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The Gubkin Russian State University of Oil and Gas is a university in Moscow. The university was founded on 17 April 1930 and is named after the geologist Ivan Gubkin. The university is affectionally known as Kerosinka , meaning "kerosene stove". The institute was part of the Moscow Geological Exploration Institute but later became a separate entity.During the Soviet period, the university, along with the Moscow State University of Railway Engineering, was known for admitting students of Jewish origin while other universities unofficially barred Jewish students.Affiliates of the Gubkin institute exist in Orenburg, Ashgabat, Turkmenistan and Tashkent, Uzbekistan. Wikipedia.

Balabin R.M.,ETH Zurich | Safieva R.Z.,Gubkin Russian State University of Oil and Gas
Energy and Fuels | Year: 2011

An effective calibration model of biodiesel fuel properties prediction, based on near-infrared (NIR) spectroscopy data and an artificial neural network (ANN), was built. Biodiesel samples were derived from multiple sources and prepared using multiple experimental parameters. Four different fuel properties, including fractional composition, were accurately predicted. The rootmean- square errors of prediction (RMSEPs) on an independent sample sets for the end boiling point (50% v/v), the end boiling point (90% v/v), the iodide value, and the cold filter plugging point were 1.73 °C, 1.78 °C, 0.90 g I 2/100 g, and 0.77 °C, respectively. Multiple linear regression (MLR), principal component regression (PCR), partial least-squares (projection to latent structures, PLS) regression, (kernel) polynomial and spline versions of partial least-squares regression (Poly-PLS and Spline-PLS), and ANNs were compared for the prediction of biodiesel properties. Data preprocessing techniques and calibration model parameters were independently optimized for each case. The ANN approach was superior to the linear (MLR, PCR, and PLS) and "quasi"-nonlinear (Poly-PLS and Spline-PLS) calibration methods. The ANN approach was a factor of 7.5±1.9 more efficient than MLR and a factor of 2.6 ± 0.9 more efficient than PLS (according to RMSEP ratios).We confirmed that biodiesel is a highly "nonlinear" object. Nine data pretreatment (preprocessing) methods (mean centering, mean scattering correction, standard normal variate, Savitzky-Golay derivatives, range scaling, etc.) were tested. The first/second-order Savitzky-Golay derivative, followed by Mean Centering plus Orthogonal Signal Correction, was found to be effective for biodiesel NIR data preprocessing. © 2011 American Chemical Society.

Balabin R.M.,ETH Zurich | Safieva R.Z.,Gubkin Russian State University of Oil and Gas | Lomakina E.I.,Moscow State University
Analytica Chimica Acta | Year: 2010

Near infrared (NIR) spectroscopy is a non-destructive (vibrational spectroscopy based) measurement technique for many multicomponent chemical systems, including products of petroleum (crude oil) refining and petrochemicals, food products (tea, fruits, e.g., apples, milk, wine, spirits, meat, bread, cheese, etc.), pharmaceuticals (drugs, tablets, bioreactor monitoring, etc.), and combustion products. In this paper we have compared the abilities of nine different multivariate classification methods: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), soft independent modeling of class analogy (SIMCA), partial least squares (PLS) classification, K-nearest neighbor (KNN), support vector machines (SVM), probabilistic neural network (PNN), and multilayer perceptron (ANN-MLP) - for gasoline classification. Three sets of near infrared (NIR) spectra (450, 415, and 345 spectra) were used for classification of gasolines into 3, 6, and 3 classes, respectively, according to their source (refinery or process) and type. The 14,000-8000cm-1 NIR spectral region was chosen. In all cases NIR spectroscopy was found to be effective for gasoline classification purposes, when compared with nuclear magnetic resonance (NMR) spectroscopy or gas chromatography (GC). KNN, SVM, and PNN techniques for classification were found to be among the most effective ones. Artificial neural network (ANN-MLP) approach based on principal component analysis (PCA), which was believed to be efficient, has shown much worse results. We hope that the results obtained in this study will help both further chemometric (multivariate data analysis) investigations and investigations in the sphere of applied vibrational (infrared/IR, near-IR, and Raman) spectroscopy of sophisticated multicomponent systems. © 2010.

Balabin R.M.,ETH Zurich | Safieva R.Z.,Gubkin Russian State University of Oil and Gas
Analytica Chimica Acta | Year: 2011

The use of biofuels, such as bioethanol or biodiesel, has rapidly increased in the last few years. Near infrared (near-IR, NIR, or NIRS) spectroscopy (>4000cm-1) has previously been reported as a cheap and fast alternative for biodiesel quality control when compared with infrared, Raman, or nuclear magnetic resonance (NMR) methods; in addition, NIR can easily be done in real time (on-line). In this proof-of-principle paper, we attempt to find a correlation between the near infrared spectrum of a biodiesel sample and its base stock. This correlation is used to classify fuel samples into 10 groups according to their origin (vegetable oil): sunflower, coconut, palm, soy/soya, cottonseed, castor, Jatropha, etc. Principal component analysis (PCA) is used for outlier detection and dimensionality reduction of the NIR spectral data. Four different multivariate data analysis techniques are used to solve the classification problem, including regularized discriminant analysis (RDA), partial least squares method/projection on latent structures (PLS-DA), K-nearest neighbors (KNN) technique, and support vector machines (SVMs). Classifying biodiesel by feedstock (base stock) type can be successfully solved with modern machine learning techniques and NIR spectroscopy data. KNN and SVM methods were found to be highly effective for biodiesel classification by feedstock oil type. A classification error (E) of less than 5% can be reached using an SVM-based approach. If computational time is an important consideration, the KNN technique (E=6.2%) can be recommended for practical (industrial) implementation. Comparison with gasoline and motor oil data shows the relative simplicity of this methodology for biodiesel classification. © 2011 Elsevier B.V.

Balabin R.M.,ETH Zurich | Lomakina E.I.,ETH Zurich | Safieva R.Z.,Gubkin Russian State University of Oil and Gas
Fuel | Year: 2011

The use of ethanol and biodiesel, which are alternative fuels or biofuels, has increased in the last few years. Modern official standards list 25 parameters that must be determined to certify biodiesel quality, and these analyses are expensive and time-consuming. Near infrared (NIR/NIRS) spectroscopy (4000-12,820 cm-1) is a cheap and fast alternative to analyse biodiesel quality, when compared with infrared, Raman, or NMR methods, and quality control can be done in realtime (on-line).We compared the performance of linear and non-linear calibration techniques - namely, multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLS), polynomial and Spline-PLS versions, and artificial neural networks (ANN) - for prediction of biodiesel properties from near infrared spectra. The model was created for four important biodiesel properties: density (at 15 °C), kinematic viscosity (at 40 °C), water content, and methanol content. We also investigated the influence of different pre-processing methods (Savitzky-Golay derivatives, orthogonal signal correction) on the model prediction capability. The lowest root mean squared errors of prediction (RMSEP) of ANN for density, viscosity, water percentage, and methanol content were 0.42 kg m-3, 0.068 mm2 s-1, 45 ppm, and 51 ppm, respectively. The artificial neural network (ANN) approach was superior to the linear (MLR, PCR, PLS) and "quasi"-non-linear (Poly-PLS, Spline-PLS) calibration methods. © 2010 Elsevier Ltd. All rights reserved.

Furtat I.B.,Gubkin Russian State University of Oil and Gas
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2013

A robust synchronization algorithm for a structural uncertainty dynamic network with a variable topology is proposed. The algorithm of a decentralized control provides the network synchronization, compensation of a parametric uncertainty and uncontrollable disturbances with a required accuracy. The modeling result for the network, consisting of the four nodes and leader, are presented. © IFAC.

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