Application of Optimization Methods to Solid Phase Analysis of Combustion Gases Desulphurization Products
Abstract The variations in calcium and sulphur concentrations measured along cross-sections of limestone-lime sulphated particles in scanning electron microscope (EDS microanalysis) and the computation of the sum of oxides CaO+SO 3 , ( s ) and the molar ratio CaO/SO 3 , ( r ) are essential to identifying the compounds which could appear. A method of estimating the phase of sulphation limestone/lime products is presented in this paper. The aim of this method is to find such component proportions which minimize the differences between the measured and the calculated values of s and r . The method is developed using the software package CAMOS and enables one to calculate automatically the component proportions for the given system. In the paper the CaS–CaSO 4 –CaO–CaCO 3 and CaS–CaSO 4 –CaO–Ca(OH) 2 systems are considered. Some experimental examples are also shown.
Computer Aided Multicriterion Optimization System in Use
In the paper software package called Computer Aided Multicriterion Optimization System (CAMOS) is briefly described. The system enables the designer to solve single and multicriterion optimization problems for nonlinear programming models with continuous, integer, discrete and mixed design variables. The system is designed to facilitate the interactive processes considering both input/output information arrangement and multicriterion decision making problems. Several engineering design examples are presented to show the advantages of the use of the system. These examples deal with design and optimization of helical springs, hydrostatic journal bearings, helical gearsets multiple clutch brakes. Multicriterion optimization models for all the examples are briefly described and the computer aided design sessions with CAMOS are presented. The structure of CAMOS and the subroutine which describes the design problem are arranged in the way that the analysis, optimization and decision making phases are one entity. The analysis and decision making phases can be supported by a graphical illustration of the problem formulation and the solution obtained. Both the system and problem oriented computer aided optimum design modules are coded in FORTRAN and prepared for an IBM PC/XT/AT.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.