Lattice constant
is one of the paramount parameters that mark the
quality of thin film fabrication. Numerous research efforts have been
made to calculate and measure lattice constant, including experimental
and empirical approaches. Not withstanding these efforts, a reliable
and simple-to-use model is still needed to predict accurately this
vital parameter. In this study, gene expression programming (GEP)
approach was implemented to establish trustworthy model for prediction
of the lattice constant of A2XY6 (A = K, Cs,
Rb, TI; X = tetravalent cation; and Y = F, Cl, Br, I) cubic crystals
based on a comprehensive experimental database. The obtained results
showed that the proposed GEP correlation provides excellent prediction
performance with an overall average absolute relative deviation (AARD%)
of 0.3596% and a coefficient of determination (R
2) of 0.9965. Moreover, the comparison of the performance between
the newly proposed correlation and the best pre-existing paradigms
demonstrated that the established GEP correlation is more robust,
reliable, and efficient than the prior models for prediction of lattice
constant of A2XY6 cubic crystals.
The rheological properties of drilling fluids have been always considered as critical aspects to the success of drilling operations; in this fact, the control of these properties was of paramount importance. In this paper, the rheological behaviour of a water-soluble polymer, namely hydroxyethyl cellulose (HEC) used in drilling fluids, was investigated. Steady-state shear rheology and frequency experiment were carried out on the sample at different concentrations ranging from 0.1 to 1.2 wt% and constant temperature of 25 ± 0.1°C using a controlled-rate rheometer. The HEC solutions showed a shear thinning behaviour that was successfully fitted using the Cross model, where its parameters were found to increase with the HEC concentration. The steady-state shear rheological data allowed determining the intrinsic viscosity of the polymer (* 15.05 dl/g) in distilled water; thereafter, an estimation of the molecular weight of HEC using the Mark-Houwink equation indicated that the studied sample was of high molecular weight (9.5 9 10 5 g/mol), which gave it good rheological properties for drilling fluid applications. The critical overlap concentration was found to be * 0.24 wt% using two methods, the intrinsic viscosity and the plot of the apparent viscosity versus the polymer concentration at specific shear rate. The frequency sweep measurements allowed confirming the viscoelastic behaviour of the polymer; HEC solutions behaved either as viscous or as elastic materials at low and high frequencies, respectively. This information is essential for mud engineers to develop and maintain the properties of drilling fluid to the required specifications.
Estimating the solubility of carbon dioxide in ionic liquids, using reliable models, is of paramount importance from both environmental and economic points of view. In this regard, the current research aims at evaluating the performance of two data-driven techniques, namely multilayer perceptron (MLP) and gene expression programming (GEP), for predicting the solubility of carbon dioxide (CO 2 ) in ionic liquids (ILs) as the function of pressure, temperature, and four thermodynamical parameters of the ionic liquid. To develop the above techniques, 744 experimental data points derived from the literature including 13 ILs were used (80% of the points for training and 20% for validation). Two backpropagation-based methods, namely Levenberg-Marquardt (LM) and Bayesian Regularization (BR), were applied to optimize the MLP algorithm. Various statistical and graphical assessments were applied to check the credibility of the developed techniques. The results were then compared with those calculated using Peng-Robinson (PR) or Soave-Redlich-Kwong (SRK) equations of state (EoS). The highest coefficient of determination (R 2 = 0.9965) and the lowest root mean square error (RMSE = 0.0116) were recorded for the MLP-LMA model on the full dataset (with a negligible difference to the MLP-BR model). The comparison of results from this model with Appl. Sci. 2020, 10, 304 2 of 18 the vastly applied thermodynamic equation of state models revealed slightly better performance, but the EoS approaches also performed well with R 2 from 0.984 up to 0.996. Lastly, the newly established correlation based on the GEP model exhibited very satisfactory results with overall values of R 2 = 0.9896 and RMSE = 0.0201.
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