Maria A. Barrufet

Theoretical models of the electrical discharge machining process. I. A simple cathode erosion model

A simple cathode erosion model for the electrical discharge machining (EDM) process is presented. This point heat‐source model differs from previous conduction models in that it accepts power rather than temperature as the boundary condition at the plasma/cathode interface. Optimum pulse times are predicted to within an average of 16% over a two‐decade range after the model is tuned to a single experimental point. A constant fraction of the total power supplied to the gap is transferred to the cathode over a wide range of currents. A universal, dimensionless model is then presented which identifies the key parameters of optimum pulse time factor (g) and erodibility (j) in terms of the thermophysical properties of the cathode material. Compton’s original energy balance for gas discharges is amended for EDM conditions. Here it is believed that the high density of the liquid dielectric causes plasmas of higher energy intensity and pressure than those for gas discharges. These differences of macroscopic diele...

Theoretical models of the electrical discharge machining process. II. The anode erosion model

As a second in a series of theoretical models for the electrical discharge machining (EDM) process, an erosion model for the anode material is presented. As with our point heat‐source model in the previous article, the present model also accepts power rather than temperature as the boundary condition at the plasma/anode interface. A constant fraction of the total power supplied to the gap is transferred to the anode. The power supplied is assumed to produce a Gaussian‐distributed heat flux on the surface of the anode material. Furthermore, the area upon which the flux is incident is assumed to grow with time. The model is capable of showing, via the determined migrating melt fronts, the rapid melting of the anodic material as well as the subsequent resolidification of the material foation from plasma dynamics modeling could improve substantially our results.

Theoretical models of the electrical discharge machining process. III. The variable mass, cylindrical plasma model

A variable mass, cylindrical plasma model (VMCPM) is developed for sparks created by electrical discharge in a liquid media. The model consist of three differential equations—one each from fluid dynamics, an energy balance, and the radiation equation—combined with a plasma equation of state. A thermophysical property subroutine allows realistic estimation of plasma enthalpy, mass density, and particle fractions by inclusion of the heats of dissociation and ionization for a plasma created from deionized water. Problems with the zero‐time boundary conditions are overcome by an electron balance procedure. Numerical solution of the model provides plasma radius, temperature, pressure, and mass as a function of pulse time for fixed current, electrode gap, and power fraction remaining in the plasma. Moderately high temperatures (≳5000 K) and pressures (≳4 bar) persist in the sparks even after long pulse times (to ∼500 μs). Quantitative proof that superheating is the dominant mechanism for electrical discharge ma...

A new approach to describe oil absorption in fried foods: a simulation study

The mechanism of oil absorption of tortilla chips during cooling was analyzed using capillary pressure theory. The experimental and theoretical results obtained with this mechanistic model agreed well. Computer simulations were made to determine the effect of different process conditions on the final product oil content. The results show higher oil content for tortilla chips with higher initial moisture content, smaller radius, lower cooling air temperature, and higher interfacial tension.

Spatial distribution of oil after deep-fat frying of tortilla chips from a stochastic model

Abstract The morphology and oil distribution of tortilla chips can be described using a statistical description based upon Monte Carlo simulation approach. The chip distribution model consists of a three-dimensional multi-phase thin plate made of small cubic cells that define a lattice. Random numbers are allocated to each cell throughout the lattice. These numbers belong to a uniform distribution density and based upon their value one decides upon the content of the cell (water, pores, dry matter). The spatial location and cluster-size (aggregates) of the different constituents are stored. This structure represents the tortilla chips before frying. The lattice is swept cell by cell and the structure of the chips after frying is generated from a random invasion of the tortilla chip by oil. Water is allowed to evaporate and leave pores behind. Oil will coat the structure and occupy a fraction of the pore space. These invasion rules satisfy the mass balances for the tortilla chips components as well as the final porosity. These simulations were done on a CRAY Y-MP2/116 Supercomputer using a vectorized code.

Modeling the structural changes of tortilla chips during frying

Abstract Monte Carlo simulation was used to model structure changes in tortilla chips during frying. The results were analyzed using Ensight™ (CEI, Morrisville, NC), a Scientist’s, plotting tool. Cluster identity (oil, water, air, solid), number of clusters, cluster size, and mean size were determined. Cluster statistics were further provided for different frying conditions. The model predicted maximum oil absorption in a control tortilla chip during the first 10 s of frying, which coincided with maximum water evaporation also observed during the first 10 s of frying. Maximum pore expansion occurred between 30 and 40 s of frying. Steam baking the tortilla chip prior to frying caused the formation of a tight barrier on the surface due to starch gelatinization. This surface prevented water evaporation as well as oil absorption. Higher initial moisture contents provided for an increasing porosity in the product, from 47.12% to 54.04%. Freeze-dried tortilla chips had higher internal oil content because smaller pores spread over the matrix provide a larger surface area and higher capillary pressures. The smaller pores are due to the absence of a tight barrier along the tortilla’s surface, as freeze-drying does not cause starch gelatinization (no heat treatment prior to frying).

Experimental viscosities of heavy oil mixtures up to 450 K and high pressures using a mercury capillary viscometer

Abstract This paper presents an experimental methodology to determine the viscosity reduction of heavy oils with a solvent and to evaluate the effect of temperature upon the viscosity of light and heavy oil mixtures. These viscosities can be used in designing solvent stimulation of wells and input to reservoir simulators for heavy oil recovery processes. The data obtained can also be used to estimate diluent quantities required to reduce oil viscosity for pipeline transportation of heavy oil. We designed a versatile capillary tube viscometer to measure viscosity of single-phase mixtures at high temperatures and pressures. Mercury (Hg) is used to achieve higher-precision measurements by providing an ideal piston effect, particularly for stabilizing fluid flow movement of heavy oils in capillary coils. The principle of the method is to measure the differential pressure given by a pressure transducer from laminar flow of a single-phase fluid along the capillary coils, and to convert it to absolute viscosity by using the Hagen–Poiseuille equation. We measured the viscosity of heavy oil and light hydrocarbon mixtures at temperatures ranging from ambient to 450 K, and at pressures from 100 to 34,000 kPa. The heavy oil sample was taken from Canadas heavy oil reserves, located in northeastern Alberta and western Saskatchewan; and the light oil, used as a viscosity reducer, was n -decane. The results presented in this paper provide valuable methodologies and experimental data of viscosity of heavy oil and n -decane useful for further studies in enhanced oil recovery of heavy oils and for transportation purposes.

Analysis of the technical, microeconomic, and political impact of a carbon tax on carbon dioxide sequestration resulting from liquefied natural gas production

With increasing attention to the environmental impact of discharging greenhouse gases (GHG) in general, and CO2 in particular, many are looking to carbon sequestration as an approach to reduce the carbon impact of stationary point sources of CO2. Although much of the focus has historically been on capturing and sequestering post-combustion CO2 from the burning of fossil fuels, there are many industrial processes that already require separation of CO2 that also contribute to GHG emissions. This CO2 can also be sequestered. One such process is the commercial production of liquefied natural gas, which necessitates the separation of CO2 from the hydrocarbon for liquefaction; resulting in a relatively pure CO2 stream which can be sequestered. The Gorgon project is one such commercial project. In the broader political environment of Australia’s carbon tax system and government grants to offset the capital investment in carbon abatement technologies, the economics of the Gorgon project can be analyzed to determine the technical and economic parameters that make the carbon sequestration more or less feasible for this self-contained project. These findings can then be applied to any such project where a pure CO2 is a necessary by-product and a carbon tax is either in effect or being considered. This analysis is the primary objective of this article. In this context, a computer-based simulator was developed to analyze the impact of technical, market, and public policy factors on project economics. A base case was developed using the current project parameters and a number of alternative scenarios were then developed. Sensitivity analyses were conducted and a “best case” scenario was developed to look at what the appetite for investment could be to improve the sequestration of CO2. The article demonstrates that CCS project competitiveness can be simulated to analyze the impact of key technological, market, and policy changes on the project.

Modeling and Operation of Oil Removal and Desalting Oilfield Brines With Modular Units

Oilfield brine is the largest volume of waste generated by the oil and gas industry; typical produced brine volumes may easily exceed the oil production by 10 times with total dissolved solids ranging from 1,000 to over 250,000 ppm. Handling costs of produced brine may lead to the premature abandonment of many oil and gas wells. At the same time that oil and gas operators are trying to cope with excess produced water, many states are critically short of freshwater resources. This paper describes and validates a process to treat this brine to meet the standards for irrigation-quality water. Components of the proposed brine-conversion plant include both microfiltration and a pretreatment system for the removal of solid particles and oil using sorption pellets made of a modified clay material, and reverse osmosis (RO) units with a variety of interchangeable semipermeable membranes for the removal of dissolved salts. We collected experimental data for oil/water separation of controlled mixtures using packed columns with modified clay particles. The average oil loading capacity of these particles is better than activated carbon (over 60%) and our experimental results indicate that packed beds can remove over 90% of the oil. We screened a variety of RO membranes and selected one to conduct a series of experiments with brines with salinity up to 40,000 ppm, transmembrane pressures up to 1,000 psia, and various rates. Our experiments indicate salt rejections of 95 to 99% depending upon the initial salt concentration, transmembrane pressure, and rate.

Improved neural-network model predicts dewpoint pressure of retrograde gases

Abstract Accurate prediction of dewpoint pressure is a critical element in reservoir-engineering calculations. The objective of this paper is to present a novel and highly accurate application of the neural-network model (NNM) to predict dewpoint pressures in retrograde gas reservoirs. We were able to demonstrate that the model described in this paper is more accurate than any presented to date. In addition, the model is simple and is able to duplicate with reasonable accuracy the temperature–dewpoint pressure behavior of constant-composition gas condensate fluids. The neural-network model was developed using a set of 802 experimental constant volume depletion (CVD) data points. To train the neural-network model, a set of 641 experimental data points of CVD for different gas condensate fluids was used. The model was tested with 161 experimental data points, not used during the training process, to prove its accuracy. The study also considered a detailed comparison between the results predicted by this more efficient neural-network model and those predicted by other correlations for estimating dewpoint pressure of retrograde gas. The performance of this improved neural-network model and available correlations was evaluated versus the Peng–Robinson Equation of State (PR-EOS) model for the same reservoir fluid composition, a gas condensate from the Cusiana Field, in Colombia. This improved neural-network model was able to predict the dewpoint pressure with an average absolute error of 8.74%, as a function of temperature, hydrocarbons and non-hydrocarbon compositions, molecular weight, and specific gravity of heptanes-plus fraction. Neural-network models can save calculation time in the prediction of the dewpoint pressures with more reliability than available multiple-regression techniques.