Parallel Computing and Mathematical Optimization

Capacities of the largest new recovery boilers are steadily rising, and there is every reason to expect this trend to continue. However, the furnace designs for these large boilers have not been optimized and, in general, are based on semiheuristic rules and experience with smaller boilers. We present a multiobjective optimization code suitable for diverse optimization tasks and use it to dimension a high-capacity recovery boiler furnace. The objective was to find the furnace dimensions (width, depth, and height) that optimize eight performance criteria while satisfying additional inequality constraints. The optimization procedure was carried out in a fully automatic manner by means of the code, which is based on a genetic algorithm optimization method and a radial basis function network surrogate model. The code was coupled with a recovery boiler furnace computational fluid dynamics model that was used to obtain performance information on the individual furnace designs considered. The optimization code found numerous furnace geometries that deliver better performance than the base design, which was taken as a starting point. We propose one of these as a better design for the high-capacity recovery boiler. In particular, the proposed design reduces the number of liquor particles landing on the walls by 37%, the average carbon monoxide (CO) content at nose level by 81%, and the regions of high CO content at nose level by 78% from the values obtained with the base design. We show that optimizing the furnace design can significantly improve recovery boiler performance.

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Methods and Technologies of Parallel Computing for Mathematical Modeling of Stress-Strain State of Constructions Taking into Account Ductile Fracture

Journal of Automation and Information Sciences ◽

10.1615/jautomatinfscien.v46.i11.30 ◽

2014 ◽

Vol 46 (11) ◽

pp. 23-35 ◽

Cited By ~ 6

Author(s):

Elena A. Velikoivanenko ◽

Alexey S. Milenin ◽

Alexander V. Popov ◽

Vladimir A. Sidoruk ◽

Alexander N. Khimich

Keyword(s):

Mathematical Modeling ◽

Parallel Computing ◽

Ductile Fracture ◽

Strain State ◽

Stress Strain ◽

Stress Strain State

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Matlab and Parallel Computing

Image Processing & Communications ◽

10.2478/v10248-012-0048-5 ◽

2012 ◽

Vol 17 (4) ◽

pp. 207-216 ◽

Cited By ~ 5

Author(s):

Magdalena Szymczyk ◽

Piotr Szymczyk

Keyword(s):

Image Processing ◽

Signal Processing ◽

Parallel Computing ◽

Distributed Computing ◽

Control Systems ◽

High Performance ◽

Parallel Applications ◽

Process Simulations ◽

Key Features ◽

Financial Process

Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB parallel applications focused on using GPU processors for image processing.

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