Physical properties of alkanes and their mixtures

Alkanes and their mixtures are some of the physically simplest molecules and are widely used in industry, yet the connection between their structure and physical properties is still poorly understood. To make progress, we study the properties of pure alkanes with neural networks and molecular dynamics, while we develop a new theoretical framework to study the properties of mixtures of alkanes. We first encode alkanes’ structure into five non-negative integers and use them as neural network inputs. Then, we utilize the neural networks to study the boiling point, vapor pressure, heat capacity, and melting point of light alkanes, as well as flash point and kinematic viscosity of linear alkanes. Neural networks model all these properties more accurately than the competing statistical and physico-chemical methods, while the cross-validation results indicate that they can confidently and accurately extrapolate the boiling point, heat capacity, and vapor pressure models to heavy alkanes. Still, due to a lack of experimental data for non-linear alkanes, neural network flash point and kinematic viscosity models cannot extrapolate to heavy alkanes, while the comparatively low accuracy of melting point models relative to other properties’ models suggests that additional physical effects need to be incorporated into them. To obtain synthetic data as a supplement for the experimental kinematic viscosity dataset, we perform molecular dynamics simulations for density and non-equilibrium molecular dynamics (NEMD) simulations for dynamic viscosity. Density simulation results are corrected through a data-driven approach to increase their accuracy, and we develop a sampling algorithm that automatically selects the shear rates at which to perform the viscosity simulations.The sampling algorithm is tested on linear alkanes, and simulation results are in excellent agreement with the experiments, encouraging applications to more complex alkanes. Then, we use neural networks with molecular structure as inputs to model the molecular dynamics density simulation values and extrapolate to 11-heptyltricosane, 8,11-dipentyloctadecane, and 8,14-dipentylhenicosane at 40°C and 100°C. These extrapolated density values are used as state points for the NEMD viscosity simulations, which are performed with the help of the shear rate sampling algorithm. While the accuracy of neural network models is high, and the usefulness and reliability of the sampling algorithm is further established, viscosity simulation results are not in a good agreement with the experiment due to systematic error in the force field. Finally, to model properties of mixtures of alkanes, we develop a theory of mixtures whose molecules’ positions have a uniform spatial distribution. We apply this theory to molar volume, isentropic compressibility, surface tension, and dynamic viscosity of mixtures of alkanes, first by fitting to experimental data, and then by using the best fit parameters for viscosity to predict viscosity of further mixtures. Best fits and predictions show excellent agreement with the experiments, and our theory shows promise for further applications to mixtures of alkanes, while its conceptual basis has the potential to be applied to other types of mixtures as well.BP-ICAM 5

Predicting physical properties of alkanes with neural networks

We train artificial neural networks to predict the physical properties of linear, single branched, and double branched alkanes. These neural networks can be trained from fragmented data, which enables us to use physical property information as inputs and exploit property-property correlations to improve the quality of our predictions. We characterize every alkane uniquely using a set of five chemical descriptors. We establish correlations between branching and the boiling point, heat capacity, and vapor pressure as a function of temperature. We establish how the symmetry affects the melting point and identify erroneous data entries in the flash point of linear alkanes. Finally, we exploit the temperature and pressure dependence of shear viscosity and density in order to model the kinematic viscosity of linear alkanes. The accuracy of the neural network models compares favorably to the accuracy of several physico-chemical/thermodynamic methods

Analisis Perbandingan Kinerja Keuangan Bank Umum Syariah BUMN dan Non BUMN dengan Metode RGEC

This study aims to analyze the comparison of the financial performance of BUMN Islamic commercial banks and BUMN Non Islamic commercial banks on the variables of Risk Profile, Good Corporate Governance, Earnings, and Capital (RGEC) for the 2017-2020 period represented by the ratio of Non Performing Financing (NPF), Financing to Deposit Ratio (FDR), Operating Expenses to Operating Income (BOPO), Good Corporate Governance (GCG), Return On Assets (ROA), Return On Equity (ROE), and Capital Adequacy Ratio (CAR). The results of this study show that of the 7 ratios analyzed, there are 5 ratios with significant differences between the financial performance of BUMN Islamic commercial banks and Non BUMN Islamic commercial banks during the 2017-2020 period, namely FDR with a significance of 0.000, BOPO with a significance of 0.011 Good Corporate Governance with a significance of 0.011. 0.003 significance, ROA with 0.024 significance, and ROE with 0.020 significance. While the NPF ratio with a significance of 0.921, CAR with a significance of 0.744, and RGEC Composite Rating with a significance of 0.510 there is no significant difference between the financial performance of BUMN Islamic commercial banks and non-BUMN Islamic commercial banks during the 2017-2020 period. The last analysis on the consolidated financial performance assessment of all RGEC variables shows that the performance of BUMN Islamic commercial banks is not better than the performance of Non-BUMN Islamic commercial banks where the average composite value for 4 years is the performance of BUMN Islamic commercial banks (80%)

PELATIHAN BUDIDAYA IKAN HIAS DAN CARA PEMASARANNYA DI MEDIA SOSIAL UNTUK MENAMBAH PENDAPATAN

The Covid-19 era had a big impact, especially for the lower middle class, because the economy was declining and there were also many reductions in the number of employees in various companies. So that there is a need for innovation to open your own business, one of the easy and inexpensive businesses is ornamental fish cultivation. The research method used in the study was training for ornamental fish cultivation with the result of increasing the knowledge and understanding of residents about the cultivation and marketing of betta type ornamental fish as a business opportunity to increase income during the Covid-19 pandemic

FTIR Analysis of Bi2O3−B2O3−Fe2O3Bi_2O_3-B_2O_3-Fe_2O_3 Glass System Doped with Nd2O3Nd_2O_3

In the present work $Bi_2O_3-B_2O_3-Fe_2O_3$ glasses have been prepared via melt quenching method. The composition has been fixed to 60 mol% B2O3, 20 mol% $Bi_2O_3$ and 20 mol % $Fe_2O_3$ which is a stable glass forming composition. In these glasses, $Nd_2O_3$ has been doped in exchange of $Fe_2O_3$ concentration. FTIR studies have been performed in these glasses to examine the distribution of different borate and bismuth structural groups. The effect of iron and neodymium on these distributions has been examined

Monte Carlo Studies of Underconstrained Magnetism in Ultracold Fermionic Alkaline Earth Atomic Gases

Physicists have been trying to create artificial magnetic systems using ultra-cold atomic gases as their simulators. However, behavior of many ultra-cold atomic systems is not very well understood yet. Ultra-cold fermionic alkaline earth atomic (AEA) gases are one of those systems. In this thesis, we study the effects of thermal fluctuations on the overall macroscopic behavior of ultra-cold fermionic AEA gases using a particular semiclassical model. We study the AEA systems on a square lattice with periodic boundary conditions. To investigate the behavior of AEA systems under the effects of thermal fluctuations, we analyze several different types of correlation functions, which correspond to different physical observables. We analyze the correlation functions using the results obtained from Monte Carlo simulations. We show that for the cases studied, all the correlation functions approach zero as the system size goes to infinity at any temperature, indicating that no phase transition occurs as a consequence of thermal fluctuations

Dataset for NEMD with automatic shear rate sampling to model viscosity and correction of systematic errors in modeling density: Application to linear and light branched alkanes

Density and viscosity results that can otherwise be found in the more concise form in the manuscript. The density sheet comprises experimental values, that have been collected from the TRC Thermodynamic tables, API 44, as well as the molecular dynamics predictions obtained through LAMMPS, and their uncertainties. The viscosity column sheet comprises all the results of viscosity molecular dynamics simulations before fitting to the Carreau model. For each viscosity result, state point (density), shear rate, dynamic and kinematic viscosity and uncertainty in them are listed

Predicting physical properties of alkanes with neural networks dataset

Data for physical properties of alkanes used in the manuscriptBP-ICAM 5