New Approach to Predict the Filtrate Invasion of Nanoparticle-Based Drilling Mud Using Artificial Neural Network

New correlations for predicting pure and impure natural gas viscosity

The ignition delay time (IDT) characteristics of new ternary and quaternary blended C1 – C3 gaseous hydrocarbons, including methane/ethane/ethylene and methane/ethane/ethylene/propane, are studied over a wide range of mixture composition, temperature (∼800 – 2000 K), pressure (∼1 – 135 bar), equivalence ratio (∼0.5 – 2.0), and dilution (∼75 – 90%) using both experimental data and kinetic modeling tools. In this regard, all of the experimental tests were designed using the Taguchi approach (L9) to fulfill the experimental matrix required to generate a comprehensive set necessary to validate a detailed chemical kinetic model. High- and low-temperature IDTs were recorded using low/high-pressure shock tubes (L/HPST) and rapid compression machines (RCM), respectively. The model predictions using NUIGMech1.2 are evaluated versus all of the newly recorded experimental data. Moreover, the individual effects on IDT predictions of the parameters studied, including mixture composition and pressure, are investigated over the temperature range. The results show that NUIGMech1.2 can reasonably reproduce the experimental IDTs over the wide range of the conditions studied. The constant-volume simulations using the chemical kinetic mechanism reveal the synergistic/antagonistic effect of blending on IDTs over the studied temperature range so that IDTs in certain temperature ranges are very sensitive to even small changes in mixture composition.

Solubility is a significant physical and chemical property. The solubility of carbon dioxide(CO 2 ) in polymers is an important application of green chemistry. Aimed at the problem of insufficient precision of the existing prediction model, a solubility prediction model based on the adaptive particle swarm optimization algorithm and the least‐squares support vector machine(APSO‐LSSVM) is proposed. Different from the traditional particle swarm algorithm, APSO algorithm improves the problem of easily falling into local optimal solution. The regularization parameters and kernel function tuning parameters of the LSSVM were optimized by APSO and then use this model to predict the solubility of CO 2 in eight polymers within a wide range of temperatures and pressures. APSO‐LSSVM model has great prediction ability, with a good correlation between prediction data and experimental data, high prediction accuracy, short calculation time and strong stability. Compared with back propagation artificial neural network(BP ANN), back propagation‐particle swarm optimization algorithm artificial neural network(BP‐PSO ANN) and LSSVM, APSO‐LSSVM has better comprehensive performance. The average absolute relative deviation(AARD), root mean square error(RMSE), determination coefficient(R 2 ) were respectively 0.2130, 0.0120, 0.9853. In addition, the model has good expansibility and can be applied to other fields such as chemistry and medicine.

Artificial neural networks (ANNs), for a first time, were successfully developed for the prediction partial molar heat capacity of aqueous solutions at infinite dilution for various polar aromatic compounds over wide range of temperatures (303.55-623.20 K) and pressures (0.1-30.2 MPa). Two three-layered feed forward ANNs with back-propagation of error were generated using three (the heat capacity in T = 303.55 K and P = 0.1 MPa, temperature and pressure) and six parameters (four theoretical descriptors, temperature and pressure) as inputs and its output is partial molar heat capacity at infinite dilution. It was found that properly selected and trained neural networks could fairly represent dependence of the heat capacity on the molecular descriptors, temperature and pressure. Mean percentage deviations (MPD) for prediction set by the models are 4.755 and 4.642, respectively.

Correlating ionic liquids density over wide range of temperature and pressure by volume shift concept

Modeling heat capacity of saturated hydrocarbon in liquid phase over a wide range of temperature and pressure

An innovative flexible faced mechanical shaft seal using common elastomeric materials was designed and tested to determine its friction coefficient at a wide range of temperatures and speeds, its rate of heat generation, and its feasibility for use in the process industry. The new seal was constructed using an elastomeric rotating element stretched over the sleeve to at least 20 percent of its original length and an unlapped silicon carbide stationary annular ring. It was found that the main advantage of the elastomeric seal is its ability to maintain stable lubrication with a fluid film considerably thinner than that of traditional hard face seals, and consequently achieve negligible net leakage. This is particularly significant with respect to control of volatile organic carbon emissions. An experimental device was designed for precise measurement of the friction coefficient as well as the long term friction behavior of seal pairs in a wide range of liquid pressure and temperature. The original data were obtained for friction coefficient of EPDM, HNBR, FKM, and TFE/P type elastomers in contact with silicon carbide in the temperature range 15–110°C, linear speeds 0–12 m/s, water pressure 0.15–0.40 MPa, and effective contact pressure 0.8–1.2 MPa. Experiments showed that the friction coefficient constantly grows, typically from 0.05 to 0.15 at sliding speeds of 2–12 m/s, with temperature increases from 15 to 70°C. The temperature behavior of the friction coefficient above 70°C greatly depends on the elastomer. For high temperature elastomers, such as FKM, the friction coefficient may decrease slightly at 70°C; whereas, for EPDM, it continues to increase as temperature increases.

Modelling density of pure and binary mixtures of normal alkanes: Comparison of hybrid soft computing techniques, gene expression programming, and equations of state

A comparative study on Arrhenius-type constitutive model and artificial neural network model to predict high-temperature deformation behaviour in Aermet100 steel

Reactions of β-hydroxypropyl radicals with O2 on the HOC3H6OO• potential energy surfaces: A theoretical study

The extended UNIQUAC model is a thermodynamic model able to estimate thermodynamic properties of aqueous electrolyte solutions under a wide range of temperature, pressure, and composition conditions. Thermodynamic properties include species activity coefficients, excess molar Gibbs energy, excess molar enthalpy, excess molar heat capacity. These properties are important for aqueous speciation calculations, mineral and gas solubility computations, chemical kinetic modeling of mineral dissolution and precipitation, and in reactive transport simulations considering chemically complex aqueous electrolyte solutions. In this paper we present a brief literature review on the extended UNIQUAC model, we report on its implementation in C++ in the Reaktoro framework for modeling chemically reactive systems, and we show its use from Python for computing mineral and gas solubilities in aqueous solutions at a wide range of temperature, pressure, and salinity conditions. We validated the calculations against experimental data and results obtained through the software ScaleCERE implementing the extended UNIQUAC model. Our conclusion is that the extended UNIQUAC model has been successfully implemented into the Reaktoro framework, thereby providing a suitable activity model for geochemical and reactive transport modeling. • Accurate estimation of mineral and gas solubilities using the extended UNIQUAC model in Reaktoro. • Validation against experimental data and ScaleCERE ensures reliability and accuracy. • Provides detailed formulation and necessary model parameters for the extended UNIQUAC model. • Demonstrates mineral solubility calculations in Reaktoro using straightforward Python scripts.

Abstract

Diffusion coefficient of carbon dioxide (CO2), a significant parameter describing the mass transfer process, exerts a profound influence on the safety of CO2 storage in depleted reservoirs, saline aquifers, and marine ecosystems. However, experimental determination of diffusion coefficient in CO2-brine system is time-consuming and complex because the procedure requires sophisticated laboratory equipment and reasonable interpretation methods. To facilitate the acquisition of more accurate values, an intelligent model, termed MKSVM-GA, is developed using a hybrid technique of support vector machine (SVM), mixed kernels (MK), and genetic algorithm (GA). Confirmed by the statistical evaluation indicators, our proposed model exhibits excellent performance with high accuracy and strong robustness in a wide range of temperatures (273–473.15 K), pressures (0.1–49.3 MPa), and viscosities (0.139–1.950 mPa·s). Our results show that the proposed model is more applicable than the artificial neural network (ANN) model at this sample size, which is superior to four commonly used traditional empirical correlations. The technique presented in this study can provide a fast and precise prediction of CO2 diffusivity in brine at reservoir conditions for the engineering design and the technical risk assessment during the process of CO2 injection.

In the present work we apply the cBΩ thermodynamic model to study the diffusion of Si in stishovite crystal at high pressure and in a wide temperature range. According to this model, the point defect activation Gibbs free energy is expressed as a function of the bulk properties of the material, i.e., gact = cBΩ, where B is the isothermal bulk modulus, Ω is the mean atomic volume, and c is a dimensionless constant. In this way, other important point defect parameters, such as the activation volume vact, the activation entropy sact, and the activation enthalpy hact may be estimated if the thermoelastic properties of the material are known over a wide temperature and pressure range. Our calculations are based on previously reported self-diffusion coefficients in stishovite single crystals measured at 14 GPa and at temperatures from 1400 to 1800 °C, in the [110] and [001] directions, by Shatskiy et al. (Am. Mineral. 2010, 95, 135–43). Furthermore, the EOS of stishovite, proposed by Wang et al. (J. Geophys. Res. 2012, 117, B06209) has been used for the accurate implementation of the cBΩ model. Our results suggest that the aforementioned point defect parameters exhibit considerable temperature dependence over the studied temperature range (1000–2000 °C). The estimated activation volumes (4.4–5.3 cm3/mol, in the range of 1400–1800 °C) are in agreement with reported experimental results. Our study confirms the potential of the cBΩ model for the theoretical investigation of diffusion processes in minerals, in order to overcome the experimental difficulties and the lack of experimental diffusion data in mantle conditions.

The reaction between H and benzene is a prototype for reactions of radicals with aromatic hydrocarbons. Here we report calculations of the reaction rate constants and the branching ratios of the two channels of the reaction (H addition and H abstraction) over a wide temperature and pressure range. Our calculations, obtained with an accurate potential energy surface, are based on variational transition-state theory for the high-pressure limit of the addition reaction and for the abstraction reaction and on system-specific quantum Rice-Ramsperger-Kassel theory calibrated by variational transition-state theory for pressure effects on the addition reaction. The latter is a very convenient way to include variational effects, corner-cutting tunneling, and anharmonicity in falloff calculations. Our results are in very good agreement with the limited experimental data and show the importance of including pressure effects in the temperature interval where the mechanism changes from addition to abstraction. We found a negative temperature effect of the total reaction rate constants at 1 atm pressure in the temperature region where experimental data are missing and accurate theoretical data were previously missing as well. We also calculated the H + C6H6/C6D6 and D + C6H6/C6D6 kinetic isotope effects, and we compared our H + C6H6 results to previous theoretical data for H + toluene. We report a very novel nonmonotonic dependence of the kinetic isotope effect on temperature. A particularly striking effect is the prediction of a negative temperature dependence of the total rate constant over 300-500 K wide temperature ranges, depending on the pressure but generally in the range from 600 to 1700 K, which includes the temperature range of ignition in gasoline engines, which is important because aromatics are important components of common fuels.