Simultaneous Optimization Technique Research Articles

초공동(超空洞) 하의 수중 주행체 캐비데이터 형상최적설계

When a projectile travels at high speed underwater, supercavitating flow arises, in which a huge cavity is generated behind the projectile so that only the nose, i.e., the cavitator, of the projectile is wetted, while the rest of it should be surrounded by the cavity. In that case, the projectile can achieve very high speed due to the reduced drag. Furthermore if the nose of the body is shaped properly, the attendant pressure drag can be maintained at a very low value, so that the overall drag is also reduced dramatically. In this study, shape optimization technique is employed to determine the optimum cavitator shape for minimum drag, given certain operating conditions. Shape optimization technique is also used to solve the potential flow problem for any given cavitator, which is a free boundary value problem having the cavity shape as unknown a priori. Analytical sensitivities are derived for various shape parameters in order to implement a gradient-based optimization algorithm. Simultaneous optimization technique is proposed for efficient cavitator shape optimization, in which the cavity and cavitator shape are determined in a single optimization routine.

Neural network based optimization of drug formulations

A pharmaceutical formulation is composed of several formulation factors and process variables. Several responses relating to the effectiveness, usefulness, stability, as well as safety must be optimized simultaneously. Consequently, expertise and experience are required to design acceptable pharmaceutical formulations. A response surface method (RSM) has widely been used for selecting acceptable pharmaceutical formulations. However, prediction of pharmaceutical responses based on the second-order polynomial equation commonly used in an RSM, is often limited to low levels, resulting in poor estimations of optimal formulations. The purpose of this review is to describe the basic concept of the multi-objective simultaneous optimization technique, in which an artificial neural network (ANN) is incorporated. ANNs are being increasingly used in pharmaceutical research to predict the nonlinear relationship between causal factors and response variables. Superior function of the ANN approach was demonstrated by the optimization for typical numerical examples.

Simultaneous optimization based on artificial neural networks in ketoprofen hydrogel formula containing O-ethyl-3-butylcyclohexanol as percutaneous absorption enhancer

The influence of the amounts of additives including 1-O-ethyl-3-n-butylcyclohexanol (OEBC), diisopropyl adipate (DIA), and isopropanol (IPA) on the penetration rate (Rp) of ketoprofen from hydrogels through rat skin in vivo was investigated. Skin irritation evoked by the application of hydrogels was evaluated based on a microscopic observation of skin cross-sections. Both optimization techniques incorporating an artificial neural network (ANN) and a second-order polynomial regression analysis were applied to the optimization of ketoprofen hydrogel formulations. Findings indicated that the Rp and total irritation score (TIS) of the skin were predicted quantitatively as a function of quantities of OEBC, DIA, and IPA, employing ANN. In contrast, the prediction ability of the polynomial regression equation was somewhat poorer compared with that of ANN. The observed results of Rp and TIS in the optimal formulation coincided well with the predictions in the simultaneous optimization technique incorporating ANN. © 2001 Wiley-Liss, Inc. and the American Pharmaceutical Association J Pharm Sci 90:1004–1014, 2001

Formula optimization of theophylline controlled-release tablet based on artificial neural networks

Formulation of the controlled-release tablet containing theophylline was optimized based on the simultaneous optimization technique in which an artificial neural network (ANN) was incorporated. As model formulations, 16 kinds of theophylline tablets were prepared. The amounts of Controse, the mixture of hydroxypropylmethyl cellulose with lactose, cornstarch and compression pressure were selected as causal factors. The release profiles of theophylline were characterized as the sum of the fast and slow release fractions. The initial weight, the rate constant in the fast release fraction and the rate constant in the slow release fraction were estimated as release parameters. A set of release parameters and causal factors were used as tutorial data for ANN and analyzed using a computer. Based on the plasma concentration profiles of theophylline predicted by the pharmacokinetic analysis in humans, a desirable set of release parameters was provided. The simultaneous optimization was performed by minimizing the generalized distance between the predicted values of each response and the desirable one that was optimized individually. The optimization technique incorporating ANN showed a fairly good agreement between the observed values of release parameters and the predicted results.

Simultaneous Optimization of Machining Parameters for Dimensional Instability Control in Aero Gas Turbine Components Made of Inconel 718 Alloy

Aero gas turbine components made of Inconel 718 superalloy revealed significant dimensional instability after machining. The dimensional instability is a manifestation of alterations in residual stresses and microstructure, which are influenced by machining parameters. This paper presents a simultaneous optimization technique used to control the dimensional instability in turning operation. Empirical equations are established for predicting surface residual stresses, surface finish, dimensional instability and tool life using response surface methodology. Experimental results show a strong correlation between residual stresses and dimensional instability. A desirability function approach is used to optimize the multiple responses and machining parameters are derived for practical applications. Inconel 718 test specimen and jet engine components machined with optimal cutting parameters show dimensional instability within the acceptable tolerance band. [S1087-1357(00)01704-4]

Formula optimization based on artificial neural networks in transdermal drug delivery

The promoting effect of O-ethylmenthol (MET) on the percutaneous absorption of ketoprofen from alcoholic hydrogels was evaluated in rats in vitro and in vivo. Furthermore, a novel simultaneous optimization technique incorporating an artificial neural network (ANN) was applied to a design of a ketoprofen hydrogel containing MET. When a small quantity of MET (0.25–0.5%) was added to the hydrogels, the permeation of ketoprofen increased remarkably, compared with the control. On the other hand, little change in permeation was observed when small amounts of menthol were used (<1%), and at least 2% menthol was required to obtain a promoting efficiency comparable with 0.25% MET. The partitioning of ketoprofen from the hydrogel to the skin was improved by the addition of a small amount of MET, whereas the diffusivity of the drug was enhanced at higher concentration of MET (0.5–1%). For the optimization study, the amount of ethanol and MET were selected as causal factors. A rate of penetration (Rp) and lag time (tL) and total irritation score (TIS) were selected as response variables. A set of causal factors and response variables was used as tutorial data for ANN and fed into a computer. Nonlinear relationships between the causal factors and the response variables were represented well with the response surface predicted by ANN. The optimization of the ketoprofen hydrogel was performed according to the generalized distance function method. The observed results of Rp and TIS, which had a lot of influence on the effectiveness and safety, coincided well the predictions.

Artificial neural network as a novel method to optimize pharmaceutical formulations.

One of the difficulties in the quantitative approach to designing pharmaceutical formulations is the difficulty in understanding the relationship between causal factors and individual pharmaceutical responses. Another difficulty is desirable formulation for one property is not always desirable for the other characteristics. This is called a multi-objective simultaneous optimization problem. A response surface method (RSM) has proven to be a useful approach for selecting pharmaceutical formulations. However, prediction of pharmaceutical responses based on the second-order polynomial equation commonly used in RSM, is often limited to low levels, resulting in poor estimations of optimal formulations. The aim of this review is to describe the basic concept of the multi-objective simultaneous optimization technique in which an artificial neural network (ANN) is incorporated. ANNs are being increasingly used in pharmaceutical research to predict the non-linear relationship between causal factors and response variables. The usefulness and reliability of this ANN approach is demonstrated by the optimization for ketoprofen hydrogel ointment as a typical numerical example, in comparison with the results obtained with a classical RSM approach.

Integrated Design of Airframe and Stabilizing Controller in Wing-in-Ground Effect Aircraft.

Wing in-Ground effect aircrafts have gained much attention during these thirty years because of their high efficiency. The aircrafts require active stabilizing controllers for safety maneuvering, which cause difficulties in designmg. In this paper, we apply simultaneous optimization technique to design Wing-in-Ground aircraft. It is shown that the simultaneous optimization on parameters of aerodynamics design and of stabilizing controller can give superior design to conventional methods. An example is given to illustrate the procedure of design and the effectiveness of simultaneous optimization.

Multi-objective simultaneous optimization based on artificial neural network in a ketoprofen hydrogel formula containing O-ethylmenthol as a percutaneous absorption enhancer

The aim of this study was to apply a novel simultaneous optimization technique incorporating an artificial neural network (ANN) to a design of a ketoprofen hydrogel containing O-ethylmenthol (MET). For model formulae, 12 kinds of ketoprofen hydrogels were prepared. The amount of ethanol and MET were selected as causal factors. A percutaneous absorption study in vivo in rats was performed and irritation evoked on rat skin was microscopically judged after the end of the experiments. The rate of penetration ( R p), lag time ( t L) and total irritation score (TIS) were selected as response variables. A set of causal factors and response variables was used as tutorial data for ANN and fed into a computer. Nonlinear relationships between the causal factors and the release parameters were represented well with the response surface predicted by ANN. The optimization of the ketoprofen hydrogel was performed according to the generalized distance function method. The observed results of R p and TIS, which had a lot of influence on the effectiveness and safety, coincided well with the predictions. It was suggested that the multi-objective simultaneous optimization technique incorporating ANN was quite useful for optimizing pharmaceutical formulae when pharmaceutical responses were nonlinearly related to the formulae and process variables.

Multi-objective simultaneous optimization technique based on an artificial neural network in sustained release formulations

The aim of this study was to develop a multi-objective simultaneous optimization technique in which an artificial neural network (ANN) was incorporated. As model formulations, 18 kinds of Trapidil tablet were prepared. The amounts of microcrystalline cellulose, hydroxypropyl methylcellulose and compression pressure were selected as causal factors. In order to characterize the release profiles of Trapidil, the release order and the rate constant were estimated. A set of release parameters and causal factors was used as tutorial data for ANN and fed into a computer. Non-linear relationships between causal factors and the release parameters were represented well with the response surface of ANN. The simultaneous optimization of the sustained-release tablet containing Trapidil was performed by minimizing the generalized distance between the predicted values of each response and the optimized one that was obtained individually. The optimal formulations gave satisfactory release profiles, since the observed results coincided well with the predicted results. These findings demonstrate that a multi-objective optimization technique incorporating ANN is quite useful in the optimization of pharmaceutical formulations.

Formulation optimization of sustained-release tablet of chlorpheniramine maleate by means of extreme vertices design and simultaneous optimization technique.

Formulation optimization of sustained-release tablet of chlorpheniramine maleate by means of extreme vertices design and simultaneous optimization technique.

Simultaneous optimization for several characteristics concerning percutaneous absorption and skin damage of ketoprofen hydrogels containing d-limonene

A simultaneous optimization technique for multi-objective problems was applied for the designing of a formulation for a gel ointment of ketoprofen (KPF) containing d-limonene and ethanol as absorption enhancers. The generalized distance function was employed for dealing with all the responses simultaneously. Pharmacokinetic parameters of KPF percutaneously absorbed from model formulations were determined in rats as prime response variables. The skin damage evoked by each formulation was microscopically judged and graded as the response variable concerned with safety to the skin. These response variables were predicted by multiple regression equations composed of a combination of the concentrations of d-limonene and ethanol as formulation factors. The regression equations for response variables were assembled as a simultaneous optimization problem. Experimental results obtained for the optimum formulation agreed well with the predictions, suggesting the usefulness and reliability of the optimization techniques based on a generalized distance function in which the impartiality among the responses was considered.