Nonlinear Optimization Algorithm for Multivariate Optical Element Design

2002 ◽  
Vol 56 (4) ◽  
pp. 477-487 ◽  
Author(s):  
Olusola O. Soyemi ◽  
Frederick G. Haibach ◽  
Paul J. Gemperline ◽  
Michael L. Myrick
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Algorithm ◽  
Optical Computing ◽  
Chemical Species ◽  
Principal Component Regression ◽  
Principal Component ◽  
Optical Element ◽  
Optical Elements ◽  
Design Algorithm ◽  
Nonlinear Optimization Algorithm

A new algorithm for the design of optical computing filters for chemical analysis, otherwise known as multivariate optical elements (MOEs), is described. The approach is based on the nonlinear optimization of the MOE layer thicknesses to minimize the standard error in sample prediction for the chemical species of interest using a modified version of the Gauss–Newton nonlinear optimization algorithm. The design algorithm can either be initialized with random layer thicknesses or with layer thicknesses derived from spectral matching of a multivariate principal component regression (PCR) vector for the constituent of interest. The algorithm has been successfully tested by using it to design various MOEs for the determination of Bismarck Brown dye in a binary mixture of Crystal Violet and Bismarck Brown.

Use of Multivariate Analysis to Determine Temperature from Low-Resolution Infrared Spectra of Carbon Dioxide

2000 ◽  
Vol 54 (2) ◽  
pp. 197-201 ◽  
Author(s):  
Michael P. Szczepanski ◽  
Augustus W. Fountain
Keyword(s):  
Carbon Dioxide ◽  
Chemical Species ◽  
Principal Component Regression ◽  
Principal Component ◽  
Industrial Applications ◽  
Low Resolution ◽  
Band Shape ◽  
Temperature Spectra ◽  
Plume Temperature ◽  
Industrial Monitoring

The remote optical monitoring of gaseous contaminants is important for both military and industrial applications. An important parameter for quantifying chemical species and for predicting plume dynamics is the temperature. While in some industrial monitoring situations it may be practical to independently measure the temperature of stack emissions, for compliance monitoring and military chemical reconnaissance a remote optical means of estimating gas plume temperature is required. It was noticed that the band shape of low-resolution spectra of carbon dioxide in equilibrium with an exhaust plume was very sensitive to temperature. Spectra of carbon dioxide were acquired under controlled laboratory conditions in 5° increments from 20 to 200 °C. Various multivariate models were used to predict the temperature. It was found that partial least-squares (PLS) was unable to effectively model the simultaneous changes in amplitude and bandwidth with temperature. However, principal component regression (PCR) was found to be well correlated with temperature and allowed cross-validated prediction within 4% error.

Monitoring of Chemical Speciation of DEA – CO2 – Water System by Raman Spectroscopy

2015 ◽  
Vol 1113 ◽  
pp. 358-363 ◽  
Author(s):  
Muhammad Zubair Shahid ◽  
Humbul Suleman ◽  
Adulhalim Shah Maulud ◽  
Mohammad Azmi Bustam Khalil ◽  
Zakaria Man
Keyword(s):  
Raman Spectroscopy ◽  
Water System ◽  
Chemical Species ◽  
Principal Component Regression ◽  
Principal Component ◽  
Carbon Dioxide Separation ◽  
Key Factor ◽  
Kinetics Of ◽  
Respective Characteristic ◽  
Good Agreement

Carbon dioxide separation has gained immense importance since its detrimental effects towards our environment has been realized. Commercially, CO2has been captured by absorption in alkanolamines such as diethanolamine (DEA), since many years. The thermodynamics and kinetics of the process is a key factor towards its efficiency and significantly depends on its qualitative and quantitative speciation. In this work, the analysis of speciation for CO2loaded aqueous DEA has been performed by Raman spectroscopy. Experimentally determined CO2loading data and modified Kent Eisenberg equation was used to quantify the chemical species present. The speciation results were fitted with the respective characteristic Raman peaks of (CO3-, HCO3-, DEACOO-, DEA, DEA+, CO2) by Principal Component Regression (PCR). The fitted results showed good agreement with thermodynamically predicted chemical species.

Task dependent synthesis of some geometrical parameters of a robot mechanism

Robotica ◽  
1993 ◽  
Vol 11 (2) ◽  
pp. 167-171 ◽  
Author(s):  
Maks Oblak ◽  
Karl Gotlih
Keyword(s):  
Mathematical Model ◽  
Nonlinear Optimization ◽  
Optimization Algorithm ◽  
Kinematic Chain ◽  
Chain Structure ◽  
Geometrical Parameters ◽  
Task Space ◽  
End Effector ◽  
Optimal Mechanism ◽  
Nonlinear Optimization Algorithm

SUMMARYThis paper deals with the synthesis of a robot mechanism, which has an open kinematic chain structure. The aim of the synthesis is to find optimal mechanism link lengths and the elevation of the robot mechanism base, with respect to the arbitrary chosen task which is described in a task space.A mathematical model, which describes the problem and enables one to use a nonlinear optimization algorithm, was developed. The usefulness of the approach is demonstrated by the example of the Manutec r3 mechanism with a prescribed task for the robot's end-effector.

Determination of Chemical Species in MDEA – Carbon Dioxide – Water System by Raman Spectroscopy

2015 ◽  
Vol 1113 ◽  
pp. 261-266 ◽  
Author(s):  
Humbul Suleman ◽  
Muhammad Zubair Shahid ◽  
Abdulhalim Shah Maulud ◽  
Zakaria Man ◽  
Mohammad Azmi Bustam Khalil
Keyword(s):  
Carbon Dioxide ◽  
Raman Spectroscopy ◽  
Chemical Speciation ◽  
Water System ◽  
Chemical Species ◽  
Principal Component Regression ◽  
Principal Component ◽  
Carbon Dioxide Absorption

Alkanolamines based carbon dioxide absorption from flue gases remains the most industrially implemented technique. The effective design of absorbers and associated equipment requires robust thermodynamic and kinetic models thus, instigating research efforts in chemical speciation and characterization of CO2loaded alkanolamine solutions. In this study, the potential of Raman spectroscopy has been investigated to determine the in situ chemical speciation in MDEA – CO2– Water system. The Raman spectra have been fitted to thermodynamic values using principal component regression. Results are in good agreement for carbonate, bicarbonate, MDEA and protonated MDEA chemical species.

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