Gubkin Russian State University of Oil and Gas
Moscow, Russia

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.

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Yakushev V.S.,Gubkin Russian State University of Oil and Gas
Earth's Cryosphere | Year: 2015

Information on the genesis of intrapermafrost hydrocarbon gases is generalized. Biochemical (microbial), katagenic (thermogenic) and coalbed (shale) types of intrapermafrost gases are documented. General scheme of distribution of different types of permafrost hydrocarbon gases on the Russian territory is presented.

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.

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.

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.

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.

Holevo A.S.,Gubkin Russian State University of Oil and Gas | Giovannetti V.,CNR Institute of Neuroscience
Reports on Progress in Physics | Year: 2012

One of the major achievements of the recently emerged quantum information theory is the introduction and thorough investigation of the notion of a quantum channel which is a basic building block of any data-transmitting or data-processing system. This development resulted in an elaborated structural theory and was accompanied by the discovery of a whole spectrum of entropic quantities, notably the channel capacities, characterizing information- processing performance of the channels. This paper gives a survey of the main properties of quantum channels and of their entropic characterization, with a variety of examples for finite-dimensional quantum systems. We also touch upon the 'continuous-variables' case, which provides an arena for quantum Gaussian systems. Most of the practical realizations of quantum information processing were implemented in such systems, in particular based on principles of quantum optics. Several important entropic quantities are introduced and used to describe the basic channel capacity formulae. The remarkable role of specific quantum correlations - entanglement - as a novel communication resource is stressed. © 2012 IOP Publishing Ltd.

Filippov A.N.,Gubkin Russian State University of Oil and Gas
Colloid Journal | Year: 2014

The interdiffusion of aqueous 1: 1 electrolytes having the same anion through a negatively charged (cation-exchange) membrane has been studied without taking into account the diffusive layers. It has been established that the interdiffusion coefficients of the cations depend (in addition to their own diffusion coefficients in the membrane) on the ratio of the diffusion coefficients of both cations to the diffusion coefficient of the anion and the ratio of the density of charges fixed in the membrane to the equal concentration of the electrolytes on both sides of the membrane, as well as the equilibrium distribution coefficients of cationanion ion pairs in the membrane matrix. The conditions have been found under which the membrane plays the role of a “blocking system” (like a diode) that is impenetrable to cations located on both sides of the membranes in spite of the existence of their concentration gradients. The developed approach can be used to describe the interdiffusion of 1: 1 electrolytes through any uniformly charged membrane. © 2014, Pleiades Publishing, Ltd.

Furtat I.B.,Gubkin Russian State University of Oil and Gas
Automation and Remote Control | Year: 2014

This paper considers the adaptive control problem for plants with delays in input signals using no predictors and plant's output measurements only. The proposed algorithm ensures a desired accuracy of plant's output tracking with respect to a reference signal. Finally, we provide simulation results to illustrate the performance of the algorithm. © 2014 Pleiades Publishing, Ltd.

Holevo A.S.,Gubkin Russian State University of Oil and Gas
Physica Scripta | Year: 2013

We study the relation between the unassisted and entanglement-assisted classical capacities C and Cea of entanglement-breaking channels. We argue that the gain of entanglement assistance Cea/C > 1 generically for measurement channels with unsharp observables; in particular for the measurements with pure posterior states the information loss in the entanglement-assisted protocol is zero, resulting in an arbitrarily large gain for very noisy or weak signal channels. This is illustrated by examples of continuous observables corresponding to state tomography in finite dimensions and heterodyne measurement. In contrast, state preparations are characterized by the property of having no gain of entanglement assistance, CeaC = 1. © 2013 The Royal Swedish Academy of Sciences.

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.

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