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Characterization of Slovenian coal and estimation of coal heating value based on proximate analysis using regression and artificial neural networks
Darja Kavšek, Adriána Bednárová, Miša Biro, Roman Kranvogl, Darinka Brodnjak-Vončina, Ernest Beinrohr, 2013, original scientific article

Abstract: Chemical composition of Slovenian coal has been characterised in terms of proximate and ultimate analyses and the relations among the chemical descriptors and the higher heating value (HHV) examined using correlation analysis and multivariate data analysis methods. The proximate analysis descriptors were used to predict HHV using multiple linear regression (MLR) and artificial neural network (ANN) methods. An attempt has been made to select the model with the optimal number of predictor variables. According to the adjusted multiple coefficient of determination in the MLR model, and alternatively, according to sensitivity analysis in ANN developing, two descriptors were evaluated by both methods as optimal predictors: fixed carbonand volatile matter. The performances of MLR and ANN when modelling HHV were comparable; the mean relative difference between the actual and calculated HHV values in the training data was 1.11% for MLR and 0.91% for ANN. The predictive ability of the models was evaluated by an external validation data set; the mean relative difference between the actual and predicted HHV values was 1.39% in MLR and 1.47% in ANN. Thus, the developed models could be appropriately used to calculate HHV.
Keywords: Slovenian coal, higher heating value, HHV, regression, artificial neural network
Published: 03.04.2017; Views: 28312; Downloads: 296
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Multivariate analysis and chemometric characterisation of textile wastewater streams
Darja Kavšek, Tina Jerič, Alenka Majcen Le Marechal, Simona Vajnhandl, Adriána Bednárová, Darinka Brodnjak-Vončina, 2013, original scientific article

Abstract: The aim of this work was to design a quick and reliable method for the evaluation and classification of wastewater streams into treatable and non-treatable effluents for reuse/recycling. Different chemometric methods were used for this purpose handling the enormous amount of data, and additionally to find any hidden information, which would increase our knowledge and improve the classification. The data obtained from the processes description, together with the analytical results of measured parameters' characterising the wastewater of a particular process, enabled us to build a fast-decision model for separating different textile wastewater outlets. Altogether 49 wastewater samples from the textile finishing company were analysed, and 19 different physical chemical measurements were performed for each of them. The resulting classification model was aimed at an automated decision about the choice of treatment technologies or a prediction about the reusability of wastewaters within any textile finishing or other company having similar characteristics of wastewater streams.
Keywords: textile finishing wastewater, chemometrics, multivariate data analysis, wastewater treatment
Published: 10.07.2015; Views: 1238; Downloads: 40
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Razvoj in uporaba analiznih metod določanja težkih kovin v okoljskih vzorcih z emisijsko spektrometrijo z induktivno skopljeno plazmo
Darja Kavšek, 2014, doctoral dissertation

Abstract: V doktorski disertaciji smo razvili, optimizirali in s pomočjo validacije potrdili analizne metode za točno in natančno določevanje izbranih težkih kovin v različnih matricah okoljskih vzorcev z optično emisijsko spektrometrijo z induktivno sklopljeno plazmo (ICP–OES). S pomočjo izmerjenih vsebnosti onesnaževal ter kemijskih in fizikalno–kemijskih parametrov smo izvedli kemometrijsko karakterizacijo različnih okoljskih vzorcev. Iskali smo korelacije med različnimi okoljskimi vzorci, ki so bili odvzeti na različnih vzorčnih mestih v različnih časovnih obdobjih.
Keywords: ICP–OES, premog, sedimenti, odpadne tekstilne vode, odpadne industrijske vode, težke kovine, validacija, kemometrijska klasifikacija, kemometrijska karakterizacija, analiza grupiranja, metoda glavnih osi, diskriminantna analiza
Published: 12.01.2015; Views: 1817; Downloads: 310
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