Desirability-based optimization and its sensitivity analysis for the perindopril and its impurities analysis in a microemulsion LC system

In the current paper the application of multiobjective optimization (MOOP) technique, via Derringer's desirability function, to a microemulsion liquid chromatographic (MELC) method is described. Chromatographic separation of perindopril tert-butylamine and its four impurities was selected as the case study. Central composite design (CCD) with fractional factorial design, ± 0.5 α star design and four replications in central point was applied for a response surface study, in order to examine in depth the effects of the most important factors. As factors that influence the system mostly (i) content of ethyl acetate and (ii) butyl acetate in composite internal phase, (iii) content of sodium dodecyl sulfate (surfactant) and (iv) n-butanol (co-surfactant), as well as (v) pH of the mobile phase were selected. Retention factor of (a) perindoprilat and (b) impurity Y 31 and (c) resolution factor for impurities Y 32 and 33 were chosen for simultaneous optimization. By adjustment of the importance coefficients and weights, according to defined objectives, the optimal mobile phase composition was predicted to be: 0.24% w/v butyl acetate, 0.3% w/v ethyl acetate, 2% w/v SDS, 7.75% w/v n-butanol and pH of the mobile phase 3.7. The sensitivity analysis of desirability function for these optimal conditions was conducted for the first time in LC separations, by applying a sensitivity procedure. The performed sensitivity analysis confirmed that the higher overall desirability does not necessarily mean a better solution. The accuracy of prediction might be affected if the optimal levels of input variables, achieved from several design points, end up with equal settings and different corresponding overall desirability. In our study this was not the issue, which confirmed the adequacy of predicted optimum.     

A new and efficient variable selection algorithm based on ant colony optimization. Applications to near infrared spectroscopy/partial least-squares analysis

A new variable selection algorithm is described, based on ant colony optimization (ACO). The algorithm aim is to choose, from a large number of available spectral wavelengths, those relevant to the estimation of analyte concentrations or sample properties when spectroscopic analysis is combined with multivariate calibration techniques such as partial least-squares (PLS) regression. The new algorithm employs the concept of cooperative pheromone accumulation, which is typical of ACO selection methods, and optimizes PLS models using a pre-defined number of variables, employing a Monte Carlo approach to discard irrelevant sensors. The performance has been tested on a simulated system, where it shows a significant superiority over other commonly employed selection methods, such as genetic algorithms. Several near infrared spectroscopic experimental data sets have been subjected to the present ACO algorithm, with PLS leading to improved analytical figures of merit upon wavelength selection. The method could be helpful in other chemometric activities such as classification or quantitative structure-activity relationship (QSAR) problems.     

Multiobjective optimization scheme for industrial synthesis gas sweetening plant in GTL process

In industrial amine plants the optimized operating conditions are obtained from the conclusion of occurred events and challenges that are normal in the working units. For the sake of reducing the costs, time consuming, and preventing unsuitable accidents, the optimization could be performed by a computer program. In this paper, simulation and parameter analysis of amine plant is performed at first. The optimization of this unit is studied using Non-Dominated Sorting Genetic Algorithm-II in order to produce sweet gas with CO 2 mole percentage less than 2.0% and H 2 S concentration less than 10 ppm for application in Fischer-Tropsch synthesis. The simulation of the plant in HYSYS v.3.1 software has been linked with MATLAB code for real-parameter NSGA-II to simulate and optimize the amine process. Three scenarios are selected to cover the effect of (DEA/MDEA) mass composition percent ratio at amine solution on objective functions. Results show that sour gas temperature and pressure of 33.98 ? C and 14.96 bar, DEA/CO 2 molar flow ratio of 12.58, regeneration gas temperature and pressure of 94.92 ? C and 3.0 bar, regenerator pressure of 1.53 bar, and ratio of DEA/MDEA = 20%/10% are the best values for minimizing plant energy consumption, amine circulation rate, and carbon dioxide recovery.     

茂铁基二氢吡唑类衍生物的液相色谱分离研究

本文利用梯度洗脱进行溶剂强度的优化,确定分离一组化合物的最佳二元流动相体系,即甲醇-水、四氢呋喃-水和乙腈-水,将这三种二元流动相的最佳组成作为溶剂选择性三角形的三个顶点,利用ORM法对流动相进行优化选择,得到流动相的最佳组成和配比为V乙腈:V四氢呋喃:V水=33.75:33:33.25,有效地分离了六种二茂铁取代二氢...     

乙醇-盐酸-水体系中溶胶-凝胶法制备纳米TiO2薄膜的研究

以Ti (OBu)4为前驱体,设计单因素优选实验,通过溶胶凝胶-浸渍提拉法在玻璃基片上制备TiO2纳米薄膜,研究了无水乙醇、浓盐酸、超纯水对其结构和性能的影响,获得了纳米TiO2薄膜的最佳制备条件.     

Study on tribological behaviors of short glass fiber and polytetrafluoroethylene reinforced polycarbonate composites via a d‐optimal mixture design

This study analyzed variations of tribological behaviors that depend on the injection molding techniques during the blending of short glass fiber (SGF) and polytetrafluoroethylene (PTFE) reinforced polycarbonate (PC) composites. The proposed planning of blending experiments is to use a D‐optimal mixture design (DMD). The tribological behaviors of friction coefficient and wear mass loss were selected for discussion. Nine experimental runs, based on a DMD method, utilized to train the back‐propagation neural network (BPNN) and then the simulated annealing algorithm (SAA) approach is applied to search for an optimal mixture ratio setting. In addition, the result of BPNN integrating SAA was also compared with response surface methodology (RSM) approach. The results of confirmation experiment show that DMD, RSM, and BPNN integrating SAA method are effective tools for the optimization of reinforced process. Furthermore, the scanning electron microscope (SEM) images show that the abundant debris are peeled off from the matrix materials and predominant delamination mechanisms and plastic deformation are shown on the worn surface after tribological behavior tests. Copyright © 2010 John Wiley & Sons, Ltd.     

A hybrid shuffled complex evolution approach based on differential evolution for unconstrained optimization

Numerous optimization methods have been proposed for the solution of the unconstrained optimization problems, such as mathematical programming methods, stochastic global optimization approaches, and metaheuristics. In this paper, a metaheuristic algorithm called Modified Shuffled Complex Evolution (MSCE) is proposed, where an adaptation of the Downhill Simplex search strategy combined with the differential evolution method is proposed. The efficiency of the new method is analyzed in terms of the mean performance and computational time, in comparison with the genetic algorithm using floating-point representation (GAF) and the classical shuffled complex evolution (SCE-UA) algorithm using six benchmark optimization functions. Simulation results and the comparisons with SCE-UA and GAF indicate that the MSCE improves the search performance on the five benchmark functions of six tested functions.     

Three-term conjugate gradient method for the convex optimization problem over the fixed point set of a nonexpansive mapping

Many constrained sets in problems such as signal processing and optimal control can be represented as a fixed point set of a certain nonexpansive mapping, and a number of iterative algorithms have been presented for solving a convex optimization problem over a fixed point set. This paper presents a novel gradient method with a three-term conjugate gradient direction that is used to accelerate conjugate gradient methods for solving unconstrained optimization problems. It is guaranteed that the algorithm strongly converges to the solution to the problem under the standard assumptions. Numerical comparisons with the existing gradient methods demonstrate the effectiveness and fast convergence of this algorithm.     

Convergence analysis and performance of the extended artificial physics optimization algorithm

This paper presents extended artificial physics optimization (EAPO), a population-based, stochastic, evolutionary algorithm (EA) for multidimensional search and optimization. EAPO extends the physicomimetics-based Artificial Physics Optimization (APO) algorithm by including each individual’s best fitness history. Including the history improves EAPO’s search capability compared to APO. EAPO and APO invoke a gravitational metaphor in which the force of gravity may be attractive or repulsive, the aggregate effect of which is to move individuals toward local and global optima. A proof of convergence is presented that reveals the conditions under which EAPO is guaranteed to converge. Discrete-time linear system theory is used to develop a second-order difference equation for an individual’s stochastic position vector as a function of time step. Stable solutions require eigenvalues inside the unit circle, leading to explicit convergence criteria relating the run parameters {m_i, w, G}. EAPO is tested against several benchmark functions with excellent results. The algorithm converges more quickly than APO and with better diversity.     

The role of crossover operator in the genetic optimization of magnetic models

The Ising model, introduced almost 100 years ago by Wilhelm Lenz and Ernst Ising, is the formalism still popular as a tool to describe magnetic properties of a wide class of materials. Among many issues which arise when using this model there exist problems related to the process of finding minimum energy of the system. Since these problems are NP-hard, optimizations can either be performed for some approximated cases or be the subject of global optimization techniques. In this paper we present an analysis of the effect of different crossover operators on the efficiency of genetic algorithm used to minimize energy in the Ising model. Although it is not a benchmark tool, we hope it may be interesting as a testing tool.