the droplist on the Graphs toolbar to look at bigger versions of each plot, such as the desirability plot below.

Desirability graph after changing X2 axis to factor C. The screen shot above is a graph displaying graduated colors — cool blue for lower desirability and warm yellow for higher. Design-Expert sets a flag at the optimal point as selected by the button bar at the top , number 1 is selected in the screenshot above.

To view a response associated with the desirability, select the desired Response from its droplist. Take a look at the Conversion plot. Right-click over this graph and choose Graph Preferences. Then go to Surface Graphs and click Show 2D grid lines. To get just what you want on the flag, right-click it again and select Edit Info.

To look at the desirability surface in three dimensions, again select Desirability from the Graphs toolbar drop-down. Then, press 3D Surface. Next, right-click the graph and select Set rotation and change horizontal control to Press your Tab key or click the graph.

What a spectacular view! In other words, the solution is relatively robust to factor C. Right-click over your graph to bring up Graph preferences.

One way or another, please show your colleagues what Design-Expert does for pointing out the most desirable process factor combinations. Do this by pressing the Default button on Surface Graphs and any other Graph Preference screens you experimented on. Design-Expert offers a very high Graph resolution option.

Try this if you like, but you may find that the processing time taken to render this, particularly while rotating the 3D graph, can be a bit bothersome.

This, of course, depends on the speed of your computer and the graphics card capability. When you generated numerical optimization, you found an area of satisfactory solutions at a temperature of 90 degrees. To see a broader operating window, click the Graphical node. The requirements are essentially the same as in numerical optimization:.

For the first response — Conversion if not already entered , type in 80 for the Lower Limit. You need not enter a high limit for graphical optimization to function properly. The present study used BBD optimization to accurately measure response variables and create polynomial equations based on experimental results.

Appropriate statistical trials were run, the best fit model was chosen, and independent variables that could generate optimal responses were further determined.

Design Expert Version Heavy dependence on dependent variables has been reported for the adopted independent variables. For all of the response variables, the results were expressed as polynomial equations. By treating the third factor as constant, 3D graphs were used to describe the product parameter interaction. The F -value of the model is high Just 0.

When the values are higher than 0. A model contraction is needed to improve the model if it includes a large number of insignificant model expressions.

This is not necessary in this case since most of them are less than 0. The 7. In order to get an acceptable model, there must be an insignificant lack of fit. The 0. The signal-to-noise ratio is measured by adequate precision. It is preferable to have a ratio of more than four. Our signal-to-noise ratio of The design area can be navigated using this model.

For this model, the polynomial equation obtained is as follows:. Response predictions for the given levels of each factor can be calculated using the equation in terms of coded factors. The main effects on particle size in the above regression equations are A , B , and C. As can be seen from the equation and Fig.

This could be due to a higher amount of polymer causing increased viscosity which was in acceptance according to the literature [ 20 , 21 ]. The formulations have particle sizes ranging from The zeta potential is measured to determine the surface charge of the nanoparticles.

Table 4 shows the individual zeta potential and PDI values for each formulation. Within the ranges of For all the nanoparticles, it was discovered that they have a positive charge value, with the formulation F2 having the highest charge value of Advanced techniques such as TEM were used to perform microscopic analysis of the optimized formulation.

The particles were found to be spherical with a uniform size range, confirming their size range within the nano-limit in Fig. The The lack of fit has an F -value of 0. The predicted R 2 of 0.

According to the equation above, chitosan concentration positively affects entrapment efficiency, as shown in Fig.

Furthermore, the coefficients A and AB were positive, indicating that entrapment efficiency will increase along with an increase in the concentration of chitosan and the interaction between two polymers.

The optimized nano-formulation was sensibly selected based on measures such as particle size minimization and maximum entrapment efficiency. Independent factors were derived from statistical and graphical analysis based on the desired range of response values. The selected formulation had 0. The experimental batch of the checkpoint utilizing the projected independent factors was developed and characterized for the response variables.

The projected results and the obtained result average particle size As shown in the figure, mesalamine was successfully loaded onto chitosan-CMI nanoparticles. The carboxylate group COO- is responsible for the above peaks by the anti-symmetric and symmetric extending modes, which are strong evidence of the addition of inulin to carboxymethyl groups [ 26 ].

At comparable wave numbers, these peaks appeared clearly in the chitosan-CMI nanoparticle spectrum. The drug peak was not very projecting in the formulation because it could be available in the polymer matrix as molecular dispersion. The FTIR findings indicate that the polymer and the drug were not chemically incompatible. Negligible drug release in the acidic media may be due to the enzyme-sensitive polymer inulin, which was adsorbed on the nanoparticle surface, and minimal hydration of the formulation.

At higher pH, chitosan present in the nanoparticle forms an interpenetrating network that, when contacted with alkaline media, swells and forms a dense, rigid network, leading to increased diffusion path length through which leaching of the drug occurs slowly, showing a sustained release pattern [ 31 ]. Hence, it can be concluded that the prepared nanoparticles show better-sustained release behavior compared to conventional marketed tablets.

In order to select the optimal fit model and regression coefficient, in vitro release data was incorporated into different mathematical models, including zero-order, first order, Higuchi Matrix, Korsemeyer-Peppas and Hixson Crowell.

Table 6 displays the results obtained In the case of Higuchi and Korsemeyer-Peppas, the regression coefficient R 2 finding of 0. The n value in the optimized formulation is 0. As a result, it is concluded that the optimized formulation used a non-Fickian release pattern, in which release was controlled by means of diffusion and swelling, as described by the Higuchi and Korsemeyer-Peppas models.

The results obtained from the ex vivo mucoadhesive study are presented in Fig. The result showed that the mucoadhesion of chitosan-CMI nanoparticle with colonic mucosa is due to the ionic association among the positively charged chitosan and negatively charged sialic acid of mucin facilitates the attraction of nano-formulation with the mucus layer of colonic epithelium, resulting in mucoadhesion.

The interaction between the chitosan-CMI nanoparticles and the colonic mucosa was confirmed using fluorescence microscopy, elucidating nanoparticle binding sites all over the body. A Fluorescent image of colonic mucosa without formulation.

B Fluorescent image of colonic mucosa showing mucoadhesion of optimized Nanoparticulate formulation. Entrapment efficiency and particle size are critical considerations for developing effective therapeutic drug delivery systems to ensure that the desired amount of drugs is delivered to the target site for the optimum therapeutic response.

These attributes can be managed during the initial phase of development by analyzing some formulation and process parameters [ 32 , 33 ]. The results of the BBD were analyzed, and the utility of this statistical design resulted in a significant amount of data being provided to optimize the formulation.

All responses were adjusted to fit the quadratic model, and ANOVA was used to check for model compatibility, lack of fit, and regression coefficients R 2.

Each response should be connected to the others to optimize responses, and a most supportive zone should be required for each response to eliminate bias. Many types of literature have supported the desirability function to optimize multiple responses [ 34 , 35 ]. All prominent drug peaks were detected in the optimized formulation, indicating that the drug and polymers used in the formulation have no chemical interaction.

DSC study also confirms that there was no drug-polymers interaction among the formulation. This indicates that mesalamine was converted to an amorphous form. The rationale of the observation may be the entrapment of the drug in the polymer matrix [ 37 ]. Diffusion is slowed, which encourages the formation of large nanodroplets at the interface [ 38 ].

These findings support previous research [ 39 ], which found that the higher the polymer concentration in the sample, the higher the frequency of collision, the higher the concentration of semi-formed particles, and the larger the overall size of the nanoparticles.

The morphological study performed with the help of TEM showed that the particles were nearly spheroidal in shape and were all approximately the same in size. The zeta potential of nanoparticles is affected by the polyelectrolyte ratio, with a decrease in chitosan viscosity causing structural instability and thus lowering the zeta potential [ 40 ].