Prediction of GC retention values under various column temperature conditions from temperature programmed data

A numerical approach has been developed for the correlation of retention limes (total retention lime) with temperature in gas chromatography, which allows the calculation of retention parameters including retention index from data acquired under two or more different temperature program conditions. By using this procedure the optimization of temperature condition can be further achieved, especially when a temperature-programmed run is the most suitable mode in the preliminary development of an analytical method for the analysis of an unknown sample.

Three-Dimensional Semiautomatic Road Extraction From a High-Resolution Aerial Image by Dynamic-Programming Optimization in the Object Space

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multi-choice mixed integer goal programming optimization for real problems in a sugar and ethanol milling company

Goal Programming (GP) is an important analytical approach devised to solve many realworld problems. The first GP model is known as Weighted Goal Programming (WGP). However, Multi-Choice Aspirations Level (MCAL) problems cannot be solved by current GP techniques. In this paper, we propose a Multi-Choice Mixed Integer Goal Programming model (MCMI-GP) for the aggregate production planning of a Brazilian sugar and ethanol milling company. The MC-MIGP model was based on traditional selection and process methods for the design of lots, representing the production system of sugar, alcohol, molasses and derivatives. The research covers decisions on the agricultural and cutting stages, sugarcane loading and transportation by suppliers and, especially, energy cogeneration decisions; that is, the choice of production process, including storage stages and distribution. The MCMIGP allows decision-makers to set multiple aspiration levels for their problems in which the more/higher, the better and the less/lower, the better in the aspiration levels are addressed. An application of the proposed model for real problems in a Brazilian sugar and ethanol mill was conducted; producing interesting results that are herein reported and commented upon. Also, it was made a comparison between MCMI GP and WGP models using these real cases. © 2013 Elsevier Inc.

A model for linear programming optimization of I/O-bound programs /

"June, 1969."

Resolution prediction and optimization of temperature programme in comprehensive two-dimensional gas chromatography

A model is developed for predicting the resolution of interested component pair and calculating the optimum temperature programming condition in the comprehensive two-dimensional gas chromatography (GC x GC). Based on at least three isothermal runs, retention times and the peak widths at half-height on both dimensions are predicted for any kind of linear temperature-programmed run on the first dimension and isothermal runs on the second dimension. The calculation of the optimum temperature programming condition is based on the prediction of the resolution of "difficult-to-separate components" in a given mixture. The resolution of all the neighboring peaks on the first dimension is obtained by the predicted retention time and peak width on the first dimension, the resolution on the second dimension is calculated only for the adjacent components with un-enough resolution on the first dimension and eluted within a same modulation period on the second dimension. The optimum temperature programming condition is acquired when the resolutions of all components of interest by GC x GC separation meet the analytical requirement and the analysis time is the shortest. The validity of the model has been proven by using it to predict and optimize GC x GC temperature programming condition of an alkylpyridine mixture. (c) 2005 Elsevier B.V. All rights reserved.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Optimization of apodization functions in terahertz transient spectrometry

A quadratic programming optimization procedure for designing asymmetric apodization windows tailored to the shape of time-domain sample waveforms recorded using a terahertz transient spectrometer is proposed. By artificially degrading the waveforms, the performance of the designed window in both the time and the frequency domains is compared with that of conventional rectangular, triangular (Mertz), and Hamming windows. Examples of window optimization assuming Gaussian functions as the building elements of the apodization window are provided. The formulation is sufficiently general to accommodate other basis functions. (C) 2007 Optical Society of America

Otimização topológica de estruturas termoelásticas tridimensionais

This work presents an optimization technique based on structural topology optimization methods, TOM, designed to solve problems of thermoelasticity 3D. The presented approach is based on the adjoint method of sensitivity analysis unified design and is intended to loosely coupled thermomechanical problems. The technique makes use of analytical expressions of sensitivities, enabling a reduction in the computational cost through the use of a coupled field adjoint equation, defined in terms the of temperature and displacement fields. The TOM used is based on the material aproach. Thus, to make the domain is composed of a continuous distribution of material, enabling the use of classical models in nonlinear programming optimization problem, the microstructure is considered as a porous medium and its constitutive equation is a function only of the homogenized relative density of the material. In this approach, the actual properties of materials with intermediate densities are penalized based on an artificial microstructure model based on the SIMP (Solid Isotropic Material with Penalty). To circumvent problems chessboard and reduce dependence on layout in relation to the final optimal initial mesh, caused by problems of numerical instability, restrictions on components of the gradient of relative densities were applied. The optimization problem is solved by applying the augmented Lagrangian method, the solution being obtained by applying the finite element method of Galerkin, the process of approximation using the finite element Tetra4. This element has the ability to interpolate both the relative density and the displacement components and temperature. As for the definition of the problem, the heat load is assumed in steady state, i.e., the effects of conduction and convection of heat does not vary with time. The mechanical load is assumed static and distributed

Multiobjective 0-1 integer programming for the use of sugarcane residual biomass in energy cogeneration

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Heterogeneity correction in the construction of optimized planning in radiotherapy using linear programming

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Cogeneration system design optimization

Cogeneration system design deals with several parameters in the synthesis phase, where not only a thermal cycle must be indicated but the general arrangement, type, capacity and number of machines need to be defined. This problem is not trivial because many parameters are considered as goals in the project. An optimization technique that considers costs and revenues, reliability, pollutant emissions and exergetic efficiency as goals to be reached in the synthesis phase of a cogeneration system design process is presented. A discussion of appropriated values and the results for a pulp and paper plant integration to a cogeneration system are shown in order to illustrate the proposed methodology.

A mixed-integer linear programming approach for optimal type, size and allocation of distributed generation in radial distribution systems

This paper presents a mixed-integer linear programming approach to solving the problem of optimal type, size and allocation of distributed generators (DGs) in radial distribution systems. In the proposed formulation, (a) the steady-state operation of the radial distribution system, considering different load levels, is modeled through linear expressions; (b) different types of DGs are represented by their capability curves; (c) the short-circuit current capacity of the circuits is modeled through linear expressions; and (d) different topologies of the radial distribution system are considered. The objective function minimizes the annualized investment and operation costs. The use of a mixed-integer linear formulation guarantees convergence to optimality using existing optimization software. The results of one test system are presented in order to show the accuracy as well as the efficiency of the proposed solution technique.© 2012 Elsevier B.V. All rights reserved.

Desenvolvimento e validação de método de análise por cromatografia gasosa para metabólitos polares de folhas de cana-de-açúcar e aplicação na avaliação da influência do teor de 'CO IND. 2' atmosférico na composição de metabólitos polares de cana-de-açucar

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Thermoplastic polyesters and Co-polyesters derived from vegetable oil: synthesis and optimization of melt polycondensation for medium and long chain poly(omega-hydroxyfatty acid)s and their ester derivatives

The synthesis of a series of omega-hydroxyfatty acid (omega-OHFA) monomers and their methyl ester derivatives (Me-omega-OHFA) from mono-unsaturated fatty acids and alcohols via ozonolysis-reduction/crossmetathesis reactions is described. Melt polycondensation of the monomers yielded thermoplastic poly(omega-hydroxyfatty acid)s [-(CH2)(n)-COO-](x) with medium (n = 8 and 12) and long (n = 17) repeating monomer units. The omega-OHFAs and Me-omega-OHFAs were all obtained in good yield (>= 80%) and purity (>= 97%) as established by H-1 NMR, Fourier Transform infra-red spectroscopy (FT-IR), mass spectroscopy (ESI-MS) and high performance liquid chromatography (HPLC) analyses. The average molecular size (M-n) and distribution (PDI) of the poly(omega-hydroxyfatty acid)s (P(omega-OHFA)s) and poly(omega-hydroxyfatty ester) s (P(Me-omega-OHFA) s) as determined by GPC varied with organo-metallic Ti(IV) isopropoxide [Ti(OiPr)(4)] polycondensation catalyst amount, reaction time and temperature. An optimization of the polymerization process provided P(omega-OHFA) s and P(Me-omega-OHFA) s with M-n and PDI values desirable for high end applications. Co-polymerization of the long chain (n = 12) and medium chain (n = 8) Me-omega-OHFAs by melt polycondensation yielded poly(omega-hydroxy tridecanoate/omega-hydroxy nonanoate) random co-polyesters (M-n = 11000- 18500 g mol(-1)) with varying molar compositions.

Chromatographic methods for determination of nimesulide and for stability studies

Simple and rapid HPLC, GC, and TLC procedures have been developed for detection and determination of nimesulide, a non-pharmacopeial drug, in preformulation and dosage form. Use of these techniques has enabled separation of impurities and the precursor in the bulk material and in formulations. Isocratic reversed-phase HPLC was performed on a C-18 column with methanol-water-acetic acid, 67:32:1 (v/v), as mobile phase and UV detection at 230 nm. Calibration curves were linear over the concentration range 100-1000 mug mL(-1) with a good correlation coefficient (0.9993) and a coefficient of variation of 1.5%. Gas chromatography was performed on an OV-17 packed column with temperature programming and flame-ionization detection. The lower limit of determination by HPLC and GC was 4 ppm. Thin-layer chromatography of nimesulide was performed on silica gel G with toluene-ethyl acetate, 8:2, as mobile phase. Stability testing of the drug was performed under different temperature, humidity, and UV-radiation conditions.