Prediction of thermodynamic properties of refrigerants using data mining

Küçüksille, Ecir Uğur; Selbaş, Reşat; Şencan, Arzu

doi:10.1016/j.enconman.2010.08.009

Cited by 29 publications

(15 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The F-measure is the harmonic mean of precision and recall. The equation of the F-measure is as follows: The comparison analysis by root mean squared error was also performed and described in Table III where n is the number of data patterns, y p,m indicates the predicted, t m,m is the measured value of one data point m and m m t , is the mean value of all measure data points [24].…”

Section: Resultsmentioning

confidence: 99%

Filter Based Feature Selection Methods for Prediction of Risks in Hepatitis Disease

Yıldırım¹

2015

IJMLC

View full text Add to dashboard Cite

Abstract-Recently, large amount of data is widely available in information systems and data mining has attracted a big attention to researchers to turn such data into useful knowledge. This implies the existence of low quality, unreliable, redundant and noisy data which negatively affect the process of observing knowledge and useful pattern. Therefore, researchers need relevant data from huge records using feature selection methods. Feature selection is the process of identifying the most relevant attributes and removing the redundant and irrelevant attributes. In this study, a comparison between filter based feature selection methods based on a well-known dataset (i.e., hepatitis dataset) was carried out and four classification algorithms were used to evaluate the performance of the algorithms. Among the algorithms, Naï ve Bayes and Decision I. INTRODUCTIONRecently, thanks to innovations of computer and information technologies, huge amounts of data can be obtained and stored in both scientific and business transactions. This amount of data implies low quality, unreliable, redundant and noisy data to observe useful patterns [1]. Therefore, researchers need relevant and highquality data from huge records using feature selection methods.Feature selection methods reduce the dimensionality of feature space, remove redundant, irrelevant or noisy data. It brings the immediate effects for application: speeding up a data mining algorithm, improving the data quality and the performance of data mining and increasing the comprehensibility of the mining results [2].In this study, the great interest of hepatitis disease was considered which is a serious health problem in the world and a comparative analysis of several filter based selection algorithms was carried out based on the performance of four classification algorithms for the prediction of disease risks [3]. The main aim of this study is to make contributions in the prediction of hepatitis disease for medical research and introduce a detailed and comprehensive comparison of Manuscript received November 5, 2014; revised February 20, 2015. Pinar Yildirim is with the Okan University, Istanbul, Turkey (e-mail: pinar.yildirim@ okan.edu.tr). popular filter based feature selection methods. II. FEATURE SELECTION METHODSSeveral feature selection methods have been introduced in the machine learning domain. The main aim of these techniques is to remove irrelevant or redundant features from the dataset. Feature selection methods have two categories: wrapper and filter. The wrapper evaluates and selects attributes based on accuracy estimates by the target learning algorithm. Using a certain learning algorithm, wrapper basically searches the feature space by omitting some features and testing the impact of feature omission on the prediction metrics. The feature that make significant difference in learning process implies it does matter and should be considered as a high quality feature. On the other hand, filter uses the general characteristics of data itself and work separatel...

show abstract

Section: Resultsmentioning

confidence: 99%

Filter Based Feature Selection Methods for Prediction of Risks in Hepatitis Disease

Yıldırım¹

2015

IJMLC

View full text Add to dashboard Cite

show abstract

“…This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level [16]. The results of this work show that DM can use for predicting accuracy of thermodynamic properties of refrigerants for every temperature and pressure [18]. Yu et al used a decision tree method for building energy demand modeling.…”

Section: Applications Of Data Mining Methods In the Energy System Applmentioning

confidence: 99%

Data Mining Method For Energy System Aplications

Selbaş¹,

Şencan²,

Küçüksille³

2011

Knowledge-Oriented Applications in Data Mining

View full text Add to dashboard Cite

“…E.U. Kucuksille et al (2009Kucuksille et al ( , 2011, using various data mining techniques, obtained simple equations for determining the enthalpy, entropy, specific volume, specific heat capacity, the coefficient of dynamic viscosity, thermal conductivity and density of refrigerants at saturation state. The formulas were developed for R134a, R404a, R407C and R410A.…”

Section: Thermodynamic and Thermokinetic Properties Of Refrigerantsmentioning

confidence: 99%

Functional Dependence of Thermodynamic and Thermokinetic Parameters of Refrigerants Used in Mine Air Refrigerators. Part 1 – Refrigerant R407C

Nowak

Życzkowski

Łuczak

2017

Archives of Mining Sciences

View full text Add to dashboard Cite

The authors of this article dealt with the issue of modeling the thermodynamic and thermokinetic properties (parameters) of refrigerants. The knowledge of these parameters is essential to design refrigeration equipment, to perform their energy efficiency analysis, or to compare the efficiency of air refrigerators using different refrigerants. One of the refrigerants used in mine air compression refrigerators is R407C. For this refrigerant, 23 dependencies were developed, determining its thermodynamic and thermokinetic parameters in the states of saturated liquid, dry saturated vapour, superheated vapor, subcooled liquid, and in the two-phase region. The created formulas have been presented in Tables 2, 5, 8, 10 and 12, respectively. It should be noted that the scope of application of these formulas is wider than the range of changes of that refrigerant during the normal operation of mine refrigeration equipment. The article ends with the statistical verification of the developed dependencies. For this purpose, for each model correlation coefficients and coefficients of determination were calculated, as well as absolute and relative deviations between the given values from the program REFPROP 7 (Lemmon et al., 2002) and the calculated ones. The results of these calculations have been contained in Tables 14 and 15. Keywords: refrigerants, R407C, thermodynamic and thermokinetic parametersAutorzy niniejszego artykułu zajęli się zagadnieniem modelowania właściwości (parametrów) termodynamicznych i termokinetycznych czynników chłodniczych. Znajomość tych parametrów jest niezbędna do projektowania urządzeń chłodniczych, ich analizy energetycznej, a także do porównaniu efektywności pracy chłodziarek powietrza wykorzystujących różne czynniki chłodnicze. Jednym ze stosowanych w gór-niczych sprężarkowych chłodziarkach powietrza czynnikiem chłodniczym jest R407C. Dla wymienionego czynnika chłodniczego opracowano 23 zależności określające jego parametry termodynamiczne i termokinetyczne w stanie cieczy nasyconej, w stanie pary nasyconej suchej, w stanie pary przegrzanej, w stanie cieczy przechłodzonej oraz w stanie pary mokrej. Utworzone wzory podano odpowiednio w tabelach: 2, 5, 8, 10 i 12. Należy zaznaczyć, że zakresy obowiązywania tych wzorów są szersze od zakresów zmian

show abstract

Prediction of thermodynamic properties of refrigerants using data mining

Cited by 29 publications

References 29 publications

Filter Based Feature Selection Methods for Prediction of Risks in Hepatitis Disease

Filter Based Feature Selection Methods for Prediction of Risks in Hepatitis Disease

Data Mining Method For Energy System Aplications

Functional Dependence of Thermodynamic and Thermokinetic Parameters of Refrigerants Used in Mine Air Refrigerators. Part 1 – Refrigerant R407C

Contact Info

Product

Resources

About