A geometric statement of the Harnack inequality for a degenerate Kolmogorov equation with rough coefficients

We consider weak solutions of second-order partial differential equations of Kolmogorov-Fokker-Planck-type with measurable coefficients in the form ∂tu + (v,∇xu) = div(A(v,x,t)∇vu) + (b(v,x,t),∇vu) + f, (v,x,t) ϵ2n+1, where A is a symmetric uniformly positive definite matrix with bounded measurable coefficients; f and the components of the vector b are bounded and measurable functions. We give a geometric statement of the Harnack inequality recently proved by Golse et al. As a corollary, we obtain a strong maximum principle

Comparison of the Design of CO₂-Capture Processes Using Equilibrium and Rate Based Models

The design of absorption processes with complex aqueous chemical reactions such as CO2-capture, selective H2S-removal as well as rate-limited physical separations like LNG pre-treatment is not simple or straightforward. Reaction kinetics, mass transfer, and thermodynamic-driven processes are coupled and must be taken into account simultaneously. A new simulation tool developed by the Virtual Materials Group and Procede Process Simulations, VMG RateBase, is introduced. It is designed to provide consistent and accurate simulations for mass transfer limited, chemically reactive systems. The absorption of CO2 from flue gas produced by a coal-fired power plant into an aqueous MEA solution is used as an example to show how a more rigorous model affects the process design and simulation. VMG RateBase has a carefully developed thermodynamic package to support calculations for acid gas absorption together with databases for chemical kinetics, tray parameters, and random as well as structured packing. Several models for hydrodynamics and mass transfer, for example, Higbie penetration model, are available. The case studies shown in this work demonstrate that the use of equilibriumbased models can cause significant undersizing of packing heights for carbon dioxide removal. Important mass transfer limitations can be observed only through the use of rate-based models, such as carbon dioxide removal limiting efficiencies as a function of solvent rate and effective mass transfer profiles as a function of packing height

Extension of the Expanded Fluid Viscosity Model to Characterized Oils

The Expanded Fluid (EF) viscosity model for Newtonian fluids is extended to crude oils characterized as mixtures of defined components and pseudo-components. The EF models take the fluid density, dilute gas viscosity, pressure, and fluid composition as inputs and requires three fluid-specific parameters, <i>c</i>₂, <i>c</i>₃, and ρ_s^o, for the fluid or its components. Generally, experimental viscosity data are required to determine these values for each component. In this study, an internally consistent estimation method was developed to predict the fluid-specific parameters of the model for hydrocarbons when no experimental viscosity data are available. The method uses <i>n</i>-paraffins as the reference system and correlates the fluid-specific parameters for hydrocarbons as departures from the reference system. The method was evaluated against viscosity data of over 250 pure hydrocarbon compounds and petroleum distillation cuts. The model predictions were within the same order of magnitude of the measurements, with an overall average absolute relative deviation of 31%. The method was then used to calculate the correlation parameters for the pseudo-components of nine dead and live oils characterized on the basis of their gas chromatography (GC) assays. The viscosities of the crude oils were predicted within a factor of 3 of the measured values using the measured density of the oils as the input. The applicability of the EF model was also demonstrated using the densities determined with the Peng–Robinson equation of state. A simple method was proposed to tune the model to available viscosity data using a single multiplier to the <i>c</i>₂ parameter (and also to <i>c</i>₃ and ρ_s^o if necessary) of the pseudo-components. Single-parameter tuning of the model improved the viscosity prediction for the characterized oils to within 30% of the measured values

Prediction of Viscosity for Characterized Oils and Their Fractions Using the Expanded Fluid Model

A methodology has been developed to predict the viscosity of crude oils and their fractions from a distillation-based oil characterization. The maltenes were characterized as a set of pseudo-components with properties determined from established generalized correlations. The asphaltene fraction was characterized as a single component and its properties were measured. The viscosities of the pseudo-components, asphaltenes, whole oils, and their fractions were determined with the Expanded Fluid (EF) viscosity model. The inputs for the model are the pressure, the density of the fluid at a given pressure and temperature, the dilute gas viscosity calculated from established generalized correlations, and three fluid-specific parameters: <i>c</i>₂, ρ_s^°, and <i>c</i>₃. Densities were calculated using the modified Rackett correlation with the Tait-COSTALD compressibility correction. The <i>c</i>₃ parameter was determined from a previously developed correlation. New correlations were developed for the <i>c</i>₂ and ρ_s^° parameters of the maltene pseudo-components, as a function of their boiling point and specific gravity. The parameters for the asphaltene fraction were estimated based on the measured viscosity of molten asphaltenes. The EF parameters for the whole oil, or any oil fraction, were determined with mass-based mixing rules and binary interaction parameters, calculated from a previously developed correlation. To develop and test the proposed approach, density and viscosity data were collected for 40 distillation cuts from 6 oils, 7 maltenes, 2 asphaltenes, 3 partially deasphalted oils, and 14 dead oils. Using this model to predict crude oil viscosity under any conditions requires the distillation assay data, the asphaltene mass content, and the specific gravity and molecular weight of the oil. The approach was tested on a development and test dataset of 4 crude oils (this study) and an independent test dataset of 4 oils from the literature with overall average absolute relative deviation (AARD) values of 41% and 43%, respectively. Single multiplier tuning of the <i>c</i>₂ parameter to one viscosity data point halved the error. Tuning both the <i>c</i>₂ and ρ_s^° parameters using two viscosity data points reduced the AARD to <8% in both cases

Blockchain as an Internet of Services Application for an Advanced Manufacturing Environment

Part 7: Cyber-Physical SystemsInternational audienceIn the current dynamic and competitive market, contemporary manufacturing systems must be constantly adapted to meet the requirements for a more agile and smart production. The advent of Industry 4.0 comes as a reference on development of applications and technologies for manufacturing process innovation. Among the pillars of Industry 4.0, a noticeable relevance is given to Cyber Physical Systems, Internet of Things and Internet of Services. In parallel, new technologies as Blockchain and Smart Contracts are important innovations also coined by the Information Technology domain. More specifically, Internet of Services is characterized by a service-oriented computing model enabling a diversity of software-based services through the Internet, among them the Blockchain solution. The paper explores these technologies bringing their intersection as well as their possible applications in the shop floor level. Through the interlock of such concepts, the paper aims to propose an architecture that promotes the utilization of Blockchain for the validation of some service demands in an advanced manufacturing scenario of the Industry 4.0. Lastly a hypothetical case study is presented for illustrating the proposed architecture