Rational and precise development of amorphous polymeric systems with dapsone by response surface methodology

The paper describes response surface methodology (RSM) based on design of experiments and analysis of variance (ANOVA) as a statistical design while developing a robust plasma transfer arc (PTA)coating process. Based on ANOVA, The relative important parameters with respect to surface at hardness values were identified in the Taguchi design, where they were further used in predictors. In addition, we applied three-dimensional graphs in RSM to develop a robust PTA response surface yielding the desired-better area of a treated layer. In this study, a quadratic polynomial with a Box-Behnken design is utilized. The results reveal that RSM provides the effective methods as compared to the traditional trial-and-error method for exploring the effects of controlled factors on response. A very good agreement was observed, as evidenced by R-squared value, 90%, between the predicted and the experimental data, and its error percent is found to be approximately 3.801% in the PTA-coating process. It is clear that RSM model demonstrated better accuracy in predicting surface hardness for PTA-coating process. Accordingly, RSM based on design of experiments was used as statistical PTA-coating design tools combined with the hardness model. Device zone optimization and yield enhancement have been demonstrated.

Nevirapine (NVP), used for the treatment of HIV/AIDS, exhibits unpredictable oral bioavailability, has a poor side effect profile and requires frequent dosing. Niosomes are novel drug delivery systems that have the potential to overcome these challenges. A thin layer hydration approach was used to produce niosomes and optimisation was undertaken using design of experiments (DoE) and response surface methodology (RSM) establish and identify parameters that may affect the manufacture of niosomes. The impact of cholesterol and surfactant content, hydration time and temperature on manufacture was investigated. Critical quality attributes (CQA) in respect of particle size (PS), entrapment efficiency (EE), polydispersity index (PDI) and the amount of NVP released at 48 hours was also assessed. The optimised niosome composition was identified and manufactured and the CQA characterised prior to placing the batch on stability for 12 weeks at 4±2 °C and 22±2 °C. The PS, PDI, EE and % NVP released at 48 h was 523.36±23.16 nm, 0.386±0.054, 96.8 % and 25.3 % for niosomes manufactured with Span® 20. Similarly, the parameters were 502.87±21.77 nm and 0.394±0.027, 98.0 % and 25.0 % for mean PS, PDI, EE and %NVP released at 48 h for Span® 80 niosomes. All characterisation was undertaken on the day of manufacture. In conclusion, a simple, cheap, rapid and precise method of manufacture of NVP niosomes was developed, validated and optimised using DoE and RSM and the product exhibited the target CQA.

Dry sliding wear performance of AA7075/MoS2 composite materials

An alternative cyclodextrin based formulation for oral anticancer drug exemestane: In vitro and cell culture studies

The preset response surface methodology (RSM) designs are commonly used in a wide range of process and design optimization applications. Although they offer ease of implementation and good performance, they are not sufficiently adaptive to reduce the required number of experiments and thus are not cost effective for applications with high cost of experimentation. We propose an efficient adaptive sequential methodology based on optimal design and experiments ranking for response surface optimization (O‐ASRSM) for industrial experiments with high experimentation cost, limited experimental resources, and requiring high design optimization performance. The proposed approach combines the concepts from optimal design of experiments, nonlinear optimization, and RSM. By using the information gained from the previous experiments, O‐ASRSM designs the subsequent experiment by simultaneously reducing the region of interest and by identifying factor combinations for new experiments. Given a given response target, O‐ASRSM identifies the input factor combination in less number of experiments than the classical single‐shot RSM designs. We conducted extensive simulated experiments involving quadratic and nonlinear response functions. The results show that the O‐ASRSM method outperforms the popular central composite design, the Box–Behnken design, and the optimal designs and is competitive with other sequential response surface methods in the literature. Furthermore, results indicate that O‐ASRSM's performance is robust with respect to the increasing number of factors. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACTThis study investigates the effects of three factors: (1) use of a mixture of two different grades of hydroxypropyl methylcellulose (HPMC), (2) apparent viscosity, and (3) tablet hardness on drug release profiles of extended-release matrix tablets. The lot-to-lot apparent viscosity difference of HPMC K15M on in vitro dissolution was also investigated. Four test formulations were made, each containing 10% of a very water-soluble active pharmaceutical ingredient (API), 32% HPMC K15M, or a mixture of HPMC K100LV and HPMC K100M, 56% diluents, and 2% lubricants. Each formulation was made at two hardness levels. A 23 full factorial design was used to study various combinations of the three factors using eight experiments conducted in a randomized order. Dissolution studies were performed in USP apparatus I. The values of t50% (time in which 50% drug is released) and tlag (lag time, the time taken by the matrix tablet edges to get hydrated and achieve a state of quasi-equilibrium before erosion and the advance of solvent front through the matrix occur) were calculated from each dissolution profile. The similarity factor (f2) was also calculated for each dissolution profile against the target dissolution profile. A simple Higuchi-type equation was used to analyze the drug release profiles. Statistical analysis using analysis of variance (ANOVA) and similarity factor (f2) values calculated from the data indicated no significant difference among the t50% values and dissolution profiles respectively for all formulations. Within the 3.3–6 kp hardness range investigated, dissolution rates were found to be independent of tablet hardness for all the formulations. Although significantly shorter lag times were observed for the tablets formulated with low- and high-viscosity HPMC mixtures in comparison to those containing a single grade of HPMC, this change had no significant impact on the overall dissolution profiles indicated by the similarity factor f2 values. From this study it can be concluded that lot-to-lot variability in apparent viscosity of HPMC should not be a concern in achieving similar dissolution profiles. Also, results indicated that within the viscosity range studied (12,000–19,500 cps) an HPMC mixture of two viscosity grades can be substituted for another HPMC grade if the apparent viscosity is comparable. Also, the drug release is diffusion-controlled and depends mostly on the viscosity of the gel layer formed.

In this paper, response surface methodology (RSM) designs and an artificial neural network (ANN) are used to obtain the optimal conditions for the oxy-combustion of a corn–rape blend. The ignition temperature (Te) and burnout index (Df) were selected as the responses to be optimised, while the CO2/O2 molar ratio, the total flow, and the proportion of rape in the blend were chosen as the influencing factors. For the RSM designs, complete, Box–Behnken, and central composite designs were performed to assess the experimental results. By applying the RSM, it was found that the principal effects of the three factors were statistically significant to compute both responses. Only the interactions of the factors on Df were successfully described by the Box–Behnken model, while the complete design model was adequate to describe such interactions on both responses. The central composite design was found to be inadequate to describe the factor interactions. Nevertheless, the three methods predicted the optimal conditions properly, due to the cancellation of net positive and negative errors in the mathematical adjustment. The ANN presented the highest regression coefficient of all methods tested and needed only 20 experiments to reach the best predictions, compared with the 32 experiments needed by the best RSM method. Hence, the ANN was found to be the most efficient model, in terms of good prediction ability and a low resource requirement. Finally, the optimum point was found to be a CO2/O2 molar ratio of 3.3, a total flow of 108 mL/min, and 61% of rape in the biomass blend.

A statistical modeling-optimization approach for efficiency photocatalytic degradation of textile azo dye using cerium-doped mesoporous ZnO: A central composite design in response surface methodology

A multi-period inventory management model for perishable products is developed with simulation approach. A single-product scenario is modelled under continuous and periodic review policies, considering Weibull demand, non-null deterministic lead time, a combination of lost sales and backorders, fixed product shelf life, and service constraints. Eight inputs, including managerial aspects and product characteristics, and six outputs, i.e. the minimum cost and the optimal operating leverages of the two policies, are evaluated using Design of Experiments. Discrete-time simulation is used to determine, for each configuration, the optimal operating leverages, namely target stock level, reorder period, and base stock, which minimise the total cost under service constraints. The outcomes were then analyzed through Analysis of Variance and Response Surface Methodology, to assess the impact of each input parameter on the responses. Results show that the optimal operating leverages mainly depend on demand features, some unitary costs (order issuing in particular), shelf life, and lost sales fraction. The shelf life turns out to be significant only if lower than a threshold value, which varies with the shape factor of the demand distribution: for shelf life values greater than the threshold, the products could be managed as non-perishable from an inventory point of view.

Piperine, a secondary metabolite compound, has a low solubility in water. In this study, through the formation of multicomponent crystals, glutamic acid is employed as a coformer to enhance the physicochemical characteristics and improve the dissolution rate of piperine. Solvent drop grinding was used to create multicomponent crystals using molar ratios of piperine and glutamic acid of 1:1 (F1), 1:2 (F2), and 2:1 (F3). Characterization of the solids’ properties was performed using X-ray diffraction (XRD) analysis, differential scanning calorimetry (DSC), Fourier transform infrared (FT-IR) spectroscopy, scanning electron microscopy (SEM), and dissolution profiles in accordance with the United States Pharmacopoeia (USP), facilitating a comparison with the physical mixture and pure piperine. XRD results showed a decrease in intensity, and DSC showed a decrease in the endothermic peak transition temperature and significant decreases in enthalpy value. The FT-IR spectra showed a change in wave numbers, and no new functional groups were formed. SEM revealed changes in particle morphology forming new crystal habits. When contrasted with the physical mixture and pure piperine, the dissolution profiles of multicomponent crystals F1, F2, and F3 demonstrate increased dissolution rates, F2 exhibiting a 1.57-fold increase, followed by 1.44-fold for F1 and 1.49-fold for F3. Multicomponent crystals F1, F2, and F3 show a better dissolution profile than the physical mixture and pure piperine, with dissolved substance percentages of 63.60±6.56, 68.59±2.53, 65.25±12.10, 44.91±10.95, and 43.58±8.92, respectively, after 60 minutes at 37±0.5 °C. The multicomponent crystals produced using solvent drop grinding exhibited enhanced physicochemical features and an increased dissolution rate. 

This study experimentally investigated the parameters such as dynamic hardening, inertia track cross-sectional area, decoupler hardness, and fluid volume affecting the damping properties of passive hydraulic engine mounts. Two levels were determined for these parameters, and the full factorial design of experiment (DoE) was created. According to the DoE, hydraulic engine mounts with various design configurations were assembled and tested. Based on the test results, a response surface was created in the Hyperstudy software, and the optimal parameters were obtained via the surface for the desired hydraulic mount performance. Another mount close to the optimum design configuration was then analyzed by the Hyperstudy, and the analysis results were confirmed with the test results. It was observed that the experimental and analysis results were consistent. As a result, it has been demonstrated that the mount parameters can be decided with the response surface generated with the DoE. So, both time and cost advantages can be achieved by eliminating trial and error in production.

In this work, experiments were carried out in line with Design of Experiments (DOE) standards to assess the performance and emission features of 5% graphene nanoparticles added linseed biodiesel. The engine was operated with the blends of B10, B20, and B30 with 5% graphene nano additives (designated as B10G5, B20G5, and B30G5). To find the parameter's optimum values, the Desirability Function approach (DFA), Swarm Salp single objective, Multi Objective Bat algorithm (MOBA), Response surface methodology (RSM) and D-optimal design approach were employed. Advanced machine learning (ML) techniques were employed to anticipate these characteristics. It was found that B20G5 had a better brake thermal efficiency (BTE), when compared to the other samples (and around 11% higher than diesel fuel at full load). The emissions of Carbon monoxide (CO) and Hydrocarbon (HC) were lower for B20G5 blended fuel than for diesel (Around 23.52% lower than diesel). In comparison to Response surface methodology (RSM), the overall coefficient of determination (R2) value using Artificial Neural Network (ANN) for was high. As a result, it was revealed that the ANN was typically better than the RSM in forecasting the various factors affecting the engine performance. The optimum outcomes were achieved by single objective (Salp Swarm algorithm) and multi-objective algorithms. According to multi-objective algorithm, a B20G5 nano additive biodiesel mix at its maximum Brake power (BP) produced the highest value of BTE with the lowest Nitrogen Oxides (NOx) emissions. The comparison shows that B20G5 can be used easily without making any modifications to engines.

This research study focused on the various aspects of tribological phenomenon using design of experiments approach, results are analyzed and compared to evaluate the gear performance. Though lot of research work had been carried out on the hydrodynamic gear under static loads, till date no one consider the parameter say stiffness, damping co-efficient, squeeze film effects and the pressure distribution at various crank angle under dynamic load conditions and its effects on inertia forces of gear pump. It covers sinusoidal load, journal speed, face width and mass flow rate with respect to variations in oil film thickness. Frictional test rig was used to measure the frictional force and oil film thickness at various crank angles for every three minutes at an interval of every 15 degree crank angle. The response surface methodology (RSM) analysis shows appreciable increases in respect of performances in mass flow rate, face width, speed and loading against the Taguchi design of experiment results whereas the film thickness is almost same in both RSM and Taguchi design of experiment results. Particular attention is given in this research exertion to learn how the variation in gear dimensions influences the characteristics of the hydrodynamic gear pump. Keywords: Hydrodynamic Gear Pump, Oil Film Thickness, Mathematical Modeling, Taghuchi and RSM, Eddycurrent Cap Sensor.

Characterization and optimization of eco-friendly gypsum materials using response surface methodology

In this paper, after modeling and analyzing a single-sided wound secondary linear synchronous motor, single- and multi-objective design optimizations of the motor are carried out to develop larger average thrust and lower thrust ripple than the prototype by using design of experiments (DOE) and response surface methodology (RSM) considering multiple responses. By defining the motor weight as another objective function, it is tried to prevent an increase in motor size and reduce its material consumption. The optimization results confirm that desirable improvements in thrust mean, thrust ripple and motor weight are achieved. Finally, a finite element analysis is employed to evaluate the effectiveness of the employed machine models used in thrust and force ripple calculations and subsequently to confirm the proposed optimization method.