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Soljic Jerbic I.,University of Zagreb | Parlov Vukovic J.,INA Oil Industry Ltd. | Jukic A.,University of Zagreb
Industrial and Engineering Chemistry Research | Year: 2012

Polymeric dispersive viscosity index improvers of lubricating mineral oils based on styrene, dodecyl-methacrylate, octadecyl methacrylate, and N,N-dimethylaminoethyl methacrylate (d-PSAMA) were produced by performing copolymerizations isothermally up to the high conversion in mineral base oil solution, using monofunctional or bifunctional peroxide initiator. The obtained kinetics results reveal the benefits of the usage of a bifunctional peroxide initiator over a monofunctional, because complete conversion of monomers was accomplished in the shorter reaction time, performing the process in a full batchwise mode. When the bifunctional initiator was applied, the required polymerization temperature was slightly higher (105 °C), and copolymers of higher average molecular weight values (M w = 60-120 kg mol -1) were obtained, while in case of the monofunctional peroxide initiator, the reaction temperature was 100 °C, and average molecular weight values of copolymers were M w = 30-100 kg/mol. Investigated application properties demonstrated that d-PSAMA additives were fully comparable with conventional pure methacrylate additives, and also it provided other advantages such as higher viscosity index and kinematic viscosity, lower values of pour point temperatures, as well as better dispersant and detergent properties. Thus, by increasing the N,N-dimethylaminoethyl methacrylate share in copolymers from 2 to 10 mol %, their weight average molecular weight decreased from 120 to 60 kg mol -1, while kinematic viscosity values at 100 °C remain high and amounted to 14.5 ± 0.5 mm 2 s -1. © 2012 American Chemical Society.

Marinovic S.,University of Zagreb | Bolanca T.,University of Zagreb | Ukic S.,University of Zagreb | Rukavina V.,INA Oil Industry Ltd. | Jukic A.,University of Zagreb
Chemistry and Technology of Fuels and Oils | Year: 2012

In this paper, two neural networks, multilayer perceptron and networks with radial-basis function, were used to predict important cold properties of commercial diesel fuels, namely cloud point and cold filter plugging point. The developed models predict the named properties using cetane number, density, viscosity, contents of total aromatics, and distillation temperatures at 10, 50, and 90 vol. % recovery as input data. The training algorithms, number of hidden layer neurons, and number of training data points were optimized in order to obtain a model with optimal predictive ability. The results indicated better prediction of cloud and cold filter plugging points in the case of multilayer perceptron networks. The obtained absolute error mean for the optimal neural network models (0.58°C for the cloud point and 1.46°C for the cold filter plugging point) are within the range of repeatability of standard cold properties determination methods. © 2012 Springer Science+Business Media, Inc.

Bolanca T.,University of Zagreb | Marinovic S.,INA Oil Industry Ltd. | Ukic S.,University of Zagreb | Jukic A.,University of Zagreb | Rukavina V.,INA Oil Industry Ltd.
Acta Chimica Slovenica | Year: 2012

This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement.

Marinovic S.,INA Oil Industry Ltd. | Kristovic M.,INA Oil Industry Ltd. | Spehar B.,INA Oil Industry Ltd. | Rukavina V.,INA Oil Industry Ltd. | Jukic A.,University of Zagreb
Journal of Analytical Chemistry | Year: 2012

Partial least squares regression (PLS) calibration models based on Fourier transform infrared (FTIR-ATR) and Raman spectra (FT-Raman) were applied to the rapid and accurate simultaneous determination of the main properties of diesel fuels. Training sets were composed of over ninety commercial diesel fuel samples. The methods use baseline-uncorrected, raw FTIR-ATR and FT-Raman spectra. Two spectral regions were studied: full spectral region and "fingerprint" region. The models were validated using the cross-validation process. Based on the correlation coefficient and root mean square error of cross validation (RMSECV) values the both developed calibration models, PLS/FTIR-ATR and PLS/FT-Raman, were very accurate and comparable with standard testing methods. The following diesel fuel properties may be confidently estimated: cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, as well as the contents of total aromatics and polycyclic aromatic hydrocarbons. As compared to the "fingerprint" spectral region, the PLS/FTIR-ATR model using full spectral region displayed slightly better performances with the most of the correlation coefficient values above 0.98. © 2012 Pleiades Publishing, Ltd.

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