Virial coefficients of normal fluids

The force constants of the square-well potential for normal fluids were correlated in terms of the critical properties P_c, T_c, and the acentric factor ω, by means of a non-linear regression technique. The second virial coefficients, a total fo 100 points chosen uniformly with respect to P_r and ω from the compilation of Dymond, together with the compressibility factors, taken from the generalized tables of Pitzer. were employed in the correlation. The valid ranges of the correlation are 0.5 ?T_r ? 2·0 and 0 ? ρ_r ? 1·0. The calculated results for the second virial coefficients compare favourably with the generalized correlation of Pitzer and Curl. The calculated third virial and cross third virial coefficients, and the compressibility factors also agree well with the literature values.     

PRSV equation of state parameter modeling through artificial neural network and adaptive network-based fuzzy inference system

Two different modeling methods have been proposed to relate the Peng-Robinson-Stryjek-Vera (PRSV) parameter, κ ₁, to some common thermodynamic constants, including critical temperature (T _c ), critical pressure (P _c ), acentric factor (ω) and molecular weight (Mw). The methods are artificial neural network (ANN) and adaptive networkbased fuzzy inference System (ANFIS). A set of 159 data points (116, 23 and 20) was used for construct training, validating and testing, respectively. The radius parameter of ANFIS was determined through genetic algorithm (GA) optimization technique. The ANN and especially ANFIS results are in a good agreement with most of the compound groups.     

Artificial Neural Networks Modeling of Ozone Bubble Columns: Mass Transfer Coefficient,Gas Hold-Up,and Bubble Size

This study aims at applying artificial neural network (ANN) modeling approach in designing ozone bubble columns. Three multi-layer perceptron (MLP) ANN models were developed to predict the overall mass transfer coefficient (k_La, s^?1), the gas hold-up (? _G , dimensionless), and the Sauter mean bubble diameter (d_S , m) in different ozone bubble columns using simple inputs such as bubble column's geometry and operating conditions. The obtained results showed excellent prediction of k_La, ? _G , and d_S values as the coefficient of multiple determination (R² ) values for all ANN models exceeded 0.98. The ANN models were then used to determine the local mass transfer coefficient (k_L , m.s^?1). A very good agreement between the modeled and the measured k_L values was observed (R² ?=?0.85).     

Thermolysis of glycol lignin in the presence of tetralin

An organosolv (ethylene glycol) hardwood lignin has been thermally treated in the presence of tetralin, a hydrogen donor solvent. The effects of reaction temperature and time on product distribution were studied as a function of a seventy index, X_c = ?n (R_ω with R_ω = Δt t × exp [(T_r - T_b)/ω] where Δt = time, T_r = reaction temperature, T_b = base temperature, and ω = characteristic parameter. Under the conditions used, up to 60% of the lignin can be converted to liquid and gaseous products. Syringols, guaiacols, aromatic aldehydes and ketones predominate at low treatment severities whilst phenol, catechol and their methyl and ethyl derivatives are the main monomers at high severities. Comparison of our data with parallel works is shown to be effectively done through the severity index, X_c.     

Intelligent Prediction of CO2 Capture in Propyl Amine Methyl Imidazole Alanine Ionic Liquid: An Artificial Neural Network Model

The objective of this paper is to create a new artificial neural network (ANN) model to predict solubility of CO₂ in a new structure of task specific ionic liquids called propyl amine methyl imidazole alanine [pamim][Ala]. Equilibrium data of CO₂ solubility were measured at the temperatures of 25, 40, and 60°C and the pressures up to 50 bar. For the purpose of performance comparison, the two most common types of ANNs, multilayer perceptron (MLP) network and radial basis function (RBF) network were used. Water content, ionic liquid content, temperature, and pressure set as input variables to ANN, while CO₂ capture rate assigned as output. Based upon optimization process, MLP neural network with 14 neurons in the hidden layer, log-sigmoid transfer function in the hidden layer and linear transfer function in the output layer, exhibited much better performance in prediction task than RBF neural network with the same neuron numbers in the hidden layer. Results obtained demonstrated that there is a very little difference between the estimated results of ANN approach and experimental data of CO₂ capture rate for the training, validation, and test data sets. Furthermore, Henry’s law constants were obtained by fitting the equilibrium data.     

Estimation of flotation rate constant and collision efficiency using regression and artificial neural networks

The effects of particle characteristics and hydrodynamic conditions on the flotation rate constant (k) and bubble–particle collision efficiency (E_c) of pyrite and chalcopyrite particles were investigated. Experimental results showed that k increases with increase of bubble surface area flux (S_b) and E_c. Artificial neural network (ANN) and multivariable linear regression procedures were used to predict both k and E_c based on the particle characteristics and hydrodynamic conditions. Multivariable linear regression resulted in R² of 0.6 and 0.93 for k and E_c, respectively. Using an ANN model, R² as high as 0.98 was achieved in modeling the E_c with regard to the available parameters. The proposed ANN model can be reliably used to determine both k and E_c parameters in froth flotation.     

Modeling and optimization of electrospun PAN nanofiber diameter using response surface methodology and artificial neural networks

Response surface methodology (RSM) based on a three‐level, three‐variable Box‐Benkhen design (BBD), and artificial neural network (ANN) techniques were compared for modeling the average diameter of electrospun polyacrylonitrile (PAN) nanofibers. The multilayer perceptron (MLP) neural networks were trained by the sets of input–output patterns using a scaled conjugate gradient backpropagation algorithm. The three important electrospinning factors were studied including polymer concentration (w/v%), applied voltage (kV) and the nozzle‐collector distance (cm). The predicted fiber diameters were in agreement with the experimental results in both ANN and RSM techniques. High‐regression coefficient between the variables and the response (R² = 0.998) indicates excellent evaluation of experimental data by second‐order polynomial regression model. The R² value was 0.990, which indicates that the ANN model was shows good fitting with experimental data. Moreover, the RSM model shows much lower absolute percentage error than the ANN model. Therefore, the obtained results indicate that the performance of RSM was better than ANN. The RSM model predicted the 118 nm value of the finest nanofiber diameter at conditions of 10 w/v% polymer concentration, 12 cm of nozzle‐collector distance, and 12 kV of the applied voltage. The predicted value (118 nm) showed only 2.5%, difference with experimental results in which 121 nm at the same setting were observed. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Comparison between the artificial neural network,SAFT and PRSV approach in obtaining the solubility of solid aromatic compounds in supercritical carbon dioxide

The aim of this study is to model the solubilities of solid aromatic compounds in supercritical carbon dioxide (SCCO₂) using feed-forward artificial neural network (ANN). Temperature, pressure, critical properties and acentric factor of each solute have been used as independent variables of ANN model. The parameters of multi-layer perceptron (MLP) network have been adjusted by back propagation learning algorithm using experimental data which have been collected from various literatures. In order to find the optimal topology of the MLP, different networks were trained and examined and the network with minimum absolute average relative deviation percent (AARD%), mean square error (MSE) and suitable regression coefficient (R²) has been selected as an optimal configuration. By this procedure a single hidden layer network composed of nineteen hidden neurons has been found as an optimal topology. Sensitivity error analyses confirmed that the optimal ANN can predict experimental data with an excellent agreement (AARD% = 4.99, MSE = 7.08 × 10⁻⁷ and R² = 0.99699). Capability of the proposed ANN model has compared with those published results which have obtained by SAFT combined with eight different mixing rules (one, two and three parameters mixing rules) and PRSV equation of state (EOS). The best presented overall AARD% for SAFT approach with one, two and three parameters mixing rules are 16.15, 12.32% and 7.65%, respectively while PRSV EOS showed AARD% of 21.10%. The results emphasize that the proposed ANN model can predict the solubilities of solid aromatic compounds in SCCO₂ more accurate than SAFT and PRSV EOS.     

A new analytical modeling for the determination of thermodynamic quantities of refrigerants

A new analytical fundamental equation of state (EOS) is presented for fluids. The equation is explicit in the effective molecular potentials and allows calculation of all thermodynamic properties over the whole fluid surface (gas, liquid, supercritical and gas–liquid phase transition). Outside the critical area (± 0.05T_c), it is valid in a vast range of temperature and pressure (0.8T_c to 7.5T_c and up to 120P_c,). The EOS is applicable for a variety of refrigerants such as C₃H₂F₄ (HFO-1234ze (E)), hydrofluorocarbons (HFCs) including C₃F₈ (R218), C₃H₂F₆ (R236ea), C₃H₂F₆ (R236fa), C₃H₃F₅ (R245ca), C₃HF₇, C₄F₈ (RC318), C₄F₁₀, C₅F₁₂, natural refrigerants including NH₃, CO₂, hydrocarbons, monatomic gases and some other fluids. Calculations of second derivatives properties of fluids are sensitive tests of EOS behavior. Therefore, estimation of the thermodynamic properties including Joule-Thomson coefficient, μ_JT, and speed of sound, w, has been considered.     

Relationships between thermally induced residual stresses and architecture of epoxy‐amine model networks

The capability of epoxy‐amine resins to develop residual stresses was studied as a function of temperature and network architecture. These residual stresses were induced while cooling epoxy‐glass bilayers from temperatures higher than the network glass transition temperature, T_g. This behavior was the result of the marked differences (α_r − α_g), in linear thermal expansion coefficient of the two components, as evidenced by the measurement of α_r for the epoxy networks under study. Various network architectures were selected, resulting from variation of (1) the chemical nature of both epoxide and curing agent, (2) the nature and relative amount of the chain‐extensor agent, and (3) the stoichiometric ratio. Three ranges of cooling temperature were observed systematically: first, the range of temperatures above T_g, where no stress has been detected, then an intermediate temperature range (from T_g to T*), where stresses develop quite slowly, and finally, the low temperature range (T < T*), where a linear increase in stress accompanies the decrease of temperature. The two latter regimes were quantitatively characterized by the extent, T_g − T*, of the first one and by the slope, SDR, of the second one. T_g − T* values were shown to be governed by the T_g of the network: the higher the T_g, the larger the gap between T_g and T*. This result was interpreted by accounting for the variation of relaxation rate at T_g from one network to the other. It was also shown that a semiempirical relationship holds between SDR and T_g: SDR decreases monotonically as T_g increases. By inspecting the effects of network architecture in more details, it turned out that SDR is governed by the Young's moduli, E_r(T − T_g), of the epoxy resins in the glassy state: the lower E_r(T − T_g), the lower SDR in a series of homologous networks. As E_r(T − T_g) values are known to be related to the characteristics of the secondary relaxation β, which depends, in turn, on crosslink density, SDR values were finally connected to the amplitude of the β relaxation processes. This finding was corroborated by the measurements on an antiplasticized dense network. Finally, data relative to thermoplastic‐filled networks showed that the addition of thermoplastic reduces the development of residual stresses, whatever the system, is homogeneous or biphasic. © 2000 John Wiley & Sons, Inc. J Appl Polym Sci 75: 638–650, 2000     

An extension of the group contribution method for estimating thermodynamic and transport properties part II. Polyatomic gases (F2, Cl2, CS2, H2S, NO and N2O)

Earlier work on the group contribution method applied to the Kihara potential is extended to polyatomic gases for the calculation of second virial coefficients, viscosities and diffusivities of dilute gases with a single set of gas group parameters. Functional group parameters are evaluated from the simultaneous regression of second virial coefficient and viscosity data of pure gases. Parameters for gas groups (F₂, Cl₂, CS₂, H₂S, NO, nd N₂O) are found to provide good predictions of second virial cross coefficients, mixture viscosities and binary diffusion coefficients of gas-gas mixtures. Application of the model shows that second virial coefficient data can be represented with good results comparable to the values by means of the corresponding states model. The reliability of the present model in viscosity predictions is proved by comparison with the Lucas method. Predictions of binary diffusion coefficients are in excellent agreement with experimental data and compare well with values obtained by means of the Fuller method.     

Ion beam irradiation of ABO4 compounds with the fergusonite,monazite, scheelite,and zircon structures

The effects of irradiation on CaWO₄, SrWO₄, BaWO₄, YVO₄, LaVO₄, YNbO₄, and LaNbO₄ were investigated on thin crystals using 1.0 MeV Kr ions at 50-1000 K. All of the ABO₄ compounds can be amorphized with calculated damage cross sections (σ_a = 1/F_c0) in the range of ~0.30-1.09 × 10^-14 cm² ion⁻¹ at zero Kelvin. The analysis of fluence-temperature data returned critical temperatures for amorphization (T_c) of 311 ± 1, 358 ± 90, 325 ± 19, 415 ± 17, 541 ± 6, 636 ± 26, and 1012 ± 1 K, respectively, for the compounds listed above. Compared with previous in situ irradiation of ABO₄ orthophosphate samples using 0.8 MeV Kr ions, the T_c values of LaVO₄ and YVO₄ are higher than those of LaPO₄ and YPO₄ by 82 K and 124 K, respectively. The T_c values of the three scheelite structures, CaWO₄, SrWO₄, and BaWO₄, indicate that they are the most radiation tolerant compounds under these conditions. The A-B cation anti-site energies, E_fAB, determined by DFT range from 2.48 to 10.58 eV and are highly correlated with the A-B cation ionic radius ratio, r_A/r_B, but are not correlated with T_c across the different structure types, suggesting that the formation and migration energies of Frenkel defects play a more important role in damage recovery in these compounds. We also discuss the role of cation and anion charge/iconicity as determined by DFT. ABO₄ compounds with the zircon structure and B = P or V have a distinct advantage over those with B = Si as the damaged regions do not appear to be significantly affected by polymerization of (PO₄)³⁻ or (VO₄)³⁻ groups which might stabilize the amorphous fraction and ultimately lead to phase separation as observed in zircon (ZrSiO₄).     

Hybrid modeling of ethylene to ethylene oxide heterogeneous reactor

In this research a dynamic grey box model (GBM) of ethylene oxide (EO) fixed bed reactor has been presented. In the first step of the study, kinetic model of the existing reactions was obtained using artificial neural network (ANN) approach. In order to build the ANN model industrial data of a typical EO reactor were employed. Time, C₂H₄, C₂H₄O, CO₂, H₂O and O₂ mole fractions were network inputs and the multiplication of reaction rate and catalyst deactivation (r * a)was ANN output. From 164 data, 109 data were employed to train ANN. After employing different training algorithms, it was found that, the radial basis function network (RBFN) training algorithm provides the best estimations of the data. This best obtained network was tested against fifty five unseen data. The network estimations were close to unseen data which confirmed generalization capability of the obtained network.In the next step of study, (r * a) was estimated with ANN and then the hybrid model of the reactor was solved. Simulation results were compared with EO mechanistic model and also with plant industrial data. It was found that GBM is 8.437 times more accurate than the mechanistic model.     

Concentration dependence of static and hydrodynamic screening lengths for three different polymers in a variety of solvents

We have used small angle neutron scattering and dynamic light scattering to measure the static and hydrodynamic screening lengths of polystyrene, polymethylmethacrylate and polydimethylsiloxane solutions ranging from marginal to good solvent quality. A universal plot is found for the scaled static screening length when the concentration is scaled using the second virial coefficient in the way suggested by renormalization group theories. The same concentration units do not produce a universal plot for the hydrodynamic screening length at the molecular weights that we have studied (all around 1-2×10⁵ g/mol). However, when the concentration is expressed in terms of k_Dc, where k_D is the virial expansion coefficient for the cooperative diffusion coefficient and c is the concentration, most of the variation between different polymer-solvent combinations is eliminated. The ratio of hydrodynamic screening length to static screening length increases with concentration for all of the polymer solvent pairs studied, and its value differs for different polymer solvent pairs.     

Correlation of Zeno (Z = 1) Line for Supercritical Fluids with Vapor-Liquid Rectilinear Diameters

For a wide range of substances, extending well beyond the regime of corresponding states behavior, the contour in the temperature-density plane along which the compressibility factor Z = P/ρkT is the same as for an ideal gas is nearly linear. This Z = 1 contour, termed the Zeno line, begins deep in the liquid region and ascends as the density decreases to the Boyle point of the supercritical fluid, specified by the temperature T_B for which (dZ/dρ)_T = 0 as ρ → 0; equivalently, at T_B the second virial coefficient vanishes. The slope of the Z = 1 line is — B₃/(dB₂/dT), in terms of the third virial coefficient and the derivative of the second, evaluated at T_B. Previous work has examined the Zeno line as a means to extend corresponding states and to enhance other practical approximations. Here we call attention to another striking aspect, a strong correlation with the line of rectilinear diameters defined by the average of the subcritical vapor and liquid densities. This correlation is obeyed well by empirical data for many substances and computer simulations for a Lennard-Jones potential; the ratios of the intercepts and slopes for the Zeno and rectilinear diameter lines are remarkably close to those predicted by the van der Waals equation, 8/9 and 16/9, respectively. Properties of the slightly imperfect fluid far above the critical point thus implicitly determine the diameter of the vapor-liquid coexistence curve below the critical point.     

Study of the actions of BTEX compounds on polypyrrole film as a gas sensor

Polypyrrole (PPy) thin films were prepared electrochemically at a constant potential. Gas-sensing behaviors, including reproducibility, sensitivity, and response time to various benzene, toluene, ethylbenzene, and xylene (BTEX) compound concentrations, were investigated. BTEX compounds were found to be able to compensate for the doping level of PPy and, hence, decrease the conductivity of PPy on exposure to them. A reasonable reproducibility of the resistance change (ΔR) was obtained. The sensitivity for each compound was 2.3 mω/ppm (benzene), 0.4 mω/ppm (toluene), 8.3 mω/ppm (ethylbenzene), and 2.9 mω/ppm (xylene). An adsorption model correlated well with the experimental results and was used to interpret the sensing behaviors. The parameters of this model, including the adsorption equilibrium constant and the ΔR caused by a pseudomonolayer of the detecting layer {[m(r₁ − r₀)]/n, where m is the number of active sites on the pseudomonolayer; r₁ and r₀ are the site resistances when the site is vacant and occupied, respectively; and n is the thickness of the film}, were determined. According to the parameters, toluene vapor had the most prominent effect in undoping PPy film but the poorest affinity to the active sites of the film. On the other hand, ethylbenzene showed the highest affinity to PPy film compared to the other BTEX compounds and consequently led to the highest sensitivity for such a sensor. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 82: 954–961, 2001     

Zur unverträglichkeit von polymergemischen. 1. Lichtstreuungsmessungen am system polystyrol/polymethylmethacrylat/benzol

A polystyrene (PS) with M?_w = 9,7 · 10⁴ was investigated by means of light scattering in the isorefractive polymer/solvens-mixture polymethylmethacrylate (PMMA)/benzene. It was found, that the second osmotic virial coefficient A₂ of PS was strongly dependent on the average viscosimetric molecular weight M?_v and on the concentration of PMMA, but scarcely on the temperature in the range of 20°C to 60°C. The θ-Point, where A₂ is zero, was independent of the temperature within experimental error. By defining the PMMA concentration at the θ-Point as c_θ, and by reducing the measured PMMA concentration c to c/c_θ, an unequivocal relation was obtained between A₂ and c/c_θ, which is independent of molecular weight and molecular weight distribution of PMMA. PS shows a high second virial coefficient in dimer and trimer MMA as well as in non-hydrogenated and hydrogenated MMA. The investigated PS constitutes a θ-System in PMMA of a degree of polymerisation of P?_w, ～ 17without the use of benzene.     

Estimating hydrogen sulfide solubility in ionic liquids using a machine learning approach

For the design and development of new processes of gas sweetening using ionic liquids (ILs), as promising candidates for amine solutions, an amazing model to predict the solubility of acid gases is of great importance. In this direction, in the current study, the capability of artificial neural networks (ANNs) trained with back propagation (BP) and particle swarm optimization (PSO), to correlate the solubility of H₂S in 11different ILs have been investigated. Different structures of three-layer feed forward neural network using acentric factor (ω), critical temperature (T_c), critical pressure (P_c) of ILs accompanied by pressure (P) and temperature (T), as input parameters, were examined and an optimized architecture has been proposed as 5–9–1.Implementation of these models for 465 experimental data points collected from the literature shows coefficient of determination (R²) of 0.99218 and mean squared error (MSE) of 0.00025 from experimental values for PSO-ANN predicted solubilities while the values of R² = 0.95151 and MSE = 0.00335 were obtained for BP-ANN model. Therefore, through PSO training algorithm we are able to attain significantly better results than with BP training procedure based on the statistical criteria.     

The viscosity of concentrated polymer solutions containing low molecular weight solvents

The zero shear rate viscosities of polystyrene/ethylbenzene solutions having polymer weight fractions ranging from 0.5 to 1.0 have been measured using a novel sealed rheometer cell over a temperature range of 50 to 200°C. The concentration and temperature dependence of the solution viscosity has been found to be well described by the relation η₀ = K c^aM_w ^3.4ζ(c, T) where the monomeric friction coefficient ζ is determined by the free volume of the solution. Following the procedure of Berry, the free volume parameters, α_f(c)/γ and T_∞ (c), and the fractional free volume, f(c,T)/γ, have been determined. After using these parameters to account for the concentration dependence of the friction coefficient, the concentration exponent a has been evaluated and found to be in reasonable agreement with the value of 3.4 obtained by Berry and Fox for other polymer/solvent systems. A comparison of the relative conributions made by the friction coefficient and the term c^3.4 to the overall concentration dependence of the viscosity of these highly concentrated solutions shows the friction coefficient to be the dominant factor