Proton dissociation of aqueous organic acids studied by multivariate chemometrics

1995 ◽  
Vol 73 (12) ◽  
pp. 2170-2177 ◽  
Author(s):  
Roberto Aruga
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Organic Acids ◽  
Principal Component ◽  
Component Analysis ◽  
Acid Dissociation ◽  
Multivariate Techniques ◽  
Thermodynamic Quantities ◽  
Gibbs Function ◽  
Proton Dissociation

Thermodynamic data of proton dissociation of 75 organic acids belonging to four classes (protonated amines, aliphatic carboxylic acids, benzoic acids, phenols) have been processed by multivariate chemometric techniques. The variables consist of conventional thermodynamic quantities (Gibbs function, enthalpy, entropy) and of partial components of these quantities (internal and external, electrostatic and nonelectrostatic components). The above data refer to the aqueous medium, at 25 °C and I = 0 mol dm−3. The Gibbs function of deprotonation in the gas phase and the Hammett σ constant have also been considered. Multivariate techniques include Principal Component Analysis, Factor Analysis, and feature selection. Factor Analysis and related concepts have proved to be useful in defining the causes of differences in acid strengths and their respective importance. Keywords: acid dissociation data, chemometrics of; chemometrics of acid dissociation data; factor analysis of acid dissociation data; principal component analysis of acid dissociation data; thermodynamics of acid dissociation.

Study of Organizational Image as Competitive Differential for the Institution of Higher Education

2015 ◽  
Vol 3 (10) ◽  
pp. 157-168
Author(s):  
Fernando de Sousa Santana ◽  
Samuel Gonçalves Pinto ◽  
Juliana Rodrigues Pereira
Keyword(s):  
Higher Education ◽  
Principal Component Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Target Audience ◽  
Satisfaction Survey ◽  
Multivariate Techniques ◽  
Organizational Image

This article presents the results of a satisfaction survey carried out with a College located in Ponte Nova/MG-Brazil. The research in question, aimed to verify what are the factors that affect the formation of the image of a school of higher education, the perception of your target audience. Among the multivariate techniques used in this paper we can highlight the principal component analysis (PCA) and factor analysis (FA) aimed mainly to reduce a large amount of data to a smaller set, to convey as much information possible. The results achieved through the analysis of the variables obtained by the survey, can serve as a basis for establishing improvement targets for forming the image of an institution of higher education, because they represent the views of the main public that establishment.

Physical-oriented and machine learning-based emission modeling in a diesel compression ignition engine: Dimensionality reduction and regression

2022 ◽  
pp. 146808742110707
Author(s):  
Aran Mohammad ◽  
Reza Rezaei ◽  
Christopher Hayduk ◽  
Thaddaeus Delebinski ◽  
Saeid Shahpouri ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Factor Analysis ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Component Analysis ◽  
Data Driven ◽  
Support Vector ◽  
Emission Models ◽  
Emission Modeling

The development of internal combustion engines is affected by the exhaust gas emissions legislation and the striving to increase performance. This demands for engine-out emission models that can be used for engine optimization for real driving emission controls. The prediction capability of physically and data-driven engine-out emission models is influenced by the system inputs, which are specified by the user and can lead to an improved accuracy with increasing number of inputs. Thereby the occurrence of irrelevant inputs becomes more probable, which have a low functional relation to the emissions and can lead to overfitting. Alternatively, data-driven methods can be used to detect irrelevant and redundant inputs. In this work, thermodynamic states are modeled based on 772 stationary measured test bench data from a commercial vehicle diesel engine. Afterward, 37 measured and modeled variables are led into a data-driven dimensionality reduction. For this purpose, approaches of supervised learning, such as lasso regression and linear support vector machine, and unsupervised learning methods like principal component analysis and factor analysis are applied to select and extract the relevant features. The selected and extracted features are used for regression by the support vector machine and the feedforward neural network to model the NOx, CO, HC, and soot emissions. This enables an evaluation of the modeling accuracy as a result of the dimensionality reduction. Using the methods in this work, the 37 variables are reduced to 25, 22, 11, and 16 inputs for NOx, CO, HC, and soot emission modeling while maintaining the accuracy. The features selected using the lasso algorithm provide more accurate learning of the regression models than the extracted features through principal component analysis and factor analysis. This results in test errors RMSETe for modeling NOx, CO, HC, and soot emissions 19.22 ppm, 6.46 ppm, 1.29 ppm, and 0.06 FSN, respectively.

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